@inbook{
 type = {inbook},
 year = {2023},
 pages = {395-411},
 publisher = {Springer International Publishing},
 city = {Cham},
 series = {Studies in Neuroscience, Psychology and Behavioral Economics},
 id = {c5c11669-885b-3da4-a86c-0e85e8fe2086},
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 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-20T07:21:12.321Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garatva2023},
 source_type = {inbook},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 bibtype = {inbook},
 author = {Garatva, Patricia and Terhorst, Yannik and Meßner, Eva-Maria and Karlen, Walter and Pryss, Rüdiger and Baumeister, Harald},
 editor = {Montag, Christian and Baumeister, Harald},
 doi = {10.1007/978-3-030-98546-2_23},
 chapter = {Smart Sensors for Health Research and Improvement},
 title = {Digital Phenotyping and Mobile Sensing}
}

@article{
 title = {Auditory deep sleep stimulation in older adults at home: a randomized crossover trial},
 type = {article},
 year = {2022},
 pages = {30},
 volume = {2},
 websites = {https://www.nature.com/articles/s43856-022-00096-6},
 month = {12},
 publisher = {Springer US},
 day = {4},
 id = {932c108d-46e6-3566-98aa-5cbd9aeef910},
 created = {2022-06-13T22:57:57.941Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:12.637Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lustenberger2022},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 abstract = {Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings. We present a fully remote, randomized, cross-over trial in healthy adults aged 62–78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition. Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof. While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology. Sleep’s restorative function is closely linked to slow waves, which are brain activity patterns that occur during deep sleep and are diminished with age. Those slow waves can be increased through auditory stimulation, a method that administers precisely-timed sounds during sleep. Here, we established whether the application of auditory stimulation can be performed in older adults over several nights remotely in their own homes. In a trial, we used a mobile device to deliver auditory stimulation during sleep and measured its effects on slow waves, mood, and vigilance. Although we showed robust increases in slow waves, we found large effect differences between participants and also between different nights within the same participants. We looked for predictors of these effect differences. Our study showed that in-home auditory stimulation is feasible, and may help to guide future auditory stimulation strategies. Lustenberger et al. perform a randomized crossover trial of auditory deep sleep stimulation in older adults, conducted over multiple weeks within the participants’ homes. The authors report substantial inter- and intra-individual differences in the slow wave response to auditory stimulation and identify predictors of response.},
 bibtype = {article},
 author = {Lustenberger, Caroline and Ferster, M. Laura and Huwiler, Stephanie and Brogli, Luzius and Werth, Esther and Huber, Reto and Karlen, Walter},
 doi = {10.1038/s43856-022-00096-6},
 journal = {Communications Medicine},
 number = {1}
}

Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings. We present a fully remote, randomized, cross-over trial in healthy adults aged 62–78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition. Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof. While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology. Sleep’s restorative function is closely linked to slow waves, which are brain activity patterns that occur during deep sleep and are diminished with age. Those slow waves can be increased through auditory stimulation, a method that administers precisely-timed sounds during sleep. Here, we established whether the application of auditory stimulation can be performed in older adults over several nights remotely in their own homes. In a trial, we used a mobile device to deliver auditory stimulation during sleep and measured its effects on slow waves, mood, and vigilance. Although we showed robust increases in slow waves, we found large effect differences between participants and also between different nights within the same participants. We looked for predictors of these effect differences. Our study showed that in-home auditory stimulation is feasible, and may help to guide future auditory stimulation strategies. Lustenberger et al. perform a randomized crossover trial of auditory deep sleep stimulation in older adults, conducted over multiple weeks within the participants’ homes. The authors report substantial inter- and intra-individual differences in the slow wave response to auditory stimulation and identify predictors of response.

@article{
 title = {Detect-and-segment: A deep learning approach to automate wound image segmentation},
 type = {article},
 year = {2022},
 keywords = {Chronic wounds,Generalizability,Machine learning,Semantic segmentation,Smartphone},
 pages = {100884},
 volume = {29},
 id = {12dafe98-05a4-318d-b4ec-735d0b9f69d4},
 created = {2022-09-04T18:02:17.480Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:04.568Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {SCEBBA2022100884},
 source_type = {article},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 abstract = {Chronic wounds significantly impact quality of life. They can rapidly deteriorate and require close monitoring of healing progress. Image-based wound analysis is a way of objectively assessing the wound status by quantifying important features that are related to healing. However, high heterogeneity of the wound types and imaging conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the perturbing background, and computed a wound segmentation map. We tested this approach on a diabetic foot ulcers data set and compared it to a segmentation method based on the full image. To evaluate its generalizability on out-of-distribution data, we measured the performance of the DS approach on 4 additional independent data sets, with larger variety of wound types from different body locations. The Matthews’ correlation coefficient (MCC) improved from 0.29 (full image) to 0.85 (DS) on the diabetic foot ulcer data set. When the DS was tested on the independent data sets, the mean MCC increased from 0.17 to 0.85 . Furthermore, the DS enabled the training of segmentation models with up to 90% less training data without impacting the segmentation performance. The proposed DS approach is a step towards automating wound analysis and reducing efforts to manage chronic wounds.},
 bibtype = {article},
 author = {Scebba, Gaetano and Zhang, Jia and Catanzaro, Sabrina and Mihai, Carina and Distler, Oliver and Berli, Martin and Karlen, Walter},
 doi = {https://doi.org/10.1016/j.imu.2022.100884},
 journal = {Informatics in Medicine Unlocked}
}

Chronic wounds significantly impact quality of life. They can rapidly deteriorate and require close monitoring of healing progress. Image-based wound analysis is a way of objectively assessing the wound status by quantifying important features that are related to healing. However, high heterogeneity of the wound types and imaging conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the perturbing background, and computed a wound segmentation map. We tested this approach on a diabetic foot ulcers data set and compared it to a segmentation method based on the full image. To evaluate its generalizability on out-of-distribution data, we measured the performance of the DS approach on 4 additional independent data sets, with larger variety of wound types from different body locations. The Matthews’ correlation coefficient (MCC) improved from 0.29 (full image) to 0.85 (DS) on the diabetic foot ulcer data set. When the DS was tested on the independent data sets, the mean MCC increased from 0.17 to 0.85 . Furthermore, the DS enabled the training of segmentation models with up to 90% less training data without impacting the segmentation performance. The proposed DS approach is a step towards automating wound analysis and reducing efforts to manage chronic wounds.

@article{
 title = {Assessment of neonatal respiratory rate variability},
 type = {article},
 year = {2022},
 pages = {ahead of print},
 publisher = {Springer},
 id = {4f489fe1-5a9b-335b-8442-874cd98559ec},
 created = {2022-09-04T18:02:17.485Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:05.006Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {coleman2022assessment},
 source_type = {article},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 abstract = {Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.},
 bibtype = {article},
 author = {Coleman, Jesse and Ginsburg, Amy Sarah and Macharia, William M and Ochieng, Roseline and Chomba, Dorothy and Zhou, Guohai and Dunsmuir, Dustin and Karlen, Walter and Ansermino, J Mark},
 doi = {10.1007/s10877-022-00840-2},
 journal = {Journal of Clinical Monitoring and Computing}
}

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.

@article{
 title = {Feedback on Trunk Movements From an Electronic Game to Improve Postural Balance in People With Nonspecific Low Back Pain: Pilot Randomized Controlled Trial},
 type = {article},
 year = {2022},
 pages = {e31685},
 volume = {10},
 id = {9c0ab92a-01f8-3bc3-843d-bd9898b42510},
 created = {2022-09-04T18:02:17.553Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:05.405Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Meinke2022},
 source_type = {article},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 bibtype = {article},
 author = {Meinke, Anita and Peters, Rick and Knols, Ruud and Swanenburg, Jaap and Karlen, Walter},
 doi = {10.2196/31685},
 journal = {JMIR Serious Games},
 number = {2}
}

@article{
 title = {Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep},
 type = {article},
 year = {2022},
 pages = {2916-2925},
 volume = {69},
 id = {41ddaefc-33ee-30ee-9a51-3f35db7789bb},
 created = {2022-09-04T18:02:17.642Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:06.768Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {9730073},
 source_type = {article},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 bibtype = {article},
 author = {Ferster, Maria Laura and Da Poian, Giulia and Menachery, Kiran and Schreiner, Simon and Lustenberger, Caroline and Maric, Angelina and Huber, Reto and Baumann, Christian and Karlen, Walter},
 doi = {10.1109/TBME.2022.3157468},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {9}
}

@article{
 title = {Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea},
 type = {article},
 year = {2021},
 pages = {350-9},
 volume = {68},
 websites = {https://ieeexplore.ieee.org/document/9091089/},
 id = {c07146f5-08bc-3d24-ad31-4c0bd3577f7c},
 created = {2020-05-15T21:25:26.537Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:59.886Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Scebba2020},
 notes = {IF-2018: 4.49},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,8bf4ed74-a467-49ad-8f7c-6ab98c981268},
 private_publication = {false},
 abstract = {Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.},
 bibtype = {article},
 author = {Scebba, Gaetano and Da Poian, Giulia and Karlen, Walter},
 doi = {10.1109/TBME.2020.2993649},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {1}
}

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.

@article{
 title = {Injury risks among elite competitive alpine skiers are underestimated if not registered prospectively, over the entire season and regardless of whether requiring medical attention},
 type = {article},
 year = {2021},
 keywords = {Epidemiology,Gender-specific injurie,Periodization,alpine ski racing,athletes,epidemiology,gender-specific injuries,injury prevention,periodization},
 pages = {1635-1643},
 volume = {29},
 websites = {https://doi.org/10.1007/s00167-020-06110-5,http://link.springer.com/10.1007/s00167-020-06110-5,https://link.springer.com/10.1007/s00167-020-06110-5},
 month = {5},
 publisher = {Springer Berlin Heidelberg},
 day = {16},
 id = {66a07433-4e43-312f-998a-324d7772aec9},
 created = {2020-06-26T09:23:54.810Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:25.421Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Frohlich2020},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Fröhlich, Stefan and Helbling, Moritz and Fucentese, Sandro F and Karlen, Walter and Frey, Walter O and Spörri, Jörg},
 doi = {10.1007/s00167-020-06110-5},
 journal = {Knee Surgery, Sports Traumatology, Arthroscopy},
 number = {5}
}

@article{
 title = {Ethics review of big data research: What should stay and what should be reformed?},
 type = {article},
 year = {2021},
 pages = {51},
 volume = {22},
 websites = {https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-021-00616-4},
 month = {12},
 day = {30},
 id = {708c409c-2ada-3075-bc8e-8d80f52dde55},
 created = {2021-05-28T08:58:43.423Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:26.745Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ferretti2021},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 bibtype = {article},
 author = {Ferretti, Agata and Ienca, Marcello and Sheehan, Mark and Blasimme, Alessandro and Dove, Edward S. and Farsides, Bobbie and Friesen, Phoebe and Kahn, Jeff and Karlen, Walter and Kleist, Peter and Liao, S. Matthew and Nebeker, Camille and Samuel, Gabrielle and Shabani, Mahsa and Rivas Velarde, Minerva and Vayena, Effy},
 doi = {10.1186/s12910-021-00616-4},
 journal = {BMC Medical Ethics},
 number = {1}
}

@article{
 title = {A response to Basner et al. (2021): “Response speed measurements on the psychomotor vigilance task: how precise is precise enough?”},
 type = {article},
 year = {2021},
 volume = {44},
 websites = {https://academic.oup.com/sleep/advance-article/doi/10.1093/sleep/zsab085/6247628,https://academic.oup.com/sleep/article/doi/10.1093/sleep/zsab085/6247628},
 month = {7},
 day = {9},
 id = {c4622fa0-3be5-33b7-a350-04d75fe34539},
 created = {2021-05-28T08:58:43.544Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:09.123Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Snipes2021},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 bibtype = {article},
 author = {Snipes, Sophia and Huber, Reto and Karlen, Walter},
 doi = {10.1093/sleep/zsab085},
 journal = {Sleep},
 number = {7}
}

@article{
 title = {Exergaming Using Postural Feedback From Wearable Sensors and Exercise Therapy to Improve Postural Balance in People With Nonspecific Low Back Pain: Protocol for a Factorial Pilot Randomized Controlled Trial},
 type = {article},
 year = {2021},
 pages = {e26982},
 volume = {10},
 websites = {https://www.researchprotocols.org/2021/8/e26982},
 month = {8},
 day = {26},
 id = {78891894-51a5-3788-a4ef-c6172dadbaa4},
 created = {2021-06-18T14:49:07.326Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:00.561Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Meinke2021},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 bibtype = {article},
 author = {Meinke, Anita and Peters, Rick and Knols, Ruud and Karlen, Walter and Swanenburg, Jaap},
 doi = {10.2196/26982},
 journal = {JMIR Research Protocols},
 number = {8}
}

@article{
 title = {Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep},
 type = {article},
 year = {2021},
 websites = {https://academic.oup.com/sleep/advance-article/doi/10.1093/sleep/zsab204/6347149},
 month = {8},
 day = {10},
 id = {6c4a404a-a775-3e3a-822d-8cef5d2bb0f0},
 created = {2021-08-30T06:16:39.835Z},
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 last_modified = {2022-09-04T18:12:14.939Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Sousouri2021},
 notes = {IF-2018: 4.57},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 abstract = {The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography (hd-EEG) in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out to different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.},
 bibtype = {article},
 author = {Sousouri, Georgia and Krugliakova, Elena and Skorucak, Jelena and Leach, Sven and Snipes, Sophia and Ferster, Maria Laura and Da Poian, Giulia and Karlen, Walter and Huber, Reto},
 doi = {10.1093/sleep/zsab204},
 journal = {Sleep}
}

The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography (hd-EEG) in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out to different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.

@article{
 title = {Wound Image Quality From a Mobile Health Tool for Home-Based Chronic Wound Management With Real-Time Quality Feedback: Randomized Feasibility Study},
 type = {article},
 year = {2021},
 keywords = {data quality,digital health,digital ulcers,ehealth,mhealth,mobile app,remote assessment,scleroderma,telemedicine},
 pages = {e26149},
 volume = {9},
 websites = {https://mhealth.jmir.org/2021/7/e26149,https://www.research-collection.ethz.ch/handle/20.500.11850/500386},
 month = {7},
 day = {30},
 id = {14dc24a4-414d-384e-8d40-3a36de639c1b},
 created = {2021-08-30T06:16:39.837Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:26.266Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhang2021a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Zhang, Jia and Mihai, Carina and Tüshaus, Laura and Scebba, Gaetano and Distler, Oliver and Karlen, Walter},
 doi = {10.2196/26149},
 journal = {JMIR mHealth and uHealth},
 number = {7}
}

@article{
 title = {Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates},
 type = {article},
 year = {2021},
 pages = {93},
 volume = {5},
 websites = {https://gatesopenresearch.org/articles/5-93/v1},
 month = {6},
 day = {10},
 id = {7ea7546c-686d-35c0-96ee-1d925962f557},
 created = {2021-08-30T13:03:06.725Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:24.139Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Coleman2021},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies.},
 bibtype = {article},
 author = {Coleman, Jesse and Ginsburg, Amy Sarah and Macharia, William M. and Ochieng, Roseline and Zhou, Guohai and Dunsmuir, Dustin and Karlen, Walter and Ansermino, J. Mark},
 doi = {10.12688/gatesopenres.13237.1},
 journal = {Gates Open Research}
}

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies.

@article{
 title = {Diurnal variations in multi-sensor wearable-derived sleep characteristics in morning- and evening-type shift workers under naturalistic conditions},
 type = {article},
 year = {2021},
 keywords = {Shift work,chronotype,circadian misalignment,first responders,wearables},
 pages = {accepted},
 websites = {https://doi.org/10.1080/07420528.2021.1941074,https://www.tandfonline.com/doi/full/10.1080/07420528.2021.1941074},
 month = {7},
 publisher = {Taylor & Francis},
 day = {18},
 id = {3e02a9fa-73e9-367b-be9f-417a0032626e},
 created = {2021-08-30T13:14:12.936Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:26.058Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Clark2021},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {Consumer-grade, multi-sensor, rest-activity trackers may be powerful tools, to help optimize rest-activity management in shiftwork populations undergoing circadian misalignment. Nevertheless, performance testing of such devices under field conditions is scarce. We previously validated Fitbit Charge 2TM against home polysomnography and now evaluated the potential of this device to document differences in rest-activity behavior, including sleep macrostructure, in first-responder shift workers in an operational setting. We continuously monitored 89 individuals (54% females; mean age: 33.9 ± 7.7 years) for 32.5 ± 9.3 days and collected 2,974 individual sleep episodes scattered around the clock. We stratified the study participants according to their self-reported circadian preference on the reduced Horne-Östberg Morningness-Evening Questionnaire (rMEQ; the scores from 4 participants were missing). Fitbit estimates of sleep duration, wakefulness after sleep onset (WASO), REM sleep percentage in the first NREM-REM sleep cycle, and REM sleep latency formed approximately sinusoidal oscillations across 24 hours. Generalized additive mixed model analyses revealed that the phase position of sleep duration minimum was delayed by 2.8 h in evening types (ET; rMEQ ≤ 11; n = 20) and by 2.6 h in intermediate types (IT; 11 < rMEQ < 18; n = 45) when compared to morning types (MT; rMEQ ≥ 18; n = 20). Similarly, the phase position of WASO was delayed by 2.7 h in ET compared to MT. While nocturnal sleep duration did not differ among the three groups, sleep episodes during the biological day decreased in duration from ET to IT to MT. Together, the findings support the notion that a consumer-grade, rest-activity tracker allows estimation of behavioral sleep/wake cycles and sleep macrostructure in shift workers under naturalistic conditions that are consistent with their self-reported chronotype.},
 bibtype = {article},
 author = {Clark, Ian and Stucky, Benjamin and Azza, Yasmine and Schwab, Patrick and Müller, Stefan and Weibel, Daniel and Button, Daniel and Karlen, Walter and Seifritz, Erich and Kleim, Birgit and Landolt, Hans-Peter},
 doi = {10.1080/07420528.2021.1941074},
 journal = {Chronobiology International}
}

Consumer-grade, multi-sensor, rest-activity trackers may be powerful tools, to help optimize rest-activity management in shiftwork populations undergoing circadian misalignment. Nevertheless, performance testing of such devices under field conditions is scarce. We previously validated Fitbit Charge 2TM against home polysomnography and now evaluated the potential of this device to document differences in rest-activity behavior, including sleep macrostructure, in first-responder shift workers in an operational setting. We continuously monitored 89 individuals (54% females; mean age: 33.9 ± 7.7 years) for 32.5 ± 9.3 days and collected 2,974 individual sleep episodes scattered around the clock. We stratified the study participants according to their self-reported circadian preference on the reduced Horne-Östberg Morningness-Evening Questionnaire (rMEQ; the scores from 4 participants were missing). Fitbit estimates of sleep duration, wakefulness after sleep onset (WASO), REM sleep percentage in the first NREM-REM sleep cycle, and REM sleep latency formed approximately sinusoidal oscillations across 24 hours. Generalized additive mixed model analyses revealed that the phase position of sleep duration minimum was delayed by 2.8 h in evening types (ET; rMEQ ≤ 11; n = 20) and by 2.6 h in intermediate types (IT; 11 < rMEQ < 18; n = 45) when compared to morning types (MT; rMEQ ≥ 18; n = 20). Similarly, the phase position of WASO was delayed by 2.7 h in ET compared to MT. While nocturnal sleep duration did not differ among the three groups, sleep episodes during the biological day decreased in duration from ET to IT to MT. Together, the findings support the notion that a consumer-grade, rest-activity tracker allows estimation of behavioral sleep/wake cycles and sleep macrostructure in shift workers under naturalistic conditions that are consistent with their self-reported chronotype.

@article{
 title = {A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data},
 type = {article},
 year = {2021},
 pages = {1284-1291},
 volume = {25},
 month = {4},
 id = {6cf647f5-8e44-3f34-8d80-1b5a1b53c478},
 created = {2022-09-04T18:02:17.472Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:05.216Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2020a},
 source_type = {article},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 abstract = {Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.},
 bibtype = {article},
 author = {Schwab, Patrick and Karlen, Walter},
 doi = {10.1109/JBHI.2020.3021143},
 journal = {IEEE Journal of Biomedical and Health Informatics},
 number = {4}
}

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.

@article{
 title = {Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection},
 type = {article},
 year = {2020},
 keywords = {Alarm fatigue,Alarm reduction,False alarms,ICU,Motion sensor,Smart alarms},
 pages = {419-426},
 volume = {32},
 websites = {http://link.springer.com/10.1007/s12028-019-00711-w},
 id = {614c223a-f5e6-3825-aaca-5c917e9b4f5e},
 created = {2019-04-10T09:38:15.381Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:56.459Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Muroi2019},
 notes = {IF-2018: 2.89},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {Background: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos. Methods: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices. Results: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. Conclusions: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.},
 bibtype = {article},
 author = {Muroi, Carl and Meier, Sando and De Luca, Valeria and Mack, David J. and Strässle, Christian and Schwab, Patrick and Karlen, Walter and Keller, Emanuela},
 doi = {10.1007/s12028-019-00711-w},
 journal = {Neurocritical Care},
 number = {2}
}

Background: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos. Methods: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices. Results: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. Conclusions: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

@article{
 title = {Forecasting intracranial hypertension using multi-scale waveform metrics},
 type = {article},
 year = {2020},
 pages = {014001},
 volume = {41},
 websites = {http://arxiv.org/abs/1902.09499,https://iopscience.iop.org/article/10.1088/1361-6579/ab6360},
 id = {45fa9fa2-d113-3407-8516-5c9664705125},
 created = {2019-09-12T13:35:08.513Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:23.207Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Huser2019},
 notes = {IF-2018: 2.25},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively and time is lost before counteractive measures are taken. A pro-active approach that predicts critical events several hours ahead of time could be beneficial for the patient. Methods: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 36% in the MIMIC-II waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Conclusion: Our approach showed promising performance and enabled us to gain insights about the critical components of the prediction model. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.},
 bibtype = {article},
 author = {Hüser, Matthias and Kündig, Adrian and Karlen, Walter and De Luca, Valeria and Jaggi, Martin},
 doi = {10.1088/1361-6579/ab6360},
 journal = {Physiological Measurement},
 number = {1}
}

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively and time is lost before counteractive measures are taken. A pro-active approach that predicts critical events several hours ahead of time could be beneficial for the patient. Methods: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 36% in the MIMIC-II waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Conclusion: Our approach showed promising performance and enabled us to gain insights about the critical components of the prediction model. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.

@article{
 title = {Pulse transit time based respiratory rate estimation with singular spectrum analysis},
 type = {article},
 year = {2020},
 pages = {257-266},
 volume = {58},
 websites = {http://link.springer.com/10.1007/s11517-019-02088-6},
 id = {10844a3e-e937-3377-952a-cf522aa85286},
 created = {2019-09-26T09:33:35.627Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:55.380Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ding2020},
 notes = {IF-2018: 2.04},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Ding, Xiao-Rong and Yan, Bryan P and Karlen, Walter and Zhang, Yuan-Ting and Tsang, Hon Ki},
 doi = {10.1007/s11517-019-02088-6},
 journal = {Medical & Biological Engineering & Computing},
 number = {2}
}

@article{
 title = {A factorial cluster-randomised controlled trial combining home-environmental and early child development interventions to improve child health and development: rationale, trial design and baseline findings.},
 type = {article},
 year = {2020},
 keywords = {Cluster-randomised trial,Diarrhoea,Early child development,Household air pollution,Household water treatment,Improved biomass cookstoves,Integrated home-based interventions,Kitchen hygiene,Peru,Respiratory infections},
 pages = {73},
 volume = {20},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/32241260,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC7115072},
 publisher = {BMC Medical Research Methodology},
 id = {930d5c4b-75f5-3b47-8c6a-02f435febdd0},
 created = {2020-04-09T05:51:19.508Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:57.003Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {StellaMHartinger2020a},
 notes = {IF-2018: <br/>2.509},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {BACKGROUND Exposure to unhealthy environments and inadequate child stimulation are main risk factors that affect children's health and wellbeing in low- and middle-income countries. Interventions that simultaneously address several risk factors at the household level have great potential to reduce these negative effects. We present the design and baseline findings of a cluster-randomised controlled trial to evaluate the impact of an integrated home-environmental intervention package and an early child development programme to improve diarrhoea, acute respiratory infections and childhood developmental outcomes in children under 36 months of age living in resource-limited rural Andean Peru. METHODS We collected baseline data on children's developmental performance, health status and demography as well as microbial contamination in drinking water. In a sub-sample of households, we measured indoor kitchen 24-h air concentration levels of carbon monoxide (CO) and fine particulate matter (PM2.5) and CO for personal exposure. RESULTS We recruited and randomised 317 children from 40 community-clusters to four study arms. At baseline, all arms had similar health and demographic characteristics, and the developmental status of children was comparable between arms. The analysis revealed that more than 25% of mothers completed primary education, a large proportion of children were stunted and diarrhoea prevalence was above 18%. Fifty-two percent of drinking water samples tested positive for thermo-tolerant coliforms and the occurrence of E.coli was evenly distributed between arms. The mean levels of kitchen PM2.5 and CO concentrations were 213 μg/m3 and 4.8 ppm, respectively. CONCLUSIONS The trial arms are balanced with respect to most baseline characteristics, such as household air and water pollution, and child development. These results ensure the possible estimation of the trial effectiveness. This trial will yield valuable information for assessing synergic, rational and cost-effective benefits of the combination of home-based interventions. TRIAL REGISTRY ISRCTN-26548981.},
 bibtype = {article},
 author = {Hartinger, Stella Maria and Nuño, Nestor and Hattendorf, Jan and Verastegui, Hector and Karlen, Walter and Ortiz, Mariela and Mäusezahl, Daniel},
 doi = {10.1186/s12874-020-00950-y},
 journal = {BMC medical research methodology},
 number = {1}
}

BACKGROUND Exposure to unhealthy environments and inadequate child stimulation are main risk factors that affect children's health and wellbeing in low- and middle-income countries. Interventions that simultaneously address several risk factors at the household level have great potential to reduce these negative effects. We present the design and baseline findings of a cluster-randomised controlled trial to evaluate the impact of an integrated home-environmental intervention package and an early child development programme to improve diarrhoea, acute respiratory infections and childhood developmental outcomes in children under 36 months of age living in resource-limited rural Andean Peru. METHODS We collected baseline data on children's developmental performance, health status and demography as well as microbial contamination in drinking water. In a sub-sample of households, we measured indoor kitchen 24-h air concentration levels of carbon monoxide (CO) and fine particulate matter (PM2.5) and CO for personal exposure. RESULTS We recruited and randomised 317 children from 40 community-clusters to four study arms. At baseline, all arms had similar health and demographic characteristics, and the developmental status of children was comparable between arms. The analysis revealed that more than 25% of mothers completed primary education, a large proportion of children were stunted and diarrhoea prevalence was above 18%. Fifty-two percent of drinking water samples tested positive for thermo-tolerant coliforms and the occurrence of E.coli was evenly distributed between arms. The mean levels of kitchen PM2.5 and CO concentrations were 213 μg/m3 and 4.8 ppm, respectively. CONCLUSIONS The trial arms are balanced with respect to most baseline characteristics, such as household air and water pollution, and child development. These results ensure the possible estimation of the trial effectiveness. This trial will yield valuable information for assessing synergic, rational and cost-effective benefits of the combination of home-based interventions. TRIAL REGISTRY ISRCTN-26548981.

@article{
 title = {A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep},
 type = {article},
 year = {2020},
 pages = {586},
 volume = {14},
 websites = {https://www.frontiersin.org/article/10.3389/fnins.2020.00586/full},
 month = {6},
 day = {17},
 id = {7044426a-9651-3628-94e2-df1ccf4df05a},
 created = {2020-06-11T05:27:49.763Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:04.352Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Leach2020},
 notes = {IF-2018: 3.65},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,60555479-b7f0-45f5-aa97-a3920f93c426},
 private_publication = {false},
 bibtype = {article},
 author = {Leach, Sven and Chung, Ku-young and Tüshaus, Laura and Huber, Reto and Karlen, Walter},
 doi = {10.3389/fnins.2020.00586},
 journal = {Frontiers in Neuroscience}
}

@inproceedings{
 title = {Wearable bioimpedance for continuous and context-aware clinical monitoring},
 type = {inproceedings},
 year = {2020},
 keywords = {actimetry,bioimpedance,context-awareness,fluid monitor-,ing,internet of medical things,wearable devices},
 pages = {3985-8},
 websites = {https://ieeexplore.ieee.org/document/9175298/,https://www.techrxiv.org/articles/preprint/Wearable_bioimpedance_for_continuous_and_context-aware_clinical_monitoring/12283877/2},
 publisher = {IEEE},
 city = {Montreal, CA},
 id = {d221d4df-3563-3b12-adc3-9a6a50288668},
 created = {2020-06-11T05:37:59.742Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:25.853Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dutt2020},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dutt, Abhilash Guru and Verling, Michaela and Karlen, Walter},
 doi = {10.1109/EMBC44109.2020.9175298},
 booktitle = {42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}
}

@inproceedings{
 title = {Covariance intersection to improve the robustness of the photoplethysmogram derived respiratory rate},
 type = {inproceedings},
 year = {2020},
 pages = {5939-42},
 websites = {https://arxiv.org/abs/2004.09934,https://ieeexplore.ieee.org/document/9175943/},
 publisher = {IEEE},
 city = {Montreal, CA},
 id = {89f65d37-d3ec-3112-a3f5-03c9e045f459},
 created = {2020-06-11T09:30:38.794Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:26.478Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhang2020a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,60555479-b7f0-45f5-aa97-a3920f93c426,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Zhang, Jia and Scebba, Gaetano and Karlen, Walter},
 doi = {10.1109/EMBC44109.2020.9175943},
 booktitle = {42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}
}

@article{
 title = {Remote health diagnosis and monitoring in the time of COVID-19},
 type = {article},
 year = {2020},
 keywords = {review},
 volume = {41},
 websites = {https://iopscience.iop.org/article/10.1088/1361-6579/abba0a},
 id = {45d74e62-8b80-3212-aa08-5dc7023cd9e8},
 created = {2020-09-24T14:42:00.548Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:05.870Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Behar2020},
 notes = {IF-2019: 2.31},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Behar, Joachim A and Liu, Chengyu and Kotzen, Kevin and Tsutsui, Kenta and Corino, Valentina D A and Singh, Janmajay and Pimentel, Marco A F and Warrick, Philip and Zaunseder, Sebastian and Andreotti, Fernando and Sebag, David and Kopanitsa, Georgy and McSharry, Patrick E and Karlen, Walter and Karmakar, Chandan and Clifford, Gari D},
 doi = {10.1088/1361-6579/abba0a},
 journal = {Physiological Measurement},
 number = {10}
}

@article{
 title = {Learning Counterfactual Representations for Estimating Individual Dose-Response Curves},
 type = {article},
 year = {2020},
 pages = {5612-5619},
 volume = {34},
 websites = {http://arxiv.org/abs/1902.00981,https://aaai.org/ojs/index.php/AAAI/article/view/6014},
 month = {4},
 publisher = {AAAI Press},
 day = {3},
 city = {New York},
 id = {76da0f6c-37d3-30a3-a25f-179ca2e70ba3},
 created = {2020-12-08T14:35:27.829Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:03.032Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2020},
 notes = {Acceptance rate: 0.20},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30,8bf4ed74-a467-49ad-8f7c-6ab98c981268},
 private_publication = {false},
 abstract = {Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.},
 bibtype = {article},
 author = {Schwab, Patrick and Linhardt, Lorenz and Bauer, Stefan and Buhmann, Joachim M. and Karlen, Walter},
 doi = {10.1609/aaai.v34i04.6014},
 journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
 number = {04}
}

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.

@article{
 title = {A transformation of oxygen saturation (the saturation virtual shunt) to improve clinical prediction model calibration and interpretation},
 type = {article},
 year = {2019},
 keywords = {gz1,limit is 8000,oxygen saturation,prediction,pulse oximeter,shunt,transformation,批注},
 pages = {3-10},
 websites = {http://www.nature.com/articles/s41390-019-0525-2,https://www.biorxiv.org/content/10.1101/391292v3},
 month = {8},
 day = {5},
 id = {87277d31-3550-3df3-bdbf-f660deb8f7ad},
 created = {2018-08-23T04:28:04.973Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:10.692Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhou2018},
 notes = {IF-2018: 2.88},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 bibtype = {article},
 author = {Zhou, Guohai and Karlen, Walter and Brant, Rollin and Wiens, Matthew and Kissoon, Niranjan and Ansermino, J. Mark},
 doi = {10.1038/s41390-019-0525-2},
 journal = {Pediatric Research}
}

@article{
 title = {PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data},
 type = {article},
 year = {2019},
 pages = {1118-25},
 volume = {33},
 websites = {https://arxiv.org/abs/1810.01485,https://aaai.org/ojs/index.php/AAAI/article/view/3904},
 month = {7},
 publisher = {AAAI Press},
 day = {17},
 city = {Honolulu, HI, USA},
 id = {d42f564e-a17e-373c-8517-511690ee24bc},
 created = {2018-11-23T14:41:50.761Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:19.794Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2018e},
 notes = {Acceptance rate: 0.16},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30,8bf4ed74-a467-49ad-8f7c-6ab98c981268},
 private_publication = {false},
 abstract = {Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.},
 bibtype = {article},
 author = {Schwab, Patrick and Karlen, Walter},
 doi = {10.1609/aaai.v33i01.33011118},
 journal = {Proceedings of the AAAI Conference on Artificial Intelligence}
}

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.

@article{
 title = {Quantifying cyanide in water and foodstuff using corrin-based CyanoKit technologies and a smartphone},
 type = {article},
 year = {2019},
 pages = {130-136},
 volume = {144},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/30460362,http://xlink.rsc.org/?DOI=C8AN01059E},
 id = {022a8f5c-1a78-340b-a3aa-8a993a5a4783},
 created = {2018-11-29T21:29:10.586Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:16.224Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Cherbuin2018},
 notes = {IF-2018: 4.02},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {This paper describes the detection of endogenous cyanide using corrin-based CyanoKit technologies in combination with a smartphone readout device.},
 bibtype = {article},
 author = {Cherbuin, Mathias and Zelder, Felix and Karlen, Walter},
 doi = {10.1039/C8AN01059E},
 journal = {The Analyst},
 number = {1}
}

This paper describes the detection of endogenous cyanide using corrin-based CyanoKit technologies in combination with a smartphone readout device.

@article{
 title = {Immuno-gold silver staining assays on capillary-driven microfluidics for the detection of malaria antigens.},
 type = {article},
 year = {2019},
 keywords = {Capillary,Fluorescent beads,Malaria infection,Microfluidics,PfHRP2,Silver staining immunoassays},
 pages = {24},
 volume = {21},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/30810808},
 day = {27},
 id = {444823dc-9e89-3bcd-9298-b0b89e5c77ea},
 created = {2019-04-10T09:38:15.366Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:56.743Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pham2018d},
 notes = {IF-2018: <br/>2.327},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {Accurate and affordable rapid diagnostic tests (RDTs) are indispensable but often lacking for many infectious diseases. Specifically, there is a lack of highly sensitive malaria RDTs that can detect low antigen concentration at the onset of infection. Here, we present a strategy to improve the sensitivity of malaria RDTs by using capillary-driven microfluidic chips and combining sandwich immunoassays with electroless silver staining. We used 5 μm fluorescent beads functionalized with capture antibodies (cAbs). These beads are self-assembled by capillary action in recessed "bead lanes", which cross the main flow path of chips microfabricated in Si and SU-8. The binding of analytes to detection antibodies (dAbs) and secondary antibodies (2ndAbs) conjugated to gold nanoparticles (NPs) allows the formation of a silver film on the beads. Such silver film masks the fluorescent core of the bead inversely proportional to the concentration of antigen in a sample. We illustrate this method using the recombinant malaria antigen Plasmodium falciparum histidine-rich-protein 2 (rPfHRP2) spiked in human serum. This antigen was a recombinant HRP2 protein expressed in Escherichia coli, which is also the standard reference material. The limit of detection (LOD) of our immunoassay was found to be less than 6 ng mL-1 of rPfHRP2 within 20 min, which is approaching the desired sensitivity needed in the Target Product Profile (TPP) for malaria elimination settings. The concept presented here is flexible and may also be utilized for implementing fluorescence immunoassays for the parallel detection of biomarkers on capillary-driven microfluidic chips.},
 bibtype = {article},
 author = {Pham, Ngoc M and Rusch, Sebastian and Temiz, Yuksel and Beck, Hans-Peter and Karlen, Walter and Delamarche, Emmanuel},
 doi = {10.1007/s10544-019-0376-y},
 journal = {Biomedical microdevices},
 number = {1}
}

Accurate and affordable rapid diagnostic tests (RDTs) are indispensable but often lacking for many infectious diseases. Specifically, there is a lack of highly sensitive malaria RDTs that can detect low antigen concentration at the onset of infection. Here, we present a strategy to improve the sensitivity of malaria RDTs by using capillary-driven microfluidic chips and combining sandwich immunoassays with electroless silver staining. We used 5 μm fluorescent beads functionalized with capture antibodies (cAbs). These beads are self-assembled by capillary action in recessed "bead lanes", which cross the main flow path of chips microfabricated in Si and SU-8. The binding of analytes to detection antibodies (dAbs) and secondary antibodies (2ndAbs) conjugated to gold nanoparticles (NPs) allows the formation of a silver film on the beads. Such silver film masks the fluorescent core of the bead inversely proportional to the concentration of antigen in a sample. We illustrate this method using the recombinant malaria antigen Plasmodium falciparum histidine-rich-protein 2 (rPfHRP2) spiked in human serum. This antigen was a recombinant HRP2 protein expressed in Escherichia coli, which is also the standard reference material. The limit of detection (LOD) of our immunoassay was found to be less than 6 ng mL-1 of rPfHRP2 within 20 min, which is approaching the desired sensitivity needed in the Target Product Profile (TPP) for malaria elimination settings. The concept presented here is flexible and may also be utilized for implementing fluorescence immunoassays for the parallel detection of biomarkers on capillary-driven microfluidic chips.

@article{
 title = {Configurable Mobile System for Autonomous High-Quality Sleep Monitoring and Closed-Loop Acoustic Stimulation},
 type = {article},
 year = {2019},
 pages = {1-4},
 volume = {3},
 websites = {https://ieeexplore.ieee.org/document/8703847/},
 month = {5},
 id = {94979261-7b3b-3169-9d56-b089af3b2756},
 created = {2019-05-08T09:35:38.703Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:55.892Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ferster2019},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 bibtype = {article},
 author = {Ferster, Maria Laura and Lustenberger, Caroline and Karlen, Walter},
 doi = {10.1109/LSENS.2019.2914425},
 journal = {IEEE Sensors Letters},
 number = {5}
}

@article{
 title = {Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l.},
 type = {article},
 year = {2019},
 keywords = {altitude,child health,hypoxemia,model,oxygen saturation,physiological adaptation,pneumonia},
 pages = {847-57},
 volume = {127},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/31525318,https://www.physiology.org/doi/10.1152/japplphysiol.00478.2018},
 id = {16ba58a5-0f20-3276-b39d-ed37244185e5},
 created = {2019-07-14T11:35:24.538Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:16.438Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Tushaus2019},
 notes = {IF-2018: 3.14},
 folder_uuids = {1fb64632-2f0e-4803-a360-a363ae831519,f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,60555479-b7f0-45f5-aa97-a3920f93c426,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since [Formula: see text] is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4,000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical data sets independently recorded from Peruvian children living at altitudes up to 4,100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal [Formula: see text] in only 17 out of 5,981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrized model is that it is rooted in physiology-derived equations and enables customization. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.},
 bibtype = {article},
 author = {Tüshaus, Laura and Moreo, Monica and Zhang, Jia and Hartinger, Stella Maria and Mäusezahl, Daniel and Karlen, Walter},
 doi = {10.1152/japplphysiol.00478.2018},
 journal = {Journal of Applied Physiology},
 number = {3}
}

Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since [Formula: see text] is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4,000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical data sets independently recorded from Peruvian children living at altitudes up to 4,100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal [Formula: see text] in only 17 out of 5,981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrized model is that it is rooted in physiology-derived equations and enables customization. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.

@inproceedings{
 title = {CXPlain: Causal Explanations for Model Interpretation under Uncertainty},
 type = {inproceedings},
 year = {2019},
 pages = {9211},
 volume = {32},
 websites = {https://arxiv.org/abs/1910.12336},
 city = {Vancouver, CA},
 series = {Advances in Neural Information Processing Systems},
 id = {28da2359-4125-3ed9-8117-2b2a4c62fbf5},
 created = {2019-09-12T10:56:46.160Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:18.579Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2019},
 notes = {Acceptance rate: 0.21},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Feature importance estimates that inform us about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models. However, providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges. Here, we frame the task of providing explanations for the decisions of machine-learning models as a causal learning task, and train causal explanation (CXPlain) models that learn to estimate to what degree certain inputs cause outputs in another machine-learning model. CXPlain can, once trained, be used to explain the target model in little time, and enables the quantification of the uncertainty associated with its feature importance estimates via bootstrap ensembling. We present experiments that demonstrate that CXPlain is significantly more accurate than both existing model-agnostic and model-specific methods for estimating feature importance, and that it is signifi- cantly faster than other model-agnostic methods. In addition, we confirm that the uncertainty estimates provided by CXPlain ensembles are strongly correlated with their ability to accurately estimate feature importance on held-out data.},
 bibtype = {inproceedings},
 author = {Schwab, Patrick and Karlen, Walter},
 editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and D'Alché-Buc, F. and Fox, E. and Garnett, R.},
 booktitle = {Advances in Neural Information Processing Systems 32 (NeurIPS 2019)}
}

Feature importance estimates that inform us about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models. However, providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges. Here, we frame the task of providing explanations for the decisions of machine-learning models as a causal learning task, and train causal explanation (CXPlain) models that learn to estimate to what degree certain inputs cause outputs in another machine-learning model. CXPlain can, once trained, be used to explain the target model in little time, and enables the quantification of the uncertainty associated with its feature importance estimates via bootstrap ensembling. We present experiments that demonstrate that CXPlain is significantly more accurate than both existing model-agnostic and model-specific methods for estimating feature importance, and that it is signifi- cantly faster than other model-agnostic methods. In addition, we confirm that the uncertainty estimates provided by CXPlain ensembles are strongly correlated with their ability to accurately estimate feature importance on held-out data.

@article{
 title = {How stress affects sleep and mental health: Nocturnal heartrate increases during prolonged stress and interacts with childhood trauma exposure to predict anxiety.},
 type = {article},
 year = {2019},
 keywords = {anxiety,childhood trauma,heart rate,medical students,sleep},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/31863098,https://academic.oup.com/sleep/advance-article/doi/10.1093/sleep/zsz310/5682806},
 id = {200f99b9-85c6-35b0-9185-1ce62b2fd616},
 created = {2019-11-01T07:06:04.007Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:17.093Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Azza2019},
 notes = {IF-2018: 4.57},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {STUDY OBJECTIVES Stress can adversely impact sleep health by eliciting arousal increase and a cascade of endocrine reactions that may impair sleep. To date, little is known regarding continuous effects of real-world stress on physiological sleep characteristics and potential effects on stress-related psychopathology. We examined effects of stress on heart-rate (HR) during sleep and total sleep time (TST) during prolonged real-world stress exposure in medical interns. Moreover, we investigated the influence of previous stress and childhood trauma exposure on HR during sleep, TST, and its interaction in predicting anxiety. METHODS We examined a sample of 50 medical students prior to and during their first internship, a well described real-world stressor. Heartrate and total sleep time were continuously collected over 12 weeks non-invasively by a wrist-worn activity monitor. Prior to starting the internship, at baseline, participants reported on their sleep, anxiety and childhood trauma exposure. They also tracked stress exposure during internship and reported on their anxiety symptoms after 3 months after this professional stress. RESULTS Mean HR during sleep increased over time, while TST remained unchanged. This effect was more pronounced in interns exposed to childhood trauma exposure. In multilevel models, childhood trauma exposure also moderated the relation between individual HR increase and development of anxiety. CONCLUSIONS Prolonged stress may lead to increased HR during sleep, whereas individuals with childhood trauma exposure are more vulnerable. childhood trauma exposure also moderated the relation between individual HR increase and development of anxiety. These findings may inform prevention and intervention measures.},
 bibtype = {article},
 author = {Azza, Yasmine and Grueschow, Marcus and Karlen, Walter and Seifritz, Erich and Kleim, Birgit},
 doi = {10.1093/sleep/zsz310},
 journal = {Sleep}
}

STUDY OBJECTIVES Stress can adversely impact sleep health by eliciting arousal increase and a cascade of endocrine reactions that may impair sleep. To date, little is known regarding continuous effects of real-world stress on physiological sleep characteristics and potential effects on stress-related psychopathology. We examined effects of stress on heart-rate (HR) during sleep and total sleep time (TST) during prolonged real-world stress exposure in medical interns. Moreover, we investigated the influence of previous stress and childhood trauma exposure on HR during sleep, TST, and its interaction in predicting anxiety. METHODS We examined a sample of 50 medical students prior to and during their first internship, a well described real-world stressor. Heartrate and total sleep time were continuously collected over 12 weeks non-invasively by a wrist-worn activity monitor. Prior to starting the internship, at baseline, participants reported on their sleep, anxiety and childhood trauma exposure. They also tracked stress exposure during internship and reported on their anxiety symptoms after 3 months after this professional stress. RESULTS Mean HR during sleep increased over time, while TST remained unchanged. This effect was more pronounced in interns exposed to childhood trauma exposure. In multilevel models, childhood trauma exposure also moderated the relation between individual HR increase and development of anxiety. CONCLUSIONS Prolonged stress may lead to increased HR during sleep, whereas individuals with childhood trauma exposure are more vulnerable. childhood trauma exposure also moderated the relation between individual HR increase and development of anxiety. These findings may inform prevention and intervention measures.

@article{
 title = {Are respiratory rate counters really so bad? Throwing the baby out with the bath water},
 type = {article},
 year = {2019},
 pages = {14},
 volume = {16},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S2589537019301786},
 month = {11},
 id = {57a6ec27-de3a-3fcc-8525-78ac4b9c9a7d},
 created = {2019-11-25T05:46:26.658Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:19.360Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ansermino2019},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Ansermino, J Mark and Dunsmuir, Dustin and Karlen, Walter and Gan, Heng and Dumont, Guy A},
 doi = {10.1016/j.eclinm.2019.09.013},
 journal = {EClinicalMedicine}
}

@article{
 title = {Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks},
 type = {article},
 year = {2019},
 pages = {4846-53},
 volume = {33},
 websites = {http://arxiv.org/abs/1802.02195,https://aaai.org/ojs/index.php/AAAI/article/view/4412},
 month = {7},
 publisher = {AAAI Press},
 day = {17},
 city = {Honolulu, HI, USA},
 id = {95bb8d99-7548-3461-9465-2c956c806117},
 created = {2020-12-11T07:58:41.553Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:25.644Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2018},
 notes = {Acceptance rate: 0.16},
 folder_uuids = {1fb64632-2f0e-4803-a360-a363ae831519,f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-theart methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.},
 bibtype = {article},
 author = {Schwab, Patrick and Miladinovic, Djordje and Karlen, Walter},
 doi = {10.1609/aaai.v33i01.33014846},
 journal = {Proceedings of the AAAI Conference on Artificial Intelligence}
}

Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-theart methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.

@article{
 title = {A bead-based immunogold-silver staining assay on capillary-driven microfluidics.},
 type = {article},
 year = {2018},
 keywords = {Immunoassays,Microbeads,Microfluidics,Silver staining},
 pages = {41},
 volume = {20},
 websites = {http://link.springer.com/10.1007/s10544-018-0284-6,http://www.ncbi.nlm.nih.gov/pubmed/29781041},
 month = {5},
 publisher = {Biomedical Microdevices},
 day = {21},
 id = {9acd70a6-a2b6-34d2-9689-00246a707655},
 created = {2018-05-21T20:20:19.632Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:01.226Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pham2018b},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0f17b6d7-75e3-4f99-a546-6054e4a41808},
 private_publication = {false},
 abstract = {Point-of-care (POC) diagnostics are critically needed for the detection of infectious diseases, particularly in remote settings where accurate and appropriate diagnosis can save lives. However, it is difficult to implement immunoassays, and specifically immunoassays relying on signal amplification using silver staining, into POC diagnostic devices. Effective immobilization of antibodies in such devices is another challenge. Here, we present strategies for immobilizing capture antibodies (cAbs) in capillary-driven microfluidic chips and implementing a gold-catalyzed silver staining reaction. We illustrate these strategies using a species/anti-species immunoassay and the capillary assembly of fluorescent microbeads functionalized with cAbs in "bead lanes", which are engraved in microfluidic chips. The microfluidic chips are fabricated in silicon (Si) and sealed with a dry film resist. Rabbit IgG antibodies in samples are captured on the beads and bound by detection antibodies (dAbs) conjugated to gold nanoparticles. The gold nanoparticles catalyze the formation of a metallic film of silver, which attenuates fluorescence from the beads in an analyte-concentration dependent manner. The performance of these immunoassays was found comparable to that of assays performed in 96 well microtiter plates using "classical" enzyme-linked immunosorbent assay (ELISA). The proof-of-concept method developed here can detect 24.6 ng mL-1 of rabbit IgG antibodies in PBS within 20 min, in comparison to 17.1 ng mL-1 of the same antibodies using a ~140-min-long ELISA protocol. Furthermore, the concept presented here is flexible and necessitate volumes of samples and reagents in the range of just a few microliters.},
 bibtype = {article},
 author = {Pham, Ngoc M and Rusch, Sebastian and Temiz, Yuksel and Lovchik, Robert D and Beck, Hans-Peter and Karlen, Walter and Delamarche, Emmanuel},
 doi = {10.1007/s10544-018-0284-6},
 journal = {Biomedical microdevices}
}

Point-of-care (POC) diagnostics are critically needed for the detection of infectious diseases, particularly in remote settings where accurate and appropriate diagnosis can save lives. However, it is difficult to implement immunoassays, and specifically immunoassays relying on signal amplification using silver staining, into POC diagnostic devices. Effective immobilization of antibodies in such devices is another challenge. Here, we present strategies for immobilizing capture antibodies (cAbs) in capillary-driven microfluidic chips and implementing a gold-catalyzed silver staining reaction. We illustrate these strategies using a species/anti-species immunoassay and the capillary assembly of fluorescent microbeads functionalized with cAbs in "bead lanes", which are engraved in microfluidic chips. The microfluidic chips are fabricated in silicon (Si) and sealed with a dry film resist. Rabbit IgG antibodies in samples are captured on the beads and bound by detection antibodies (dAbs) conjugated to gold nanoparticles. The gold nanoparticles catalyze the formation of a metallic film of silver, which attenuates fluorescence from the beads in an analyte-concentration dependent manner. The performance of these immunoassays was found comparable to that of assays performed in 96 well microtiter plates using "classical" enzyme-linked immunosorbent assay (ELISA). The proof-of-concept method developed here can detect 24.6 ng mL-1 of rabbit IgG antibodies in PBS within 20 min, in comparison to 17.1 ng mL-1 of the same antibodies using a ~140-min-long ELISA protocol. Furthermore, the concept presented here is flexible and necessitate volumes of samples and reagents in the range of just a few microliters.

@inproceedings{
 title = {Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care},
 type = {inproceedings},
 year = {2018},
 pages = {4525--34},
 volume = {80},
 websites = {http://arxiv.org/abs/1802.05027},
 month = {2},
 day = {14},
 city = {Stockholm, Sweden},
 series = {Proceedings of Machine Learning Research},
 id = {62e76d15-a0e7-383f-be70-8d709ee66617},
 created = {2018-05-31T07:22:57.387Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:01.457Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2018b},
 notes = {Acceptance rate: 0.25},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive to movement artefacts and technical errors. As a result, they typically trigger hundreds to thousands of false alarms per patient per day - drowning the important alarms in noise and adding to the exhaustion of clinical staff. In this setting, data is abundantly available, but obtaining trustworthy annotations by experts is laborious and expensive. We frame the problem of false alarm reduction from multivariate time series as a machine-learning task and address it with a novel multitask network architecture that utilises distant supervision through multiple related auxiliary tasks in order to reduce the number of expensive labels required for training. We show that our approach leads to significant improvements over several state-of-the-art baselines on real-world ICU data and provide new insights on the importance of task selection and architectural choices in distantly supervised multitask learning.},
 bibtype = {inproceedings},
 author = {Schwab, Patrick and Keller, Emanuela and Muroi, Carl and Mack, David J and Strässle, Christian and Karlen, Walter},
 doi = {10.3929/ethz-b-000241127},
 booktitle = {Proceedings of the 35th International Conference on Machine Learning, ICML 2018}
}

Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive to movement artefacts and technical errors. As a result, they typically trigger hundreds to thousands of false alarms per patient per day - drowning the important alarms in noise and adding to the exhaustion of clinical staff. In this setting, data is abundantly available, but obtaining trustworthy annotations by experts is laborious and expensive. We frame the problem of false alarm reduction from multivariate time series as a machine-learning task and address it with a novel multitask network architecture that utilises distant supervision through multiple related auxiliary tasks in order to reduce the number of expensive labels required for training. We show that our approach leads to significant improvements over several state-of-the-art baselines on real-world ICU data and provide new insights on the importance of task selection and architectural choices in distantly supervised multitask learning.

@article{
 title = {Investigating Pre-sleep Processes And How They Influence Sleep And Nightmares},
 type = {article},
 year = {2018},
 pages = {A372-A373},
 volume = {41},
 websites = {https://academic.oup.com/sleep/article/41/suppl_1/A372/4988049},
 month = {4},
 day = {27},
 id = {4bebed2b-824c-32a1-89c4-48baaa8d7ccc},
 created = {2018-06-04T19:21:39.713Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:01.004Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Maeder2018},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb,92c0b725-22b5-4be1-8c92-eff848e26a9e},
 private_publication = {false},
 bibtype = {article},
 author = {Maeder, Thomas and Whitford, J and Feinaigle, P and Karlen, Walter and Kleim, Birgit},
 doi = {10.1093/sleep/zsy061.1005},
 journal = {Sleep},
 number = {suppl_1}
}

@article{
 title = {Malaria and the ‘last’ parasite: how can technology help?},
 type = {article},
 year = {2018},
 keywords = {Elimination,Malaria,Microfluidics,Rapid diagnostic tests,Smartphones},
 pages = {260},
 volume = {17},
 websites = {https://malariajournal.biomedcentral.com/articles/10.1186/s12936-018-2408-0},
 publisher = {BioMed Central},
 id = {40ffaa11-fb93-3073-bdb4-4323096fb744},
 created = {2018-07-16T09:22:59.205Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:17.912Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pham2018c},
 notes = {IF-2018: 2.8},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {Malaria, together with HIV/AIDS, tuberculosis and hepatitis are the four most deadly infectious diseases globally. Progress in eliminating malaria has saved millions of lives, but also creates new challenges in detecting the ‘last para‑ site’. Effective and accurate detection of malaria infections, both in symptomatic and asymptomatic individuals are needed. In this review, the current progress in developing new diagnostic tools to fight malaria is presented. An ideal rapid test for malaria elimination is envisioned with examples to demonstrate how innovative technologies can assist the global defeat against this disease. Diagnostic gaps where technology can bring an impact to the elimination cam‑ paign for malaria are identified. Finally, how a combination of microfluidic‑based technologies and smartphone‑based read‑outs could potentially represent the next generation of rapid diagnostic tests is discussed},
 bibtype = {article},
 author = {Pham, Ngoc M and Karlen, Walter and Beck, Hans-Peter and Delamarche, Emmanuel},
 doi = {10.1186/s12936-018-2408-0},
 journal = {Malaria Journal},
 number = {1}
}

Malaria, together with HIV/AIDS, tuberculosis and hepatitis are the four most deadly infectious diseases globally. Progress in eliminating malaria has saved millions of lives, but also creates new challenges in detecting the ‘last para‑ site’. Effective and accurate detection of malaria infections, both in symptomatic and asymptomatic individuals are needed. In this review, the current progress in developing new diagnostic tools to fight malaria is presented. An ideal rapid test for malaria elimination is envisioned with examples to demonstrate how innovative technologies can assist the global defeat against this disease. Diagnostic gaps where technology can bring an impact to the elimination cam‑ paign for malaria are identified. Finally, how a combination of microfluidic‑based technologies and smartphone‑based read‑outs could potentially represent the next generation of rapid diagnostic tests is discussed

@inproceedings{
 title = {Multispectral camera fusion increases robustness of ROI detection for biosignal estimation with nearables in real-world scenarios},
 type = {inproceedings},
 year = {2018},
 pages = {5672-5},
 websites = {https://ieeexplore.ieee.org/document/8513501/},
 month = {7},
 publisher = {IEEE},
 city = {Honolulu, HI, USA},
 id = {12517edf-e55b-36b6-b2e4-8abbbdb91fc7},
 created = {2018-11-08T08:20:04.310Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:14.722Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Scebba2018},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Thermal cameras enable non-contact estimation of the respiratory rate (RR). Accurate estimation of RR is highly dependent on the reliable detection of the region of interest (ROI), especially when using cameras with low pixel resolution. We present a novel approach for the automatic detection of the human nose ROI, based on facial landmark detection from an RGB camera that is fused with the thermal image after tracking. We evaluated the detection rate and spatial accuracy of the novel algorithm on recordings obtained from 16 subjects under challenging detection scenarios. Results show a high detection rate (median: 100 %, 5th - 95th percentile: 92 % - 100 %) and very good spatial accuracy with an average root mean square error of 2 pixels in the detected ROI center when compared to manual labeling. Therefore, the implementation of a multispectral camera fusion algorithm is a valid strategy to improve the reliability of non-contact RR estimation with nearable devices featuring thermal cameras.},
 bibtype = {inproceedings},
 author = {Scebba, Gaetano Claudio and Tüshaus, Laura and Karlen, Walter},
 doi = {10.1109/EMBC.2018.8513501},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Thermal cameras enable non-contact estimation of the respiratory rate (RR). Accurate estimation of RR is highly dependent on the reliable detection of the region of interest (ROI), especially when using cameras with low pixel resolution. We present a novel approach for the automatic detection of the human nose ROI, based on facial landmark detection from an RGB camera that is fused with the thermal image after tracking. We evaluated the detection rate and spatial accuracy of the novel algorithm on recordings obtained from 16 subjects under challenging detection scenarios. Results show a high detection rate (median: 100 %, 5th - 95th percentile: 92 % - 100 %) and very good spatial accuracy with an average root mean square error of 2 pixels in the detected ROI center when compared to manual labeling. Therefore, the implementation of a multispectral camera fusion algorithm is a valid strategy to improve the reliability of non-contact RR estimation with nearable devices featuring thermal cameras.

@article{
 title = {Changes of hemodynamic and cerebral oxygenation after exercise in normobaric and hypobaric hypoxia: associations with acute mountain sickness.},
 type = {article},
 year = {2018},
 keywords = {Acute mountain sickness,Cerebral oxygenation,Cognitive dysfunction,Hypobaric hypoxia,Near-infrared spectroscopy,Normobaric hypoxia},
 pages = {66},
 volume = {30},
 websites = {https://aoemj.biomedcentral.com/articles/10.1186/s40557-018-0276-2,http://www.ncbi.nlm.nih.gov/pubmed/30479778,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6245893},
 publisher = {Annals of Occupational and Environmental Medicine},
 id = {f9b6e237-19dc-3edb-a0ca-f181936db19b},
 created = {2018-11-29T21:20:56.860Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:09.749Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Kammerer2018},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 abstract = {Objective Normobaric (NH) and hypobaric hypoxia (HH) are associated with acute mountain sickness (AMS) and cognitive dysfunction. Only few variables, like heart-rate-variability, are correlated with AMS. However, prediction of AMS remains difficult. We therefore designed an expedition-study with healthy volunteers in NH/HH to investigate additional non-invasive hemodynamic variables associated with AMS. Methods Eleven healthy subjects were examined in NH (FiO2 13.1%; equivalent of 3.883 m a.s.l; duration 4 h) and HH (3.883 m a.s.l.; duration 24 h) before and after an exercise of 120 min. Changes in parameters of electrical cardiometry (cardiac index (CI), left-ventricular ejection time (LVET), stroke volume (SV), index of contractility (ICON)), near-infrared spectroscopy (cerebral oxygenation, rScO2), Lake-Louise-Score (LLS) and cognitive function tests were assessed. One-Way-ANOVA, Wilcoxon matched-pairs test, Spearman's-correlation-analysis and Student's t-test were performed. Results HH increased heart rate (HR), mean arterial pressure (MAP) and CI and decreased LVET, SV and ICON, whereas NH increased HR and decreased LVET. In both NH and HH cerebral oxygenation decreased and LLS increased significantly. After 24 h in HH, 6 of 11 subjects (54.6%) developed AMS. LLS remained increased until 24 h in HH, whereas cognitive function remained unaltered. In HH, HR and LLS were inversely correlated (r = - 0.692; p < 0.05). More importantly, the rScO2-decrease after exercise in NH significantly correlated with LLS after 24 h in HH (r = - 0.971; p < 0.01) and rScO2 correlated significantly with HR (r = 0.802; p < 0.01), CI (r = 0.682; p < 0.05) and SV (r = 0.709; p < 0.05) after exercise in HH. Conclusions Both acute NH and HH altered hemodynamic and cerebral oxygenation and induced AMS. Subjects, who adapted their CI had higher rScO2 and lower LLS. Furthermore, rScO2 after exercise under normobaric conditions was associated with AMS at high altitudes.},
 bibtype = {article},
 author = {Kammerer, Tobias and Faihs, Valentina and Hulde, Nikolai and Bayer, Andreas and Hübner, Max and Brettner, Florian and Karlen, Walter and Kröpfl, Julia Maria and Rehm, Markus and Spengler, Christina and Schäfer, Simon Thomas},
 doi = {10.1186/s40557-018-0276-2},
 journal = {Annals of occupational and environmental medicine},
 number = {1}
}

Objective Normobaric (NH) and hypobaric hypoxia (HH) are associated with acute mountain sickness (AMS) and cognitive dysfunction. Only few variables, like heart-rate-variability, are correlated with AMS. However, prediction of AMS remains difficult. We therefore designed an expedition-study with healthy volunteers in NH/HH to investigate additional non-invasive hemodynamic variables associated with AMS. Methods Eleven healthy subjects were examined in NH (FiO2 13.1%; equivalent of 3.883 m a.s.l; duration 4 h) and HH (3.883 m a.s.l.; duration 24 h) before and after an exercise of 120 min. Changes in parameters of electrical cardiometry (cardiac index (CI), left-ventricular ejection time (LVET), stroke volume (SV), index of contractility (ICON)), near-infrared spectroscopy (cerebral oxygenation, rScO2), Lake-Louise-Score (LLS) and cognitive function tests were assessed. One-Way-ANOVA, Wilcoxon matched-pairs test, Spearman's-correlation-analysis and Student's t-test were performed. Results HH increased heart rate (HR), mean arterial pressure (MAP) and CI and decreased LVET, SV and ICON, whereas NH increased HR and decreased LVET. In both NH and HH cerebral oxygenation decreased and LLS increased significantly. After 24 h in HH, 6 of 11 subjects (54.6%) developed AMS. LLS remained increased until 24 h in HH, whereas cognitive function remained unaltered. In HH, HR and LLS were inversely correlated (r = - 0.692; p < 0.05). More importantly, the rScO2-decrease after exercise in NH significantly correlated with LLS after 24 h in HH (r = - 0.971; p < 0.01) and rScO2 correlated significantly with HR (r = 0.802; p < 0.01), CI (r = 0.682; p < 0.05) and SV (r = 0.709; p < 0.05) after exercise in HH. Conclusions Both acute NH and HH altered hemodynamic and cerebral oxygenation and induced AMS. Subjects, who adapted their CI had higher rScO2 and lower LLS. Furthermore, rScO2 after exercise under normobaric conditions was associated with AMS at high altitudes.

@article{
 title = {Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis},
 type = {article},
 year = {2018},
 pages = {e11896},
 volume = {6},
 websites = {http://mhealth.jmir.org/2018/12/e11896/},
 month = {12},
 day = {14},
 id = {ac4eb66e-352f-39fc-864c-cc47f356429a},
 created = {2018-11-29T21:20:56.871Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:14.302Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhang2018b},
 notes = {IF-2018: 4.3},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0f17b6d7-75e3-4f99-a546-6054e4a41808,9aa961a4-04a6-4a65-b081-d534c7a81a35,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Zhang, Jia and Tüshaus, Laura and Nuño Martínez, Néstor and Moreo, Monica and Verastegui, Hector and Hartinger, Stella Maria and Mäusezahl, Daniel and Karlen, Walter},
 doi = {10.2196/11896},
 journal = {JMIR mHealth and uHealth},
 number = {12}
}

@article{
 title = {Automation in medicine: from homecare to clinical applications},
 type = {article},
 year = {2018},
 pages = {990-992},
 volume = {66},
 websites = {https://www.degruyter.com/view/j/auto.2018.66.issue-12/auto-2018-0131/auto-2018-0131.xml},
 month = {12},
 day = {19},
 id = {dafd453a-164d-3a4f-894e-7e5066c819ae},
 created = {2018-12-01T09:27:18.877Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:11.613Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2018a},
 folder_uuids = {1fb64632-2f0e-4803-a360-a363ae831519,f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {article},
 author = {Karlen, Walter and Schauer, Thomas and Möller, Knut and Simanski, Olaf},
 doi = {10.1515/auto-2018-0131},
 journal = {at - Automatisierungstechnik},
 number = {12}
}

@misc{
 title = {Perfect match: A simple method for learning representations for counterfactual inference with neural networks},
 type = {misc},
 year = {2018},
 source = {arXiv},
 id = {c4414882-f9ba-3a20-9abe-e1201b4fa7a5},
 created = {2020-10-27T23:59:00.000Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:08.921Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 citation_key = {Schwab2018a},
 folder_uuids = {1fb64632-2f0e-4803-a360-a363ae831519,f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 abstract = {Copyright © 2018, arXiv, All rights reserved. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment t1?". However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmarks, particularly in settings with many treatments.},
 bibtype = {misc},
 author = {Schwab, Patrick and Linhardt, L. and Karlen, Walter}
}

Copyright © 2018, arXiv, All rights reserved. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment t1?". However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmarks, particularly in settings with many treatments.

@inproceedings{
 title = {Improving ROI detection in photoplethysmographic imaging with thermal cameras},
 type = {inproceedings},
 year = {2017},
 pages = {4285-88},
 city = {Jeju Island, South Korea},
 id = {a4432d87-5248-390f-b2e7-771078baec9f},
 created = {2017-09-05T06:55:26.665Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:24.959Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Scebba2017a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Photoplethismographic imaging (PPGi) enables the estimation of heart rate without body contact by analyzing the temporal skin color changes from video recordings. Motion artifacts and atypical facial characteristics cause poor signals and currently limit the applicability of PPGi. We have developed a novel algorithm for locating cheek and forehead region of interests (ROI) with the aim to improve PPGi during challenging situations. The proposed approach is based on the fusion of RGB and far-infrared (FIR) video streams where FIR ROI is used as fall-back when RGB alone fails. We validated and compared the algorithm against the detection based on single sources, using videos from 8 subjects with distinctively different face characteristics. The subject performed three scenarios with incremental motion artifact content (head at rest, intensive head movements, speaking). The results showed that combining the two imaging sources increased the detection rate of cheeks from 75% (RGB) to 92% (RGB+FIR) in the challenging intensive head movement scenario. This work demonstrated that FIR imaging is complementary to simple RGB imaging and when combined, adds robustness to the detection of ROI in PPGi applications},
 bibtype = {inproceedings},
 author = {Scebba, Gaetano and Dragas, Jelena and Hu, Suyi and Karlen, Walter},
 doi = {10.1109/EMBC.2017.8037803},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Photoplethismographic imaging (PPGi) enables the estimation of heart rate without body contact by analyzing the temporal skin color changes from video recordings. Motion artifacts and atypical facial characteristics cause poor signals and currently limit the applicability of PPGi. We have developed a novel algorithm for locating cheek and forehead region of interests (ROI) with the aim to improve PPGi during challenging situations. The proposed approach is based on the fusion of RGB and far-infrared (FIR) video streams where FIR ROI is used as fall-back when RGB alone fails. We validated and compared the algorithm against the detection based on single sources, using videos from 8 subjects with distinctively different face characteristics. The subject performed three scenarios with incremental motion artifact content (head at rest, intensive head movements, speaking). The results showed that combining the two imaging sources increased the detection rate of cheeks from 75% (RGB) to 92% (RGB+FIR) in the challenging intensive head movement scenario. This work demonstrated that FIR imaging is complementary to simple RGB imaging and when combined, adds robustness to the detection of ROI in PPGi applications

@inproceedings{
 title = {Influence of acute normobaric and hypobaric hypoxia on hemodynamics, cognitive function, cerebral near-infrared spectroscopy and gene expression},
 type = {inproceedings},
 year = {2017},
 pages = {2017},
 issue = {December},
 city = {Köln},
 id = {3321628e-e704-3a94-978a-a80d70132c37},
 created = {2018-03-05T20:27:33.343Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:00.140Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Faihs2017a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb,92c0b725-22b5-4be1-8c92-eff848e26a9e},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Faihs, Valentina and Kammerer, Tobias and Hulde, Nikolai and Brettner, Florian and Rehm, Markus and Karlen, Walter and Kröpfl, Julia Maria and Spengler, Christina and Kreth, Simone and Schäfer, Simon},
 booktitle = {2nd Human Physiology Workshop}
}

@article{
 title = {Corrigendum: Variability in estimating shunt from single pulse oximetry measurements (2015 Physiol. Meas. 36 967)},
 type = {article},
 year = {2017},
 pages = {1746-1747},
 volume = {38},
 publisher = {IOP Publishing},
 id = {e024372a-37ca-3786-8b15-8e1038474a1c},
 created = {2019-05-28T14:09:29.969Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:17.315Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2017},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 bibtype = {article},
 author = {Karlen, Walter and Petersen, Chris and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1088/1361-6579/aa7c81},
 journal = {Physiological Measurement},
 number = {8}
}

@article{
 title = {Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks},
 type = {article},
 year = {2017},
 pages = {1-4},
 volume = {44},
 websites = {http://www.cinc.org/archives/2017/pdf/363-223.pdf},
 month = {9},
 day = {14},
 city = {Rennes, F},
 id = {39dd8240-969e-3db5-b8b4-afc49667129d},
 created = {2020-12-13T11:41:09.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:03.468Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schwab2017},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {INTRODUCTION: Previous work on detecting arrhythmias in electrocardiogram (ECG) records has predominantly focused on identifying atrial fibrillation (AF) in data obtained from clinical settings or Holter devices, where long-term recordings with multiple leads are the norm. However, the advent of mobile cardiac event recorders increased the importance of being able to differentiate between multiple types of rhythms in noisy short-term recordings with just a single lead. We propose a machine-learning architecture to learn the temporal and morphological patterns of various types of rhythms in order to perform multiclass classification under these more challenging conditions. METHODS: We segment the input ECG signal with a QRS detector into individual heartbeats. From each heartbeat, we extract - among others - morphological features with the encoding side of a stacked denoising autoencoder that was trained in an unsupervised manner. The extracted features are passed in original heartbeat order as input sequences to an ensemble of recurrent neural networks (RNNs). The RNNs were trained on different features, random overlapping subsets of the training data and in various one-versus-all setups in order to increase the model diversity within the ensemble. We blend the individual RNNs’ predictions into a final classification solution using a multilayer perceptron (MLP) that was trained on held-out data. RESULTS: Our best ensemble at time of writing achieves an average F1-score over all classes of 0.78 (F1,normal=0.88, F1,af=0.75, F1,other=0.72, F1,noisy=0.78) on an out-of-sample test set (342 samples) and an average F1-score over all classes of 0.65 (F1,normal=0.82, F1,af=0.77, F1,other=0.64, F1,noisy=0.36) on the private test set for phase 1 of the PhysioNet 2017 challenge. CONCLUSION: Deep recurrent models enable our ensemble to differentiate between multiple types of heart rhythms by identifying temporal and morphological patterns in segmented ECG recordings of any length.},
 bibtype = {article},
 author = {Schwab, Patrick and Scebba, Gaetano Claudio and Zhang, Jia and Delai, Marco and Karlen, Walter},
 doi = {10.22489/CinC.2017.363-223},
 journal = {Computing in Cardiology (CinC)}
}

INTRODUCTION: Previous work on detecting arrhythmias in electrocardiogram (ECG) records has predominantly focused on identifying atrial fibrillation (AF) in data obtained from clinical settings or Holter devices, where long-term recordings with multiple leads are the norm. However, the advent of mobile cardiac event recorders increased the importance of being able to differentiate between multiple types of rhythms in noisy short-term recordings with just a single lead. We propose a machine-learning architecture to learn the temporal and morphological patterns of various types of rhythms in order to perform multiclass classification under these more challenging conditions. METHODS: We segment the input ECG signal with a QRS detector into individual heartbeats. From each heartbeat, we extract - among others - morphological features with the encoding side of a stacked denoising autoencoder that was trained in an unsupervised manner. The extracted features are passed in original heartbeat order as input sequences to an ensemble of recurrent neural networks (RNNs). The RNNs were trained on different features, random overlapping subsets of the training data and in various one-versus-all setups in order to increase the model diversity within the ensemble. We blend the individual RNNs’ predictions into a final classification solution using a multilayer perceptron (MLP) that was trained on held-out data. RESULTS: Our best ensemble at time of writing achieves an average F1-score over all classes of 0.78 (F1,normal=0.88, F1,af=0.75, F1,other=0.72, F1,noisy=0.78) on an out-of-sample test set (342 samples) and an average F1-score over all classes of 0.65 (F1,normal=0.82, F1,af=0.77, F1,other=0.64, F1,noisy=0.36) on the private test set for phase 1 of the PhysioNet 2017 challenge. CONCLUSION: Deep recurrent models enable our ensemble to differentiate between multiple types of heart rhythms by identifying temporal and morphological patterns in segmented ECG recordings of any length.

@article{
 title = {Building quality mHealth for low resource settings},
 type = {article},
 year = {2016},
 pages = {431-43},
 volume = {40},
 websites = {https://www.tandfonline.com/doi/full/10.1080/03091902.2016.1213906},
 day = {25},
 id = {9f315ceb-653d-38e9-a04d-9b35725cb082},
 created = {2014-07-26T01:09:20.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:01.715Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ettinger2016},
 notes = {IF-2018: n/a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0f17b6d7-75e3-4f99-a546-6054e4a41808,9aa961a4-04a6-4a65-b081-d534c7a81a35,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Ettinger, Kate Michi and Pharaoh, Hamilton and Buckman, Reymound Yaw and Conradie, Hoffie and Karlen, Walter},
 doi = {10.1080/03091902.2016.1213906},
 journal = {Journal of Medical Engineering & Technology},
 number = {7-8}
}

@article{
 title = {Evaluation of cardiac modulation in children in response to apnea/hypopnea using the Phone Oximeter™},
 type = {article},
 year = {2016},
 pages = {187-202},
 volume = {37},
 websites = {http://stacks.iop.org/0967-3334/37/i=2/a=187?key=crossref.90bafa387933f4fb238552e83edf9c42},
 id = {9537742f-8f60-3cd1-8530-14ccb11e5fae},
 created = {2015-11-19T06:33:05.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:12.069Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dehkordi2015},
 notes = {IF-2018: 2.25},
 folder_uuids = {1fb64632-2f0e-4803-a360-a363ae831519,f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Petersen, Chris L and Wensley, David and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1088/0967-3334/37/2/187},
 journal = {Physiological Measurement},
 number = {2}
}

@inproceedings{
 title = {Analysis of the Predictive Potential of Pulse Oximeter Data for Admission},
 type = {inproceedings},
 year = {2016},
 pages = {75-76},
 volume = {122},
 issue = {5S},
 publisher = {Anesthesia and analgesia},
 id = {745577e2-9607-3da8-9e0c-4758a7b25738},
 created = {2016-06-23T15:34:09.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:54.602Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dunsmuir2016},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0f17b6d7-75e3-4f99-a546-6054e4a41808,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dunsmuir, Dustin and Garde, Ainara and Zhou, Guohai and Raihana, Shahreen and Huda, Tanvir and Karlen, Walter and Dehkordi, Parastoo and Arifeen, Shams El and Ansermino, Mark J},
 booktitle = {Proceedings of the 2016 Society for Technology in Anesthesia Annual Meeting}
}

@inproceedings{
 title = {A pulse transit time based fusion method for the noninvasive and continuous monitoring of respiratory rate},
 type = {inproceedings},
 year = {2016},
 pages = {4240-3},
 websites = {http://ieeexplore.ieee.org/document/7591663/},
 publisher = {IEEE},
 id = {33565b54-0f97-33f0-9c2c-3dd225b3e4d8},
 created = {2016-07-05T14:51:20.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:22.712Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ding2016},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Ding, Xiao-Rong and Zhang, Yuan-Ting and Tsang, Hon Ki and Karlen, Walter},
 doi = {10.1109/EMBC.2016.7591663},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

@article{
 title = {Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study},
 type = {article},
 year = {2016},
 pages = {e011094},
 volume = {6},
 websites = {http://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2016-011094},
 id = {45b75c5f-8211-3bb5-b589-2d7bc650b126},
 created = {2016-08-19T05:51:40.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:11.146Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2016d},
 notes = {IF-2018: <br/>2.376},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02},
 private_publication = {false},
 bibtype = {article},
 author = {Garde, Ainara and Zhou, Guohai and Raihana, Shahreen and Dunsmuir, Dustin and Karlen, Walter and Dehkordi, Parastoo and Huda, Tanvir and Arifeen, Shams El and Larson, Charles P and Kissoon, Niranjan and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1136/bmjopen-2016-011094},
 journal = {BMJ Open},
 number = {8}
}

@article{
 title = {Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study},
 type = {article},
 year = {2016},
 pages = {e011094},
 volume = {6},
 websites = {http://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2016-011094},
 id = {56a8c6b8-dc0a-3f50-98ab-73f3d60054a3},
 created = {2021-11-19T07:51:12.356Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:59.046Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2016b},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,20f4663c-5015-44ee-8c35-394df252dd32},
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mHealth enables remote health monitoring and facilitates communication and data exchange between health providers and patients, but comes at the cost of reduced data quality. Loss of quality can be multifaceted and includes an increase in missing data and a higher level of uncertainty in data compared to clinical data acquisition. This short review discusses emerging strategies to cope with challenges in keeping mHealth systems that are used by lay users reliable.

@article{
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@article{
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@article{
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@article{
 title = {Variability in estimating shunt from single pulse oximetry measurements},
 type = {article},
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 abstract = {Virtual shunt describes the overall loss of O2 content between the alveolar gas and arterial blood. Clinicians indirectly estimate the magnitude of the virtual shunt by monitoring peripheral blood oxygen saturation (SpO2) using non-invasive pulse oximetry. An inherent limitation of this method is the variable precision of pulse oximeters and the non-linear relationship between virtual shunt and SpO2 which is rarely depicted.We propose a model using a combination of basic physiological equations to analyze the estimation of virtual shunt from inspired oxygen (FiO2) and SpO2. The model emphasizes the effect of the non-linearity of the Hb-O2 dissociation curve. Furthermore, it accounts for the variability in SpO2 measurements due to the precision of pulse oximeters.The model was validated with experiments conducted on healthy subjects in a normobaric hypoxia chamber comparing the simultaneous readings from two different commercial pulse oximeters at FiO2 = 21% and 17%. SpO2 probability distributions calculated from the model were estimated. Although a variable bias (1.2-2.1%) in SpO2 between the pulse oximeter brands was observed, the tested pulse oximeters were both within tolerance specified by the manufacturers and matched the probability distributions from the model.The theoretical and experimental findings show that the estimation of virtual shunt is challenging with a single SpO2 measurement using pulse oximeters with tolerances of 2%. Clinical decisions must be based on an appreciation of these limitations.},
 bibtype = {article},
 author = {Karlen, Walter and Petersen, Christian L and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1088/0967-3334/36/5/967},
 journal = {Physiological measurement},
 number = {5}
}

Virtual shunt describes the overall loss of O2 content between the alveolar gas and arterial blood. Clinicians indirectly estimate the magnitude of the virtual shunt by monitoring peripheral blood oxygen saturation (SpO2) using non-invasive pulse oximetry. An inherent limitation of this method is the variable precision of pulse oximeters and the non-linear relationship between virtual shunt and SpO2 which is rarely depicted.We propose a model using a combination of basic physiological equations to analyze the estimation of virtual shunt from inspired oxygen (FiO2) and SpO2. The model emphasizes the effect of the non-linearity of the Hb-O2 dissociation curve. Furthermore, it accounts for the variability in SpO2 measurements due to the precision of pulse oximeters.The model was validated with experiments conducted on healthy subjects in a normobaric hypoxia chamber comparing the simultaneous readings from two different commercial pulse oximeters at FiO2 = 21% and 17%. SpO2 probability distributions calculated from the model were estimated. Although a variable bias (1.2-2.1%) in SpO2 between the pulse oximeter brands was observed, the tested pulse oximeters were both within tolerance specified by the manufacturers and matched the probability distributions from the model.The theoretical and experimental findings show that the estimation of virtual shunt is challenging with a single SpO2 measurement using pulse oximeters with tolerances of 2%. Clinical decisions must be based on an appreciation of these limitations.

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 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

mHealth enables remote health monitoring and facilitates communication and data exchange between health providers and patients, but comes at the cost of reduced data quality. Loss of quality can be multifaceted and includes an increase in missing data and a higher level of uncertainty in data compared to clinical data acquisition. This short review discusses emerging strategies to cope with challenges in keeping mHealth systems that are used by lay users reliable.

@article{
 title = {Development and Internal Validation of a Predictive Model Including Pulse Oximetry for Hospitalization of Under-Five Children in Bangladesh},
 type = {article},
 year = {2015},
 pages = {e0143213},
 volume = {10},
 websites = {http://dx.plos.org/10.1371/journal.pone.0143213},
 month = {11},
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 author = {Raihana, Shahreen and Dunsmuir, Dustin and Huda, Tanvir and Zhou, Guohai and Rahman, Qazi Sadeq-ur and Garde, Ainara and Moinuddin, Md and Karlen, Walter and Dumont, Guy A and Kissoon, Niranjan and El Arifeen, Shams and Larson, Charles and Ansermino, J. Mark},
 editor = {Chalumeau, Martin},
 doi = {10.1371/journal.pone.0143213},
 journal = {PLOS ONE},
 number = {11},
 keywords = {MHSLpublications}
}

@article{
 title = {Improving the accuracy and efficiency of respiratory rate measurements in children using mobile devices.},
 type = {article},
 year = {2014},
 pages = {e99266},
 volume = {9},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24919062,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053345&tool=pmcentrez&rendertype=abstract},
 id = {e539d539-50c7-33ec-839f-cb90fa144f45},
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 abstract = {The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.},
 bibtype = {article},
 author = {Karlen, Walter and Gan, Heng and Chiu, Michelle and Dunsmuir, Dustin and Zhou, Guohai and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1371/journal.pone.0099266},
 journal = {PLoS ONE},
 number = {6}
}

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

@inproceedings{
 title = {Sharing Vital Signs between mobile phone applications.},
 type = {inproceedings},
 year = {2014},
 pages = {3646-9},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/25570781},
 id = {2c896f34-5f39-381f-aedb-9283543bec23},
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 private_publication = {false},
 abstract = {We propose a communication library, ShareVitalSigns, for the standardized exchange of vital sign information between health applications running on mobile platforms. The library allows an application to request one or multiple vital signs from independent measurement applications on the Android OS. Compatible measurement applications are automatically detected and can be launched from within the requesting application, simplifying the work flow for the user and reducing typing errors. Data is shared between applications using intents, a passive data structure available on Android OS. The library is accompanied by a test application which serves as a demonstrator. The secure exchange of vital sign information using a standardized library like ShareVitalSigns will facilitate the integration of measurement applications into diagnostic and other high level health monitoring applications and reduce errors due to manual entry of information.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Dumont, Guy A and Scheffer, Cornie},
 doi = {10.1109/EMBC.2014.6944413},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

We propose a communication library, ShareVitalSigns, for the standardized exchange of vital sign information between health applications running on mobile platforms. The library allows an application to request one or multiple vital signs from independent measurement applications on the Android OS. Compatible measurement applications are automatically detected and can be launched from within the requesting application, simplifying the work flow for the user and reducing typing errors. Data is shared between applications using intents, a passive data structure available on Android OS. The library is accompanied by a test application which serves as a demonstrator. The secure exchange of vital sign information using a standardized library like ShareVitalSigns will facilitate the integration of measurement applications into diagnostic and other high level health monitoring applications and reduce errors due to manual entry of information.

@book{
 title = {Mobile Point-of-Care Monitors and Diagnostic Device Design},
 type = {book},
 year = {2014},
 websites = {http://www.crcpress.com/product/isbn/9781466589292},
 publisher = {CRC Press},
 city = {Boca Raton},
 series = {Devices, Circuits, and Systems},
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@inproceedings{
 title = {Sleep Stage Classification in Children Using Photoplethysmogram Pulse Rate Variability},
 type = {inproceedings},
 year = {2014},
 pages = {297-300},
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 bibtype = {inproceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Computing in Cardiology (CinC)}
}

@inproceedings{
 title = {Design of an interactive medical guideline application for community health workers.},
 type = {inproceedings},
 year = {2014},
 pages = {1366-1369},
 id = {ff4b2fa6-6f1b-3e6b-ab3f-8f0e762b6978},
 created = {2013-09-22T23:33:59.000Z},
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 citation_key = {Karlen2014k},
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 abstract = {Clinical guidelines, such as the Integrated Management of Childhood Illness (IMCI), are used worldwide to support community health workers in the assessment of severely ill children. These guidelines are distributed in paper form, complicating their use at the point-of-care. We have developed a framework for building advanced clinical guideline applications for the Android mobile phone OS. The framework transfers clinical guidelines into a flexible and interactive electronic format using an XML interpreter. The resulting application supports intuitive navigation of guidelines while assessing the patient, easy integration of patient management tools, and logging of performed assessments and treatments. The novel approach transforms clinical guidelines from a mere paper dictionary into a working tool that integrates into the daily workflow of community health workers and simplifies their task at the care and administrative levels.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Scheffer, Cornie},
 doi = {10.1109/EMBC.2014.6943853},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Clinical guidelines, such as the Integrated Management of Childhood Illness (IMCI), are used worldwide to support community health workers in the assessment of severely ill children. These guidelines are distributed in paper form, complicating their use at the point-of-care. We have developed a framework for building advanced clinical guideline applications for the Android mobile phone OS. The framework transfers clinical guidelines into a flexible and interactive electronic format using an XML interpreter. The resulting application supports intuitive navigation of guidelines while assessing the patient, easy integration of patient management tools, and logging of performed assessments and treatments. The novel approach transforms clinical guidelines from a mere paper dictionary into a working tool that integrates into the daily workflow of community health workers and simplifies their task at the care and administrative levels.

@inproceedings{
 title = {Respiratory Rate Assessment from Photoplethysmographic Imaging},
 type = {inproceedings},
 year = {2014},
 pages = {5397-400},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Chicago,IL,USA},
 id = {fc5fa8f0-0c68-3516-b9af-b3fdca4fa6bd},
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 notes = {Selected paper},
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 private_publication = {false},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Garde, Ainara and Myers, Dorothy and Scheffer, Cornie and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

@inproceedings{
 title = {Oxygen saturation resolution influences regularity measurements.},
 type = {inproceedings},
 year = {2014},
 pages = {2257-60},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/25570437},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Chicago},
 id = {1a5df28e-9de3-3816-b74f-c45d6d2d6cb6},
 created = {2014-08-27T19:17:35.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:02.590Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014c},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {The measurement of regularity in the oxygen saturation (SpO(2)) signal has been suggested for use in identifying subjects with sleep disordered breathing (SDB). Previous work has shown that children with SDB have lower SpO(2) regularity than subjects without SDB (NonSDB). Regularity was measured using non-linear methods like approximate entropy (ApEn), sample entropy (SamEn) and Lempel-Ziv (LZ) complexity. Different manufacturer's pulse oximeters provide SpO(2) at various resolutions and the effect of this resolution difference on SpO(2) regularity, has not been studied. To investigate this effect, we used the SpO(2) signal of children with and without SDB, recorded from the Phone Oximeter (0.1% resolution) and the same SpO(2) signal rounded to the nearest integer (artificial 1% resolution). To further validate the effect of rounding, we also used the SpO(2) signal (1% resolution) recorded simultaneously from polysomnography (PSG), as a control signal. We estimated SpO(2) regularity by computing the ApEn, SamEn and LZ complexity, using a 5-min sliding window and showed that different resolutions provided significantly different results. The regularity calculated using 0.1% SpO(2) resolution provided no significant differences between SDB and NonSDB. However, the artificial 1% resolution SpO(2) provided significant differences between SDB and NonSDB, showing a more random SpO(2) pattern (lower SpO(2) regularity) in SDB children, as suggested in the past. Similar results were obtained with the SpO(2) recorded from PSG (1% resolution), which further validated that this SpO(2) regularity change was due to the rounding effect. Therefore, the SpO(2) resolution has a great influence in regularity measurements like ApEn, SamEn and LZ complexity that should be considered when studying the SpO(2) pattern in children with SDB.},
 bibtype = {inproceedings},
 author = {Garde, Ainara and Karlen, Walter and Dehkordi, Parastoo and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1109/EMBC.2014.6944069},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

The measurement of regularity in the oxygen saturation (SpO(2)) signal has been suggested for use in identifying subjects with sleep disordered breathing (SDB). Previous work has shown that children with SDB have lower SpO(2) regularity than subjects without SDB (NonSDB). Regularity was measured using non-linear methods like approximate entropy (ApEn), sample entropy (SamEn) and Lempel-Ziv (LZ) complexity. Different manufacturer's pulse oximeters provide SpO(2) at various resolutions and the effect of this resolution difference on SpO(2) regularity, has not been studied. To investigate this effect, we used the SpO(2) signal of children with and without SDB, recorded from the Phone Oximeter (0.1% resolution) and the same SpO(2) signal rounded to the nearest integer (artificial 1% resolution). To further validate the effect of rounding, we also used the SpO(2) signal (1% resolution) recorded simultaneously from polysomnography (PSG), as a control signal. We estimated SpO(2) regularity by computing the ApEn, SamEn and LZ complexity, using a 5-min sliding window and showed that different resolutions provided significantly different results. The regularity calculated using 0.1% SpO(2) resolution provided no significant differences between SDB and NonSDB. However, the artificial 1% resolution SpO(2) provided significant differences between SDB and NonSDB, showing a more random SpO(2) pattern (lower SpO(2) regularity) in SDB children, as suggested in the past. Similar results were obtained with the SpO(2) recorded from PSG (1% resolution), which further validated that this SpO(2) regularity change was due to the rounding effect. Therefore, the SpO(2) resolution has a great influence in regularity measurements like ApEn, SamEn and LZ complexity that should be considered when studying the SpO(2) pattern in children with SDB.

@article{
 title = {Development of a screening tool for sleep disordered breathing in children using the phone oximeter™.},
 type = {article},
 year = {2014},
 pages = {e112959},
 volume = {9},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4234680&tool=pmcentrez&rendertype=abstract},
 id = {3c7e454c-b9f2-32ad-b462-e4b69e0b94fc},
 created = {2014-09-19T20:26:30.000Z},
 accessed = {2014-11-26},
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 last_modified = {2022-09-04T18:11:57.272Z},
 read = {true},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014b},
 notes = {IF-2018: 2.78},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,0801d9e0-d1ec-46e2-803d-c74946b43a02,7ecba661-0765-42d5-a37e-6a57b331e28c},
 private_publication = {false},
 abstract = {BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.},
 bibtype = {article},
 author = {Garde, Ainara and Dehkordi, Parastoo and Karlen, Walter and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1371/journal.pone.0112959},
 journal = {PLoS ONE},
 number = {11}
}

BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.

@inproceedings{
 title = {Detrended fluctuation analysis of photoplethysmogram pulse rate intervals in sleep disordered breathing},
 type = {inproceedings},
 year = {2014},
 pages = {323-326},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7038940},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Seattle},
 id = {fc710ddb-05cc-3253-ab53-70afb92c239a},
 created = {2014-10-07T11:32:23.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:55.619Z},
 read = {true},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dehkordi2014a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Petersen, Chris L and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1109/HIC.2014.7038940},
 booktitle = {2014 IEEE Healthcare Innovation Conference (HIC)}
}

@inproceedings{
 title = {Assessing the Quality of Manual Respiratory Rate Measurements using Mobile Devices},
 type = {inproceedings},
 year = {2014},
 keywords = {be inaccurate,implications for pneumonia diagnosis,measurement confidence,ment,mobile devices,quality assess-,respiratory rate,rr is known to,the manual assessment of,this can have severe,us-},
 pages = {1-4},
 publisher = {IET},
 city = {London, UK},
 id = {7889864c-c0ba-3e72-a508-89f9576e695f},
 created = {2014-10-06T10:58:32.000Z},
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 abstract = {We have designed a mobile device application (RRate), to provide an efficient measurement of respiratory rate with clinically acceptable accuracy. The method is based on analysis of multiple consecutive breath intervals. We investigated in this study the difference in measurement variability between breaths as a representative of recording quality. Respiratory rate of 322 children aged 0 - 12 years at a Ugandan rural health centre were recorded using the RRate mobile application, and compared to respiratory rate recordings obtained from 22 volunteers using the RRate application while observing 10 videos of children breathing in a lab setting. The variability of the recorded breaths (confidence) of both groups follow a similar Weibull distribution. However, we observed a trend towards higher variability in the data obtained in the field (median 89.7% confidence) compared to the data obtained in the laboratory setting (median 92.6% confidence). This suggests that it is more difficult to obtain consistent measurements when assessing patients in a clinical setting, and therefore the confidence in the measured respiratory rate is reduced. The mobile device application provided a respiratory rate value up to 6 times faster than the current practice of one minute counting. The measure of variability between individual measured breaths provided a powerful way to display confidence in a measurement.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Wiens, Matthew O and Gan, Heng and Dunsmuir, Dustin and Chiu, Michelle and Dumont, Guy A and Ansermino, J Mark},
 booktitle = {Appropriate Healthcare Technologies for Low Resource Settings (AHT 2014)}
}

We have designed a mobile device application (RRate), to provide an efficient measurement of respiratory rate with clinically acceptable accuracy. The method is based on analysis of multiple consecutive breath intervals. We investigated in this study the difference in measurement variability between breaths as a representative of recording quality. Respiratory rate of 322 children aged 0 - 12 years at a Ugandan rural health centre were recorded using the RRate mobile application, and compared to respiratory rate recordings obtained from 22 volunteers using the RRate application while observing 10 videos of children breathing in a lab setting. The variability of the recorded breaths (confidence) of both groups follow a similar Weibull distribution. However, we observed a trend towards higher variability in the data obtained in the field (median 89.7% confidence) compared to the data obtained in the laboratory setting (median 92.6% confidence). This suggests that it is more difficult to obtain consistent measurements when assessing patients in a clinical setting, and therefore the confidence in the measured respiratory rate is reduced. The mobile device application provided a respiratory rate value up to 6 times faster than the current practice of one minute counting. The measure of variability between individual measured breaths provided a powerful way to display confidence in a measurement.

@article{
 title = {Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram.},
 type = {article},
 year = {2014},
 pages = {e86427},
 volume = {9},
 websites = {http://dx.plos.org/10.1371/journal.pone.0086427,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3899260&tool=pmcentrez&rendertype=abstract},
 day = {22},
 id = {9cde86ba-981b-359d-b4c6-47dbffabce4f},
 created = {2014-02-02T14:15:44.000Z},
 accessed = {2014-01-24},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:08.497Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014},
 notes = {IF-2018: 2.78},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.},
 bibtype = {article},
 author = {Garde, Ainara and Karlen, Walter and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1371/journal.pone.0086427},
 journal = {PLoS ONE},
 number = {1}
}

The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

@article{
 title = {Where do pulse oximeter probes break?},
 type = {article},
 year = {2014},
 keywords = {dw and jl participated,gvdm,gvdm collected data for,in the design of,jh,low-resource settings,performed the statistical,pulse oximetry á probes,sc,the study,this study and sc,á probe wiring á},
 pages = {309-14},
 volume = {28},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24420339},
 id = {78b66762-19c6-3c26-a3d9-30e5a082ce50},
 created = {2015-08-18T06:49:00.000Z},
 accessed = {2014-05-19},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:06.298Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Crede2014},
 notes = {IF-2018: 2.18},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0f17b6d7-75e3-4f99-a546-6054e4a41808,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,7b5629e9-9bc6-4936-8bd2-19ec35e76b1b},
 private_publication = {false},
 abstract = {Pulse oximetry, a non-invasive method for accurate assessment of blood oxygen saturation (SPO2), is an important monitoring tool in health care facilities. However, it is often not available in many low-resource settings, due to expense, overly sophisticated design, a lack of organised procurement systems and inadequate medical device management and maintenance structures. Furthermore medical devices are often fragile and not designed to withstand the conditions of low-resource settings. In order to design a probe, better suited to the needs of health care facilities in low-resource settings this study aimed to document the site and nature of pulse oximeter probe breakages in a range of different probe designs in a low to middle income country. A retrospective review of job cards relating to the assessment and repair of damaged or faulty pulse oximeter probes was conducted at a medical device repair company based in Cape Town, South Africa, specializing in pulse oximeter probe repairs. 1,840 job cards relating to the assessment and repair of pulse oximeter probes were reviewed. 60.2 % of probes sent for assessment were finger-clip probes. For all probes, excluding the neonatal wrap probes, the most common point of failure was the probe wiring (>50 %). The neonatal wrap most commonly failed at the strap (51.5 %). The total cost for quoting on the broken pulse oximeter probes and for the subsequent repair of devices, excluding replacement components, amounted to an estimated ZAR 738,810 (USD $98,508). Improving the probe wiring would increase the life span of pulse oximeter probes. Increasing the life span of probes will make pulse oximetry more affordable and accessible. This is of high priority in low-resource settings where frequent repair or replacement of probes is unaffordable or impossible.},
 bibtype = {article},
 author = {Crede, Sarah and Van der Merwe, G and Hutchinson, J and Woods, David and Karlen, Walter and Lawn, Joy},
 doi = {10.1007/s10877-013-9538-2},
 journal = {Journal of clinical monitoring and computing},
 number = {3}
}

Pulse oximetry, a non-invasive method for accurate assessment of blood oxygen saturation (SPO2), is an important monitoring tool in health care facilities. However, it is often not available in many low-resource settings, due to expense, overly sophisticated design, a lack of organised procurement systems and inadequate medical device management and maintenance structures. Furthermore medical devices are often fragile and not designed to withstand the conditions of low-resource settings. In order to design a probe, better suited to the needs of health care facilities in low-resource settings this study aimed to document the site and nature of pulse oximeter probe breakages in a range of different probe designs in a low to middle income country. A retrospective review of job cards relating to the assessment and repair of damaged or faulty pulse oximeter probes was conducted at a medical device repair company based in Cape Town, South Africa, specializing in pulse oximeter probe repairs. 1,840 job cards relating to the assessment and repair of pulse oximeter probes were reviewed. 60.2 % of probes sent for assessment were finger-clip probes. For all probes, excluding the neonatal wrap probes, the most common point of failure was the probe wiring (>50 %). The neonatal wrap most commonly failed at the strap (51.5 %). The total cost for quoting on the broken pulse oximeter probes and for the subsequent repair of devices, excluding replacement components, amounted to an estimated ZAR 738,810 (USD $98,508). Improving the probe wiring would increase the life span of pulse oximeter probes. Increasing the life span of probes will make pulse oximetry more affordable and accessible. This is of high priority in low-resource settings where frequent repair or replacement of probes is unaffordable or impossible.

@inproceedings{
 title = {Design of an interactive medical guideline application for community health workers.},
 type = {inproceedings},
 year = {2014},
 pages = {1366-1369},
 id = {7116319a-d543-3e0a-9539-7fa5cf9dbb3c},
 created = {2021-11-19T07:53:04.648Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:56.181Z},
 read = {false},
 starred = {false},
 authored = {true},
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 hidden = {false},
 citation_key = {Karlen2014k},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,20f4663c-5015-44ee-8c35-394df252dd32},
 private_publication = {false},
 abstract = {Clinical guidelines, such as the Integrated Management of Childhood Illness (IMCI), are used worldwide to support community health workers in the assessment of severely ill children. These guidelines are distributed in paper form, complicating their use at the point-of-care. We have developed a framework for building advanced clinical guideline applications for the Android mobile phone OS. The framework transfers clinical guidelines into a flexible and interactive electronic format using an XML interpreter. The resulting application supports intuitive navigation of guidelines while assessing the patient, easy integration of patient management tools, and logging of performed assessments and treatments. The novel approach transforms clinical guidelines from a mere paper dictionary into a working tool that integrates into the daily workflow of community health workers and simplifies their task at the care and administrative levels.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Scheffer, Cornie},
 doi = {10.1109/EMBC.2014.6943853},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

Clinical guidelines, such as the Integrated Management of Childhood Illness (IMCI), are used worldwide to support community health workers in the assessment of severely ill children. These guidelines are distributed in paper form, complicating their use at the point-of-care. We have developed a framework for building advanced clinical guideline applications for the Android mobile phone OS. The framework transfers clinical guidelines into a flexible and interactive electronic format using an XML interpreter. The resulting application supports intuitive navigation of guidelines while assessing the patient, easy integration of patient management tools, and logging of performed assessments and treatments. The novel approach transforms clinical guidelines from a mere paper dictionary into a working tool that integrates into the daily workflow of community health workers and simplifies their task at the care and administrative levels.

@article{
 title = {Improving the accuracy and efficiency of respiratory rate measurements in children using mobile devices.},
 type = {article},
 year = {2014},
 pages = {e99266},
 volume = {9},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24919062,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053345&tool=pmcentrez&rendertype=abstract},
 month = {1},
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 created = {2021-11-19T08:01:01.712Z},
 accessed = {2014-08-07},
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 abstract = {The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.},
 bibtype = {article},
 author = {Karlen, Walter and Gan, Heng and Chiu, Michelle and Dunsmuir, Dustin and Zhou, Guohai and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1371/journal.pone.0099266},
 journal = {PLoS ONE},
 number = {6}
}

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

@inproceedings{
 title = {Respiratory Rate Assessment from Photoplethysmographic Imaging},
 type = {inproceedings},
 year = {2014},
 pages = {5397-400},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Chicago,IL,USA},
 id = {4262e6ed-3ef4-3f65-bd7e-b892d4a1bd14},
 created = {2021-11-19T08:01:02.178Z},
 file_attached = {false},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:22.026Z},
 read = {false},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014c},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,212f55fa-55d4-4143-9099-99fbbd76f9fa},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Garde, Ainara and Myers, Dorothy and Scheffer, Cornie and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

@article{
 title = {Improving the accuracy and efficiency of respiratory rate measurements in children using mobile devices.},
 type = {article},
 year = {2014},
 pages = {e99266},
 volume = {9},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24919062,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053345&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {85b8529a-fe50-3e24-836f-838048f2b379},
 created = {2021-11-19T08:07:17.650Z},
 accessed = {2014-08-07},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:14.514Z},
 read = {false},
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 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,c861438a-f137-4be3-8c86-0f244c523c17},
 private_publication = {false},
 abstract = {The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.},
 bibtype = {article},
 author = {Karlen, Walter and Gan, Heng and Chiu, Michelle and Dunsmuir, Dustin and Zhou, Guohai and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1371/journal.pone.0099266},
 journal = {PLoS ONE},
 number = {6}
}

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

@article{
 title = {Development of a screening tool for sleep disordered breathing in children using the phone oximeter™.},
 type = {article},
 year = {2014},
 pages = {e112959},
 volume = {9},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4234680&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {94f973f8-5006-33f7-9622-0ddeaa1b3389},
 created = {2022-06-18T08:43:44.135Z},
 accessed = {2014-11-26},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:11.354Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014b},
 folder_uuids = {afa125a0-d96d-43e8-8b00-67710f3acd9f,f1f67efc-95a7-4f1a-b181-c3670c667a34},
 private_publication = {false},
 abstract = {BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.},
 bibtype = {article},
 author = {Garde, Ainara and Dehkordi, Parastoo and Karlen, Walter and Wensley, David and Ansermino, J. Mark and Dumont, Guy A.},
 doi = {10.1371/journal.pone.0112959},
 journal = {PLoS ONE},
 number = {11},
 keywords = {melatosense}
}

BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.

@inproceedings{
 title = {Assessing the Quality of Manual Respiratory Rate Measurements using Mobile Devices},
 type = {inproceedings},
 year = {2014},
 keywords = {Quality in mHealth},
 pages = {in press},
 publisher = {IET},
 city = {London, UK},
 id = {69157cfa-3034-3949-9fea-9cbd3655ab87},
 created = {2022-06-18T09:48:07.139Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:12.875Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014d},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,bd798101-5e2f-49c6-84e3-81f386d0742a},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Karlen, W and Wiens, M O and Gan, H and Dunsmuir, D and Chiu, M and Dumont, G A and Ansermino, J M},
 booktitle = {Appropriate Healthcare Technologies AHT'14}
}

@article{
 title = {Improving the accuracy and efficiency of respiratory rate measurements in children using mobile devices.},
 type = {article},
 year = {2014},
 pages = {e99266},
 volume = {9},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24919062,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053345&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {88fca902-c2a0-3203-b204-7a3844d91e97},
 created = {2022-06-18T09:48:17.098Z},
 accessed = {2014-08-07},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:21.766Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,bd798101-5e2f-49c6-84e3-81f386d0742a},
 private_publication = {false},
 abstract = {The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.},
 bibtype = {article},
 author = {Karlen, Walter and Gan, Heng and Chiu, Michelle and Dunsmuir, Dustin and Zhou, Guohai and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1371/journal.pone.0099266},
 journal = {PLoS ONE},
 number = {6},
 keywords = {Quality in mHealth}
}

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

@article{
 title = {Where do pulse oximeter probes break?},
 type = {article},
 year = {2014},
 keywords = {Quality in mHealth},
 pages = {309-14},
 volume = {28},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24420339},
 month = {12},
 day = {6},
 id = {dce107bf-4da3-371a-baf1-a63cd08dad6f},
 created = {2022-06-18T09:48:18.825Z},
 accessed = {2014-05-19},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:13.318Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Crede2014},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,bd798101-5e2f-49c6-84e3-81f386d0742a},
 private_publication = {false},
 abstract = {Pulse oximetry, a non-invasive method for accurate assessment of blood oxygen saturation (SPO2), is an important monitoring tool in health care facilities. However, it is often not available in many low-resource settings, due to expense, overly sophisticated design, a lack of organised procurement systems and inadequate medical device management and maintenance structures. Furthermore medical devices are often fragile and not designed to withstand the conditions of low-resource settings. In order to design a probe, better suited to the needs of health care facilities in low-resource settings this study aimed to document the site and nature of pulse oximeter probe breakages in a range of different probe designs in a low to middle income country. A retrospective review of job cards relating to the assessment and repair of damaged or faulty pulse oximeter probes was conducted at a medical device repair company based in Cape Town, South Africa, specializing in pulse oximeter probe repairs. 1,840 job cards relating to the assessment and repair of pulse oximeter probes were reviewed. 60.2 % of probes sent for assessment were finger-clip probes. For all probes, excluding the neonatal wrap probes, the most common point of failure was the probe wiring (>50 %). The neonatal wrap most commonly failed at the strap (51.5 %). The total cost for quoting on the broken pulse oximeter probes and for the subsequent repair of devices, excluding replacement components, amounted to an estimated ZAR 738,810 (USD $98,508). Improving the probe wiring would increase the life span of pulse oximeter probes. Increasing the life span of probes will make pulse oximetry more affordable and accessible. This is of high priority in low-resource settings where frequent repair or replacement of probes is unaffordable or impossible.},
 bibtype = {article},
 author = {Crede, S and Van der Merwe, G and Hutchinson, J and Woods, D and Karlen, W and Lawn, J},
 doi = {10.1007/s10877-013-9538-2},
 journal = {Journal of clinical monitoring and computing},
 number = {3}
}

Pulse oximetry, a non-invasive method for accurate assessment of blood oxygen saturation (SPO2), is an important monitoring tool in health care facilities. However, it is often not available in many low-resource settings, due to expense, overly sophisticated design, a lack of organised procurement systems and inadequate medical device management and maintenance structures. Furthermore medical devices are often fragile and not designed to withstand the conditions of low-resource settings. In order to design a probe, better suited to the needs of health care facilities in low-resource settings this study aimed to document the site and nature of pulse oximeter probe breakages in a range of different probe designs in a low to middle income country. A retrospective review of job cards relating to the assessment and repair of damaged or faulty pulse oximeter probes was conducted at a medical device repair company based in Cape Town, South Africa, specializing in pulse oximeter probe repairs. 1,840 job cards relating to the assessment and repair of pulse oximeter probes were reviewed. 60.2 % of probes sent for assessment were finger-clip probes. For all probes, excluding the neonatal wrap probes, the most common point of failure was the probe wiring (>50 %). The neonatal wrap most commonly failed at the strap (51.5 %). The total cost for quoting on the broken pulse oximeter probes and for the subsequent repair of devices, excluding replacement components, amounted to an estimated ZAR 738,810 (USD $98,508). Improving the probe wiring would increase the life span of pulse oximeter probes. Increasing the life span of probes will make pulse oximetry more affordable and accessible. This is of high priority in low-resource settings where frequent repair or replacement of probes is unaffordable or impossible.

@inbook{
 type = {inbook},
 year = {2013},
 pages = {31-42},
 websites = {https://link.springer.com/chapter/10.1007/978-3-642-29752-6_3},
 publisher = {Springer-Verlag},
 city = {Berlin Heidelberg},
 id = {26e4c9f5-96aa-310f-9d94-716ea86ddfa3},
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 last_modified = {2022-09-04T18:11:58.564Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012a},
 folder_uuids = {0560f016-99a0-4947-b8d2-af51a6797449,f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,4c3d8d03-453f-4372-a5a6-d6fc6eb5ad4a,4afa922c-d8d6-102e-ac9a-0024e85ead87,0d3ec8ac-06e7-459a-9eec-b845649cdc31},
 private_publication = {false},
 bibtype = {inbook},
 author = {Karlen, Walter and Petersen, Chris and Gow, Jennifer and Ansermino, J Mark and Dumont, Guy A},
 editor = {Fred, A and Filipe, J and Gamboa, H},
 doi = {10.1007/978-3-642-29752-6_3},
 chapter = {Photoplethysmogram Processing Using An Adaptive Single Frequency Phase Vocoder Algorithm},
 title = {BIOSTEC 2011, CCIS 273}
}

@article{
 title = {Monitoring nociception during general anesthesia with cardiorespiratory coherence.},
 type = {article},
 year = {2013},
 keywords = {antinociception á analgesia,arrhythmia  nociception,cardiorespiratory coherence  heart rate,variability  respiratory sinus},
 pages = {551-60},
 volume = {27},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23568315},
 day = {9},
 id = {f1198b30-d988-3c97-bae1-e20d0b6cefa3},
 created = {2013-04-12T14:12:01.000Z},
 accessed = {2013-09-26},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:54.238Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brouse2013a},
 notes = {IF-2018: 2.18},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.},
 bibtype = {article},
 author = {Brouse, Christopher J and Karlen, Walter and Dumont, Guy A and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Ansermino, J Mark},
 doi = {10.1007/s10877-013-9463-4},
 journal = {Journal of clinical monitoring and computing},
 number = {5}
}

A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.

@inproceedings{
 title = {Recognition of correct finger placement for photoplethysmographic imaging},
 type = {inproceedings},
 year = {2013},
 keywords = {Consumer health,Emerging IT for efficient/low-cost healthcare deli,Mobile health,User experience,Wireless/ubiquitous technologies and systems},
 pages = {7480-3},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24111475},
 city = {Osaka},
 id = {cc709576-791c-3538-b46e-63e7a3ad1a15},
 created = {2013-01-17T07:41:46.000Z},
 accessed = {2013-10-23},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:57.818Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013},
 folder_uuids = {439018bc-93d2-4d1e-9f95-7d8541a20cf1,f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,4afa922c-d8d6-102e-ac9a-0024e85ead87,6502c3f2-6e56-45fe-98d8-9274a086a27f,d9198259-8733-497d-ab87-d2a9518e0d30,283c5c0f-23cb-4595-855e-08632d168559},
 private_publication = {false},
 abstract = {In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Lim, Joanne and Ansermino, J Mark and Dumont, Guy A and Scheffer, Cornie},
 doi = {10.1109/EMBC.2013.6611288},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.

@inproceedings{
 title = {Detection of the optimal region of interest for camera oximetry},
 type = {inproceedings},
 year = {2013},
 keywords = {Consumer health,Emerging IT for efficient/low-cost healthcare deli,Mobile health},
 pages = {2263-6},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24110175},
 city = {Osaka},
 id = {da143f54-9182-3f49-8c60-d11622398411},
 created = {2013-03-25T11:45:13.000Z},
 accessed = {2013-10-23},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:24.542Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013f},
 folder_uuids = {439018bc-93d2-4d1e-9f95-7d8541a20cf1,f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {The estimation of heart rate and blood oxygen saturation with an imaging array on a mobile phone (camera oximetry) has great potential for mobile health applications as no additional hardware other than a camera and LED flash enabled phone are required. However, this approach is challenging as the configuration of the camera can negatively influence the estimation quality. Further, the number of photons recorded with the photo detector is largely dependent on the optical path length, resulting in a non-homogeneous image. In this paper we describe a novel method to automatically detect the optimal region of interest (ROI) for the captured image to extract a pulse waveform. We also present a study to select the optimal camera settings, notably the white balance. The experiments show that the incandescent white balance mode is the preferable setting for camera oximetry applications on the tested mobile phone (Samsung Galaxy Ace). Also, the ROI algorithm successfully identifies the frame regions which provide waveforms with the largest amplitudes.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Ansermino, J Mark and Dumont, Guy A and Scheffer, Cornie},
 doi = {10.1109/EMBC.2013.6609988},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

The estimation of heart rate and blood oxygen saturation with an imaging array on a mobile phone (camera oximetry) has great potential for mobile health applications as no additional hardware other than a camera and LED flash enabled phone are required. However, this approach is challenging as the configuration of the camera can negatively influence the estimation quality. Further, the number of photons recorded with the photo detector is largely dependent on the optical path length, resulting in a non-homogeneous image. In this paper we describe a novel method to automatically detect the optimal region of interest (ROI) for the captured image to extract a pulse waveform. We also present a study to select the optimal camera settings, notably the white balance. The experiments show that the incandescent white balance mode is the preferable setting for camera oximetry applications on the tested mobile phone (Samsung Galaxy Ace). Also, the ROI algorithm successfully identifies the frame regions which provide waveforms with the largest amplitudes.

@inproceedings{
 title = {Pulse rate variability compared with heart rate variability in children with and without sleep disordered breathing},
 type = {inproceedings},
 year = {2013},
 pages = {6563-6},
 id = {c17c8db6-bb11-3421-a340-541f721dd7b6},
 created = {2013-05-01T19:29:39.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:02.805Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dehkordi2013},
 folder_uuids = {439018bc-93d2-4d1e-9f95-7d8541a20cf1,f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

@inproceedings{
 title = {Analysis of Oxygen Saturation in Children with Obstructive Sleep Apnea Using the Phone-Oximeter},
 type = {inproceedings},
 year = {2013},
 pages = {2531-4},
 volume = {4},
 city = {Osaka},
 id = {ef808537-13e2-3be5-b541-7243a9c26647},
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 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:58.333Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2013},
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 private_publication = {false},
 bibtype = {inproceedings},
 author = {Garde, Ainara and Karlen, Walter and Dehkordi, Parastoo and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

@inproceedings{
 title = {Pulse Rate Variability in Children with Disordered Breathing During Different Sleep Stages},
 type = {inproceedings},
 year = {2013},
 pages = {1015 - 8},
 volume = {40},
 websites = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6713552},
 publisher = {IEEE},
 city = {Zaragoza},
 id = {83511df5-eb6b-333d-a893-d13d70497701},
 created = {2013-09-26T15:02:49.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:22.268Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dehkordi2013a},
 notes = {Best Poster Award},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Computing in Cardiology (CinC)}
}

@inproceedings{
 title = {Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram},
 type = {inproceedings},
 year = {2013},
 pages = {799-802},
 volume = {40},
 websites = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6713498,http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6713498},
 publisher = {IEEE},
 city = {Zaragoza},
 id = {57e1935d-7841-3f3e-99a3-6935dbed1b54},
 created = {2015-09-30T14:37:34.000Z},
 accessed = {2014-11-12},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:10.925Z},
 read = {true},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2013a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,4afa922c-d8d6-102e-ac9a-0024e85ead87,0d3ec8ac-06e7-459a-9eec-b845649cdc31,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Garde, Ainara and Karlen, Walter and Dehkordi, Parastoo and Ansermino, J Mark and Dumont, Guy A},
 editor = {Murray, Alan},
 booktitle = {Computing in Cardiology (CinC)}
}

@article{
 title = {Multiparameter respiratory rate estimation from the photoplethysmogram.},
 type = {article},
 year = {2013},
 keywords = {Adolescent,Adult,Aged,Algorithms,Automated,Automated: methods,Child,Computer Systems,Computer-Assisted,Computer-Assisted: methods,Data Interpretation,Diagnosis,Fourier Analysis,Humans,Infant,Middle Aged,Pattern Recognition,Photoplethysmography,Photoplethysmography: methods,Preschool,Reproducibility of Results,Respiratory Rate,Respiratory Rate: physiology,Sensitivity and Specificity,Statistical,Young Adult,photoplethysmogram,pulse oximeter,respiratory rate},
 pages = {1946-53},
 volume = {60},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23399950,https://www.researchgate.net/publication/235521997_Multiparameter_Respiratory_Rate_Estimation_From_the_Photoplethysmogram},
 id = {23ae7710-6df8-3f97-85b4-6cfe0f2f8192},
 created = {2017-08-28T07:29:49.619Z},
 accessed = {2013-06-03},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:25.199Z},
 read = {true},
 starred = {true},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013a},
 notes = {IF-2018: 4.49},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,9aa961a4-04a6-4a65-b081-d534c7a81a35,7b0fcef9-e247-4690-b778-5ca357548066,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,0d3ec8ac-06e7-459a-9eec-b845649cdc31,1d5c9041-7902-453a-83e6-6cd19528c777,7ecba661-0765-42d5-a37e-6a57b331e28c,8bf4ed74-a467-49ad-8f7c-6ab98c981268},
 private_publication = {false},
 abstract = {We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.},
 bibtype = {article},
 author = {Karlen, Walter and Raman, Srinivas and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1109/TBME.2013.2246160},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {7}
}

We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.

@inproceedings{
 title = {Detection of the optimal region of interest for camera oximetry},
 type = {inproceedings},
 year = {2013},
 keywords = {Consumer health,Emerging IT for efficient/low-cost healthcare deli,Mobile health},
 pages = {2263-6},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24110175},
 month = {7},
 city = {Osaka},
 id = {dc31e7c3-2b73-3184-b774-20afacde5063},
 created = {2021-11-19T08:04:09.999Z},
 accessed = {2013-10-23},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:08.266Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013f},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,d25bf112-04e3-4db2-9a05-346bfdcbbda2},
 private_publication = {false},
 abstract = {The estimation of heart rate and blood oxygen saturation with an imaging array on a mobile phone (camera oximetry) has great potential for mobile health applications as no additional hardware other than a camera and LED flash enabled phone are required. However, this approach is challenging as the configuration of the camera can negatively influence the estimation quality. Further, the number of photons recorded with the photo detector is largely dependent on the optical path length, resulting in a non-homogeneous image. In this paper we describe a novel method to automatically detect the optimal region of interest (ROI) for the captured image to extract a pulse waveform. We also present a study to select the optimal camera settings, notably the white balance. The experiments show that the incandescent white balance mode is the preferable setting for camera oximetry applications on the tested mobile phone (Samsung Galaxy Ace). Also, the ROI algorithm successfully identifies the frame regions which provide waveforms with the largest amplitudes.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Ansermino, J Mark and Dumont, Guy A and Scheffer, Cornie},
 doi = {10.1109/EMBC.2013.6609988},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

The estimation of heart rate and blood oxygen saturation with an imaging array on a mobile phone (camera oximetry) has great potential for mobile health applications as no additional hardware other than a camera and LED flash enabled phone are required. However, this approach is challenging as the configuration of the camera can negatively influence the estimation quality. Further, the number of photons recorded with the photo detector is largely dependent on the optical path length, resulting in a non-homogeneous image. In this paper we describe a novel method to automatically detect the optimal region of interest (ROI) for the captured image to extract a pulse waveform. We also present a study to select the optimal camera settings, notably the white balance. The experiments show that the incandescent white balance mode is the preferable setting for camera oximetry applications on the tested mobile phone (Samsung Galaxy Ace). Also, the ROI algorithm successfully identifies the frame regions which provide waveforms with the largest amplitudes.

@inproceedings{
 title = {The Phone Oximeter for Mobile Spot-Check},
 type = {inproceedings},
 year = {2012},
 pages = {S21},
 volume = {115},
 issue = {2 Suppl},
 publisher = {Anesthesia and analgesia},
 city = {West Palm Beach},
 id = {5444cfe8-4e52-3365-a0dc-d9ff36053f50},
 created = {2012-01-22T12:53:03.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:21.251Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dunsmuir2012a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dunsmuir, Dustin and Petersen, Chris and Karlen, Walter and Lim, Joanne and Dumont, Guy A and Ansermino, J Mark},
 booktitle = {Abstracts of the 2012 Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

@article{
 title = {Usability testing of a prototype Phone Oximeter with healthcare providers in high- and low-medical resource environments},
 type = {article},
 year = {2012},
 pages = {957-67},
 volume = {67},
 websites = {http://doi.wiley.com/10.1111/j.1365-2044.2012.07196.x},
 day = {2},
 id = {79b0a170-90c5-368e-9fb4-463db36f6302},
 created = {2011-05-12T18:45:03.000Z},
 accessed = {2012-08-07},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:06.533Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Hudson2012},
 notes = {IF-2018: 5.88},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,633ed26f-2916-4068-84dc-6db4ea545a28,ca44f58c-c969-466a-8b92-fcbba0899260},
 private_publication = {false},
 abstract = {To increase the use of pulse oximetry by capitalise on the wide availability of mobile phones, we have designed, developed and evaluated a prototype pulse oximeter interfaced to a mobile phone. Usability of this Phone Oximeter was tested as part of a rapid prototyping process. Phase 1 of the study (20 subjects) was performed in Canada. Users performed 23 tasks, while thinking aloud. Time for completion of tasks and analysis of user response to a mobile phone usability questionnaire were used to evaluate usability. Five interface improvements were made to the prototype before evaluation in Phase 2 (15 subjects) in Uganda. The lack of previous pulse oximetry experience and mobile phone use increased median (IQR [range]) time taken to perform tasks from 219 (160–247 [118–274]) s in Phase 1 to 228 (151–501 [111–2661]) s in Phase 2. User feedback was positive and overall usability high (Phase 1 – 82%, Phase 2 – 78%).},
 bibtype = {article},
 author = {Hudson, Jacqueline and Nguku, S. M. and Sleiman, Jules and Karlen, Walter and Dumont, Guy A and Petersen, Chris and Warriner, C. B. and Ansermino, J Mark},
 doi = {10.1111/j.1365-2044.2012.07196.x},
 journal = {Anaesthesia},
 number = {9}
}

To increase the use of pulse oximetry by capitalise on the wide availability of mobile phones, we have designed, developed and evaluated a prototype pulse oximeter interfaced to a mobile phone. Usability of this Phone Oximeter was tested as part of a rapid prototyping process. Phase 1 of the study (20 subjects) was performed in Canada. Users performed 23 tasks, while thinking aloud. Time for completion of tasks and analysis of user response to a mobile phone usability questionnaire were used to evaluate usability. Five interface improvements were made to the prototype before evaluation in Phase 2 (15 subjects) in Uganda. The lack of previous pulse oximetry experience and mobile phone use increased median (IQR [range]) time taken to perform tasks from 219 (160–247 [118–274]) s in Phase 1 to 228 (151–501 [111–2661]) s in Phase 2. User feedback was positive and overall usability high (Phase 1 – 82%, Phase 2 – 78%).

@inproceedings{
 title = {Design challenges for camera oximetry on a mobile phone},
 type = {inproceedings},
 year = {2012},
 keywords = {Biosensing Techniques,Biosensing Techniques: methods,Cellular Phone,Humans,Oximetry,Oximetry: methods,Pulse},
 pages = {2448-51},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23366420},
 month = {1},
 id = {82f4ed12-9531-3ebd-85e8-210a41d8b7e3},
 created = {2012-03-28T10:36:03.000Z},
 accessed = {2014-06-18},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:10.021Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012b},
 folder_uuids = {a13e6a31-7386-4390-be02-764b33c9bf9b,f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,0c88f6e0-ac5c-4be2-998a-77e6b654d0f8,900e12a5-90cb-4158-81b8-f5db7487742b,4afa922c-d8d6-102e-ac9a-0024e85ead87,edc949fa-693f-41b5-992d-487afe67fe70,6474b937-06bf-4f1e-b2ea-031526d25d55,633ed26f-2916-4068-84dc-6db4ea545a28,ca44f58c-c969-466a-8b92-fcbba0899260,6502c3f2-6e56-45fe-98d8-9274a086a27f,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Lim, Joanne and Ansermino, J Mark and Dumont, Guy and Scheffer, Cornie},
 doi = {10.1109/EMBC.2012.6346459},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.

@article{
 title = {Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation.},
 type = {article},
 year = {2012},
 keywords = {oximeter,photoplethysmogram signal quality estimation,photoplethysmography,pulse,repeated gaussian filters,segmentation,signal quality index},
 pages = {1617-29},
 volume = {33},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22986287},
 month = {9},
 day = {18},
 id = {2ac56741-78d3-3a06-8128-29971a2cd8d1},
 created = {2012-05-19T16:46:01.000Z},
 accessed = {2012-09-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:23.675Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012c},
 notes = {IF-2018: 2.25},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0c88f6e0-ac5c-4be2-998a-77e6b654d0f8,9aa961a4-04a6-4a65-b081-d534c7a81a35,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,89326dc1-c9fe-40ed-b9af-f0b50fbe810d,0d3ec8ac-06e7-459a-9eec-b845649cdc31,6502c3f2-6e56-45fe-98d8-9274a086a27f,283c5c0f-23cb-4595-855e-08632d168559},
 private_publication = {false},
 abstract = {Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.},
 bibtype = {article},
 author = {Karlen, Walter and Kobayashi, Koji and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1088/0967-3334/33/10/1617},
 journal = {Physiological Measurement},
 number = {10}
}

Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.

@inproceedings{
 title = {Real-time cardiorespiratory coherence detects antinociception during general anesthesia.},
 type = {inproceedings},
 year = {2012},
 pages = {3813-6},
 volume = {2012},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23366759},
 month = {1},
 id = {9002a0ff-9693-35e1-bce6-5ff480fc017c},
 created = {2013-03-25T14:51:03.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:20.330Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brouse2012b},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,e494fae8-b6c3-41be-99e2-96af0155546c,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.},
 bibtype = {inproceedings},
 author = {Brouse, Christopher J and Karlen, Walter and Dumont, Guy A and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Ansermino, J Mark},
 doi = {10.1109/EMBC.2012.6346798},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.

@inproceedings{
 title = {Measuring Adequacy of Analgesia with Cardiorespiratory Coherence},
 type = {inproceedings},
 year = {2012},
 keywords = {Anesthesia,Anesthesiology,Humans,Medical Laboratory Science},
 pages = {S8},
 volume = {115},
 issue = {2 Suppl},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22826529},
 publisher = {Anesthesia and analgesia},
 city = {West Palm Beach},
 id = {9d0b9eb2-3951-335b-bba9-517459c99c08},
 created = {2012-01-22T08:28:58.000Z},
 accessed = {2013-09-18},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:15.612Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brouse2012c},
 notes = {Best Clinical Application of Technology Award},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Brouse, Christopher J and Karlen, Walter and Dumont, Guy A and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Ansermino, J Mark},
 doi = {10.1213/01.ane.0000418552.16222.39},
 booktitle = {Abstracts of the 2012 Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

@inproceedings{
 title = {A Data Fusion Approach for RR estimation from PPG (STA Engineering Challenge)},
 type = {inproceedings},
 year = {2012},
 volume = {31},
 issue = {3},
 city = {West Palm Beach},
 id = {91b40cd5-a8f2-3ac4-951f-730dedc1c9d7},
 created = {2012-01-22T08:32:29.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:09.334Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Raman2012},
 notes = {1st place STA Student Engineering Challenge},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Raman, Srinivas and Brouse, Christopher J and Karlen, Walter and Dumont, Guy A and Ansermino, J Mark},
 booktitle = {Proceedings of the 2012 Society for Technology in Anesthesia Annual Meeting}
}

@inproceedings{
 title = {Adaptive pulse segmentation and artifact detection in photoplethysmography for mobile applications},
 type = {inproceedings},
 year = {2012},
 keywords = {Adaptive filtering,Biomedical signal classification,Signals and systems},
 pages = {3131-3134},
 websites = {http://ieeexplore.ieee.org/document/6346628/},
 month = {8},
 publisher = {IEEE},
 city = {San Diego},
 id = {75cc4b1e-4557-35f5-940e-22d00aed61a4},
 created = {2012-04-01T20:55:06.000Z},
 file_attached = {true},
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 last_modified = {2022-09-04T18:12:23.925Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012},
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 private_publication = {false},
 abstract = {Abstract—Pulse oximeters non-invasively measure heart rate and oxygen saturation and have great potential for predicting critical illness. The photoplethysmogram (PPG) recorded from pulse oximeters is often corrupted with artifacts that can render the vital signs obtained inaccurate. We present a novel real-time algorithm for segmentation of the PPG into pulses and classification of artifacts. The line segmentation algorithm operates in the time domain and extracts morphological fea- tures of the PPG. These features are characterized as lines which are classified as pulses and artifacts using adaptive thresholds. The algorithm was evaluated using the Complex System Laboratory (CSL) Benchmark data set. A sensitivity of 98.93% and positive predictive value of 96.68% have been obtained, which compares very favorably with the benchmark algorithm. The novel algorithm is currently being implemented into mobile phone pulse oximeters},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Ansermino, J Mark and Dumont, G.},
 doi = {10.1109/EMBC.2012.6346628},
 booktitle = {2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

Abstract—Pulse oximeters non-invasively measure heart rate and oxygen saturation and have great potential for predicting critical illness. The photoplethysmogram (PPG) recorded from pulse oximeters is often corrupted with artifacts that can render the vital signs obtained inaccurate. We present a novel real-time algorithm for segmentation of the PPG into pulses and classification of artifacts. The line segmentation algorithm operates in the time domain and extracts morphological fea- tures of the PPG. These features are characterized as lines which are classified as pulses and artifacts using adaptive thresholds. The algorithm was evaluated using the Complex System Laboratory (CSL) Benchmark data set. A sensitivity of 98.93% and positive predictive value of 96.68% have been obtained, which compares very favorably with the benchmark algorithm. The novel algorithm is currently being implemented into mobile phone pulse oximeters

@article{
 title = {Pulse oximeter plethysmograph variation and its relationship to the arterial waveform in mechanically ventilated children.},
 type = {article},
 year = {2012},
 keywords = {arterial pulse pressure variation,index,pulse oximeter plethysmograph á,variation á plethysmograph variability,á plethysmograph},
 pages = {145-51},
 volume = {26},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22407178},
 month = {6},
 day = {10},
 id = {d549f7c9-06b8-3c0b-a39c-6ae69de1b8f4},
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 accessed = {2012-07-24},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:07.186Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Chandler2012},
 notes = {IF-2018: 2.18},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {The variations induced by mechanical ventilation in the arterial pulse pressure and pulse oximeter plethysmograph waveforms have been shown to correlate closely and be effective in adults as markers of volume responsiveness. The aims of our study were to investigate: (1) the feasibility of recording plethysmograph indices; and (2) the relationship between pulse pressure variation (ΔPP), plethysmograph variation (ΔPOP) and plethysmograph variability index (PVI) in a diverse group of mechanically ventilated children. A prospective, observational study was performed. Mechanically ventilated children less than 11 years of age, with arterial catheters, were enrolled during the course of their clinical care in the operating room or in the pediatric intensive care unit. Real time monitor waveforms and trend data were recorded. ΔPP and ΔPOP were manually calculated and the relationships between ΔPP, ΔPOP and PVI were compared using Bland-Altman analysis and Pearson correlations. Forty-nine children were recruited; four (8%) subjects were excluded due to poor quality of the plethysmograph waveforms. ΔPP and ΔPOP demonstrated a strong correlation (r = 0.8439, P < 0.0001) and close agreement (Bias = 1.44 ± 6.4%). PVI was found to correlate strongly with ΔPP (r = 0.7049, P < 0.0001) and ΔPOP (r = 0.715, P < 0.0001). This study demonstrates the feasibility of obtaining plethysmographic variability indices in children under various physiological stresses. These data show a similarly strong correlation to that described in adults, between the variations induced by mechanical ventilation in arterial pulse pressure and the pulse oximeter plethysmograph.},
 bibtype = {article},
 author = {Chandler, John R and Cooke, Erin and Petersen, Chris and Karlen, Walter and Froese, Norbert and Lim, Joanne and Ansermino, J Mark},
 doi = {10.1007/s10877-012-9347-z},
 journal = {Journal of clinical monitoring and computing},
 number = {3}
}

The variations induced by mechanical ventilation in the arterial pulse pressure and pulse oximeter plethysmograph waveforms have been shown to correlate closely and be effective in adults as markers of volume responsiveness. The aims of our study were to investigate: (1) the feasibility of recording plethysmograph indices; and (2) the relationship between pulse pressure variation (ΔPP), plethysmograph variation (ΔPOP) and plethysmograph variability index (PVI) in a diverse group of mechanically ventilated children. A prospective, observational study was performed. Mechanically ventilated children less than 11 years of age, with arterial catheters, were enrolled during the course of their clinical care in the operating room or in the pediatric intensive care unit. Real time monitor waveforms and trend data were recorded. ΔPP and ΔPOP were manually calculated and the relationships between ΔPP, ΔPOP and PVI were compared using Bland-Altman analysis and Pearson correlations. Forty-nine children were recruited; four (8%) subjects were excluded due to poor quality of the plethysmograph waveforms. ΔPP and ΔPOP demonstrated a strong correlation (r = 0.8439, P < 0.0001) and close agreement (Bias = 1.44 ± 6.4%). PVI was found to correlate strongly with ΔPP (r = 0.7049, P < 0.0001) and ΔPOP (r = 0.715, P < 0.0001). This study demonstrates the feasibility of obtaining plethysmographic variability indices in children under various physiological stresses. These data show a similarly strong correlation to that described in adults, between the variations induced by mechanical ventilation in arterial pulse pressure and the pulse oximeter plethysmograph.

@inproceedings{
 title = {The Phone Oximeter for Mobile Spot-Check},
 type = {inproceedings},
 year = {2012},
 pages = {S21},
 volume = {115},
 issue = {2 Suppl},
 publisher = {Anesthesia and analgesia},
 city = {West Palm Beach},
 id = {2751fffc-645a-3cd7-b5eb-241231a932c2},
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 file_attached = {true},
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 last_modified = {2022-09-04T18:12:00.770Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dunsmuir2012a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,20f4663c-5015-44ee-8c35-394df252dd32},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Dunsmuir, Dustin and Petersen, Chris and Karlen, Walter and Lim, Joanne and Dumont, Guy A. and Ansermino, J. Mark},
 booktitle = {Abstracts of the 2012 Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

@inproceedings{
 title = {Adaptive Pulse Segmentation and Artifact Detection in Photoplethysmography for Mobile Applications},
 type = {inproceedings},
 year = {2012},
 keywords = {Adaptive filtering,Biomedical signal classification,Signals and systems},
 pages = {3131-4},
 month = {8},
 city = {San Diego},
 id = {560145ab-7673-31e2-967a-cb9f54692441},
 created = {2021-11-19T08:04:10.548Z},
 file_attached = {true},
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 last_modified = {2022-09-04T18:12:03.246Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,d25bf112-04e3-4db2-9a05-346bfdcbbda2},
 private_publication = {false},
 abstract = {Abstract—Pulse oximeters non-invasively measure heart rate and oxygen saturation and have great potential for predicting critical illness. The photoplethysmogram (PPG) recorded from pulse oximeters is often corrupted with artifacts that can render the vital signs obtained inaccurate. We present a novel real-time algorithm for segmentation of the PPG into pulses and classification of artifacts. The line segmentation algorithm operates in the time domain and extracts morphological fea- tures of the PPG. These features are characterized as lines which are classified as pulses and artifacts using adaptive thresholds. The algorithm was evaluated using the Complex System Laboratory (CSL) Benchmark data set. A sensitivity of 98.93% and positive predictive value of 96.68% have been obtained, which compares very favorably with the benchmark algorithm. The novel algorithm is currently being implemented into mobile phone pulse oximeters},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Ansermino, J Mark and Dumont, Guy A.},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

Abstract—Pulse oximeters non-invasively measure heart rate and oxygen saturation and have great potential for predicting critical illness. The photoplethysmogram (PPG) recorded from pulse oximeters is often corrupted with artifacts that can render the vital signs obtained inaccurate. We present a novel real-time algorithm for segmentation of the PPG into pulses and classification of artifacts. The line segmentation algorithm operates in the time domain and extracts morphological fea- tures of the PPG. These features are characterized as lines which are classified as pulses and artifacts using adaptive thresholds. The algorithm was evaluated using the Complex System Laboratory (CSL) Benchmark data set. A sensitivity of 98.93% and positive predictive value of 96.68% have been obtained, which compares very favorably with the benchmark algorithm. The novel algorithm is currently being implemented into mobile phone pulse oximeters

@inproceedings{
 title = {Design challenges for camera oximetry on a mobile phone},
 type = {inproceedings},
 year = {2012},
 keywords = {Biosensing Techniques,Biosensing Techniques: methods,Cellular Phone,Humans,Oximetry,Oximetry: methods,Pulse},
 pages = {2448-51},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23366420},
 month = {1},
 id = {7b19cc12-1011-3061-b2cc-84b595744fa5},
 created = {2021-11-19T08:04:11.426Z},
 accessed = {2014-06-18},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:07.596Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012b},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,d25bf112-04e3-4db2-9a05-346bfdcbbda2},
 private_publication = {false},
 abstract = {The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Lim, Joanne and Ansermino, J Mark and Dumont, Guy and Scheffer, Cornie},
 doi = {10.1109/EMBC.2012.6346459},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.

@article{
 title = {Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation.},
 type = {article},
 year = {2012},
 keywords = {Quality in mHealth,SQI},
 pages = {1617-29},
 volume = {33},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22986287},
 month = {9},
 day = {18},
 id = {2b1068dd-dc01-3238-8c6e-934df6ff69a5},
 created = {2022-06-18T09:45:36.993Z},
 accessed = {2012-09-22},
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 last_modified = {2022-09-04T18:12:06.077Z},
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 citation_key = {Karlen2012c},
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 private_publication = {false},
 abstract = {Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.},
 bibtype = {article},
 author = {Karlen, W and Kobayashi, K and Ansermino, J M and Dumont, G A},
 doi = {10.1088/0967-3334/33/10/1617},
 journal = {Physiological measurement},
 number = {10}
}

Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.

@inproceedings{
 title = {An Adaptive Single Frequency Phase Vocoder For Low-power Heart Rate Detection},
 type = {inproceedings},
 year = {2011},
 keywords = {embedded systems,heart rate estimation,mobile phones,photoplethysmography.,pulse detection},
 pages = {30-35},
 publisher = {INSTICC Press},
 id = {ad0c951d-2080-38ca-8450-62e6bb12bf64},
 created = {2010-10-08T17:26:14.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:12.315Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011},
 notes = {Selected paper},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Mobile phones can be used as a platform for clinical decision making in resource-poor and remote areas. Their limited battery and computational resources, however, demand efficient and low-power algorithms. We present a new algorithm for the fast and economical estimation of heart rate (HR) from the photoplethysmogram (PPG) recorded with a pulse oximeter connected to a mobile phone. The new method estimates the HR frequency by adaptively modeling the PPG wave with a sine function using a modified phase vocoder. The obtained wave is also used as an envelope for the detection of peaks in the PPG signal. HR is computed using the vocoder center frequency and using the peak intervals in a histogram. Experiments on a mobile device show comparable speed performance with other time domain algorithms. Preliminary tests show that the HR computed from the vocoder center frequency is robust to noisy PPG. The instantaneous HR calculated with the vocoder peak detection method was more sensitive to short-term HR variations. These results point to further developments using a combination of both HR estimation methods that will enable the robust implementation of adaptive phase vocoders into mobile phone applications.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Petersen, Chris and Gow, Jennifer and Ansermino, J. Mark and Dumont, Guy A.},
 booktitle = {BIODEVICES 2011 - Proceedings of the International Conference on Biomedical Electronics and Devices, Rome, Italy, January 26-29, 2011}
}

Mobile phones can be used as a platform for clinical decision making in resource-poor and remote areas. Their limited battery and computational resources, however, demand efficient and low-power algorithms. We present a new algorithm for the fast and economical estimation of heart rate (HR) from the photoplethysmogram (PPG) recorded with a pulse oximeter connected to a mobile phone. The new method estimates the HR frequency by adaptively modeling the PPG wave with a sine function using a modified phase vocoder. The obtained wave is also used as an envelope for the detection of peaks in the PPG signal. HR is computed using the vocoder center frequency and using the peak intervals in a histogram. Experiments on a mobile device show comparable speed performance with other time domain algorithms. Preliminary tests show that the HR computed from the vocoder center frequency is robust to noisy PPG. The instantaneous HR calculated with the vocoder peak detection method was more sensitive to short-term HR variations. These results point to further developments using a combination of both HR estimation methods that will enable the robust implementation of adaptive phase vocoders into mobile phone applications.

@inproceedings{
 title = {SleepPic. Hardware Developments for a Wearable On-line Sleep and Wake Discrimination System},
 type = {inproceedings},
 year = {2011},
 keywords = {Hardware,SleePic,wearable},
 pages = {132-7},
 publisher = {SciTePress},
 city = {Rome, Italy},
 id = {f87cf3af-b28d-3c85-b8d4-a36a63df21fe},
 created = {2010-10-08T17:17:59.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:09.536Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011a},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30,7ecba661-0765-42d5-a37e-6a57b331e28c,4afa8d9a-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {The design of wearable systems comes with constraints in computational and power resources. We describe the development of customized hardware for the wearable discrimination of human sleep and wake based on cardio-respiratory signals. The device was designed for efficient and low-power computation of Fast Fourier Transforms and artificial neural networks required for the on-line classification. We discuss methods for reducing computational load and consequently power requirements of the device. The developed wearable SleePic prototype was tested for autonomy and comfort on eight healthy subjects. SleePic showed an energetic autonomy of more than 36 hours. The SleePic device will require further integration for increased comfort and improved user interaction.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Floreano, Dario},
 editor = {Babiloni, Fabio and Fred, Ana and Filipe, Joaquim and Gamboa, Hugo},
 booktitle = {Proc. of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS)}
}

The design of wearable systems comes with constraints in computational and power resources. We describe the development of customized hardware for the wearable discrimination of human sleep and wake based on cardio-respiratory signals. The device was designed for efficient and low-power computation of Fast Fourier Transforms and artificial neural networks required for the on-line classification. We discuss methods for reducing computational load and consequently power requirements of the device. The developed wearable SleePic prototype was tested for autonomy and comfort on eight healthy subjects. SleePic showed an energetic autonomy of more than 36 hours. The SleePic device will require further integration for increased comfort and improved user interaction.

@inproceedings{
 title = {Human-centered Phone Oximeter Interface Design for the Operating Room},
 type = {inproceedings},
 year = {2011},
 keywords = {anesthesia,human-centered,interface design,mobile phones,photoplethysmography,pulse oximeter},
 pages = {433-8},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0003335204330438},
 publisher = {SciTePress - Science and and Technology Publications},
 city = {Rome, Italy},
 id = {197b5ce0-6a90-3e99-b86f-62d3737d2dbf},
 created = {2010-12-09T06:29:30.000Z},
 accessed = {2013-06-05},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:21.023Z},
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 citation_key = {Karlen2011f},
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 private_publication = {false},
 abstract = {Mobile phones offer huge potential as platforms for clinical decision making in resource-poor and remote areas. We present methods for the development of a human-centered interface for anesthesia monitoring that is targeted to remote operating rooms in developing countries. The Phone Oximeter is compatible with major PC and mobile phone operating systems and is optimized for small phone screens. It displays vital physiological parameters in the corresponding clinical colours. Combined with an easily identifiable icon, this guarantees that accessibility is language-independent. To evaluate the acceptance and usability of the initial prototype of the Phone Oximeter, the Think Aloud process while completing a specific Task List, followed by the Mobile Phone Usability Questionnaire (MPUQ) were tested on 20 subjects with varying medical and mobile phone experience. The acceptance rate of 81.9 % from the MPUQ questionnaire clearly demonstrates the usability of the Phone Oximeter. The incorporation of the most relevant errors and complaints into the design of the next iteration of the Phone Oximeter prototype enhanced its capabilities further.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Dumont, Guy A and Petersen, Chris and Gow, Jennifer and Lim, Joanne and Sleiman, Jules and Ansermino, J Mark},
 editor = {Traver, Vicente and Fred, Ana and Filipe, Joaquim and Gamboa, Hugo},
 doi = {10.5220/0003335204330438},
 booktitle = {Proc. of the Int. Conf. on Health Informatics (HEALTHINF)}
}

Mobile phones offer huge potential as platforms for clinical decision making in resource-poor and remote areas. We present methods for the development of a human-centered interface for anesthesia monitoring that is targeted to remote operating rooms in developing countries. The Phone Oximeter is compatible with major PC and mobile phone operating systems and is optimized for small phone screens. It displays vital physiological parameters in the corresponding clinical colours. Combined with an easily identifiable icon, this guarantees that accessibility is language-independent. To evaluate the acceptance and usability of the initial prototype of the Phone Oximeter, the Think Aloud process while completing a specific Task List, followed by the Mobile Phone Usability Questionnaire (MPUQ) were tested on 20 subjects with varying medical and mobile phone experience. The acceptance rate of 81.9 % from the MPUQ questionnaire clearly demonstrates the usability of the Phone Oximeter. The incorporation of the most relevant errors and complaints into the design of the next iteration of the Phone Oximeter prototype enhanced its capabilities further.

@inproceedings{
 title = {Location independence in patient monitoring},
 type = {inproceedings},
 year = {2011},
 keywords = {Anesthesia,Anesthesiology,Computer Simulation,Humans,Medical,Technology},
 pages = {S37},
 volume = {113},
 issue = {2 Suppl},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21788323},
 month = {8},
 publisher = {Anesthesia and Analgesia},
 city = {Las Vegas},
 id = {40c34821-695b-3af0-b409-ca02fd28398b},
 created = {2010-12-30T01:54:18.000Z},
 accessed = {2011-12-05},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:54.863Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011g},
 notes = {STA Excellence in Technology Innovation Award},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb,4dc8f08e-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {Introduction Hospital patients require physiological monitoring throughout their stay. Monitoring requirements depend on the hospital unit (e.g. Admission, OR, ICU, ward). Currently, monitoring devices are stationary and are connected by wires to sensors and patient. This is cumbersome for both patient and health care providers, and sensors must be disconnected when the patient is prepared for transfer between units. Further, sensors located in one unit are often incompatible with those in another. We propose a novel concept that simplifies patient monitoring throughout the hospital. Method Approach:We propose a two level wireless network (Fig. 1). A personal area network (PAN) is private to the patient and is responsible for the control of data communication. The PAN host device connects to all required sensors using a wide range of supported protocols (e.g. serial, USB,WiFi and Bluetooth), and is attached to the patient during the entire hospital stay. The PAN host then wirelessly transmits the standardized data to a local area network (LAN) that records patient health information in a database. This information can be retrieved in real time by either stationary monitoring devices or mobile devices of health care providers throughout the hospital network. Prototype: The prototype consists of two pulse oximeters (Nonin, USA) connected via Bluetooth and wired connection, respectively, to a computer with a Linux operating system that acts as the host for the PAN. The LAN consists of a server running a web-based sensor actuator network portal called Sense Tecnic [1]. AWiFi enabled mobile device is used as the monitoring display. Results & Discussion Blood oxygen saturation and heart rate trend signals are recorded and displayed in real time at a 1 Hz update rate. The web-based data portal allows platform independent, real-time monitoring. The PAN allows for easy connection of sensors to the patient and facilitates monitoring during patient movement and transportation. This approach will facilitate the use of elementary sensors without interruption throughout the hospital. Unit specific sensors can be added to the PAN when required. Future work will include geolocation by indoor triangulation using theWiFi network, and size reduction of the PAN host.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Blackstock, Michael and Ansermino, J Mark},
 doi = {10.1213/01.ANE.0000403381.51061.df},
 booktitle = {Abstracts of the 2011 Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

Introduction Hospital patients require physiological monitoring throughout their stay. Monitoring requirements depend on the hospital unit (e.g. Admission, OR, ICU, ward). Currently, monitoring devices are stationary and are connected by wires to sensors and patient. This is cumbersome for both patient and health care providers, and sensors must be disconnected when the patient is prepared for transfer between units. Further, sensors located in one unit are often incompatible with those in another. We propose a novel concept that simplifies patient monitoring throughout the hospital. Method Approach:We propose a two level wireless network (Fig. 1). A personal area network (PAN) is private to the patient and is responsible for the control of data communication. The PAN host device connects to all required sensors using a wide range of supported protocols (e.g. serial, USB,WiFi and Bluetooth), and is attached to the patient during the entire hospital stay. The PAN host then wirelessly transmits the standardized data to a local area network (LAN) that records patient health information in a database. This information can be retrieved in real time by either stationary monitoring devices or mobile devices of health care providers throughout the hospital network. Prototype: The prototype consists of two pulse oximeters (Nonin, USA) connected via Bluetooth and wired connection, respectively, to a computer with a Linux operating system that acts as the host for the PAN. The LAN consists of a server running a web-based sensor actuator network portal called Sense Tecnic [1]. AWiFi enabled mobile device is used as the monitoring display. Results & Discussion Blood oxygen saturation and heart rate trend signals are recorded and displayed in real time at a 1 Hz update rate. The web-based data portal allows platform independent, real-time monitoring. The PAN allows for easy connection of sensors to the patient and facilitates monitoring during patient movement and transportation. This approach will facilitate the use of elementary sensors without interruption throughout the hospital. Unit specific sensors can be added to the PAN when required. Future work will include geolocation by indoor triangulation using theWiFi network, and size reduction of the PAN host.

@inproceedings{
 title = {Automated Validation of Capillary Refill Time Measurements Using Photo-Plethysmogram from a Portable Device for Effective Triage in Children},
 type = {inproceedings},
 year = {2011},
 pages = {66-71},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6103610},
 month = {10},
 publisher = {IEEE},
 id = {7ebe6e87-72bf-3153-b768-317cf9f88044},
 created = {2011-05-12T18:49:41.000Z},
 accessed = {2012-01-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:55.127Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011i},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,0f17b6d7-75e3-4f99-a546-6054e4a41808,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Capillary refill time (CRT) is an important tool for the clinical assessment of trauma and dehydration. Indeed, it has been incorporated into advanced life support guidelines as part of the rapid assessment of critically ill patients. However, digitalized CRT techniques are not readily available and the standard assessment based on the visual inspection of CRT lacks standardization and is prone to a high inter-observer variability. We present an algorithm for the automatic validation of the CRT measurement on the finger using photo-plethysmogram recordings on a small portable device. It is based on a set of deterministic rules for the classification of finger pressure and regular plethysmographic pulses. Validation studies using the classification of 93 pediatric recordings from Canada and Uganda showed that the novel algorithm reliably detects invalid CRT measurements (sensitivity 98.4%). This includes patterns such as insufficient pressure, low perfusion signals, and artifacts. Since our device consists of widely available components already in use, the promising results suggest that the algorithm could be readily integrated in operating rooms and intensive care units around the world. This more robust assessment of CRT would produce a more powerful diagnostic tool for clinical triage in critical care settings.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Pickard, Amelia and Daniels, Jeremy and Kwizera, Arthur and Ibingira, Charles and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1109/GHTC.2011.19},
 booktitle = {2011 IEEE Global Humanitarian Technology Conference (GHTC)}
}

Capillary refill time (CRT) is an important tool for the clinical assessment of trauma and dehydration. Indeed, it has been incorporated into advanced life support guidelines as part of the rapid assessment of critically ill patients. However, digitalized CRT techniques are not readily available and the standard assessment based on the visual inspection of CRT lacks standardization and is prone to a high inter-observer variability. We present an algorithm for the automatic validation of the CRT measurement on the finger using photo-plethysmogram recordings on a small portable device. It is based on a set of deterministic rules for the classification of finger pressure and regular plethysmographic pulses. Validation studies using the classification of 93 pediatric recordings from Canada and Uganda showed that the novel algorithm reliably detects invalid CRT measurements (sensitivity 98.4%). This includes patterns such as insufficient pressure, low perfusion signals, and artifacts. Since our device consists of widely available components already in use, the promising results suggest that the algorithm could be readily integrated in operating rooms and intensive care units around the world. This more robust assessment of CRT would produce a more powerful diagnostic tool for clinical triage in critical care settings.

@inproceedings{
 title = {Respiratory rate estimation using respiratory sinus arrhythmia from photoplethysmography.},
 type = {inproceedings},
 year = {2011},
 keywords = {anesthesia,heart rate variability,photo-plethysmogram,pulse oximeter,respiratory rate,respiratory sinus arrhythmia},
 pages = {1201-4},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22254531},
 month = {8},
 city = {Boston},
 id = {b4e1a9b0-0979-3206-8821-7cbf19e89920},
 created = {2011-05-12T18:57:10.000Z},
 accessed = {2012-01-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:18.866Z},
 read = {true},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011j},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,0d3ec8ac-06e7-459a-9eec-b845649cdc31,4afa93ee-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Respiratory rate (RR) is an important measurement for ambulatory care and there is high interest in its detection using unobtrusive mobile devices. For this study, we investigated the estimation of RR from a photoplethysmography (PPG) signal that originated from a pulse oximeter sensor and had a sub-optimal sampling rate. We explored the possibility of estimating RR by extracting respiratory sinus arrhythmia (RSA) from the PPG-derived heart rate variability (HRV) measurement using real-time algorithms. Data from 29 children and 13 adults undergoing general anesthesia were analyzed. We compared the RSA power derived from electrocardiography (ECG) with PPG at the reference RR derived from capnography. The power of the PPG was significantly higher than that of the ECG (182.42 ± 36.75 dB vs. 162.30 ± 43.66 dB). Further, the mean RR error for PPG was lower than ECG. Both PPG and ECG RR estimation techniques were more powerful and reliable in cases of spontaneous ventilation than when pressure controlled ventilation was used. The analysis of cases containing artifacts in the PPG revealed a significant increase in RR error, a trend that was less pronounced for controlled ventilation. These results indicate that the estimation of RR from the sub-optimally sampled PPG signal is possible and more reliable than from the ECG.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Brouse, Christopher J and Cooke, Erin and Ansermino, J Mark and Dumont, Guy A},
 doi = {10.1109/IEMBS.2011.6090282},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Respiratory rate (RR) is an important measurement for ambulatory care and there is high interest in its detection using unobtrusive mobile devices. For this study, we investigated the estimation of RR from a photoplethysmography (PPG) signal that originated from a pulse oximeter sensor and had a sub-optimal sampling rate. We explored the possibility of estimating RR by extracting respiratory sinus arrhythmia (RSA) from the PPG-derived heart rate variability (HRV) measurement using real-time algorithms. Data from 29 children and 13 adults undergoing general anesthesia were analyzed. We compared the RSA power derived from electrocardiography (ECG) with PPG at the reference RR derived from capnography. The power of the PPG was significantly higher than that of the ECG (182.42 ± 36.75 dB vs. 162.30 ± 43.66 dB). Further, the mean RR error for PPG was lower than ECG. Both PPG and ECG RR estimation techniques were more powerful and reliable in cases of spontaneous ventilation than when pressure controlled ventilation was used. The analysis of cases containing artifacts in the PPG revealed a significant increase in RR error, a trend that was less pronounced for controlled ventilation. These results indicate that the estimation of RR from the sub-optimally sampled PPG signal is possible and more reliable than from the ECG.

@inproceedings{
 title = {Wavelet transform cardiorespiratory coherence detects patient movement during general anesthesia.},
 type = {inproceedings},
 year = {2011},
 pages = {6114-7},
 volume = {2011},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22255734},
 month = {8},
 id = {c99682d7-ede3-304a-8378-2d00ce456813},
 created = {2011-05-13T00:32:24.000Z},
 accessed = {2012-01-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:02.140Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brouse2011},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiores-piratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). We have assessed the algorithm's ability to detect movement events (indicative of patient response to nociception) in 39 pediatric patients receiving general anesthesia. Sixty movement events were recorded during the 39 surgical procedures. Minimum and average WTCRC were calculated in a 30 second window surrounding each movement event. We used a 95% significance level as the threshold for detecting nociception during patient movement. The 95% significance level was calculated relative to a red noise background, using Monte Carlo simulations. It was calculated to be 0.7. Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.},
 bibtype = {inproceedings},
 author = {Brouse, Christopher J and Karlen, Walter and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Dumont, Guy A and Ansermino, J Mark},
 doi = {10.1109/IEMBS.2011.6091510},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiores-piratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). We have assessed the algorithm's ability to detect movement events (indicative of patient response to nociception) in 39 pediatric patients receiving general anesthesia. Sixty movement events were recorded during the 39 surgical procedures. Minimum and average WTCRC were calculated in a 30 second window surrounding each movement event. We used a 95% significance level as the threshold for detecting nociception during patient movement. The 95% significance level was calculated relative to a red noise background, using Monte Carlo simulations. It was calculated to be 0.7. Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.

@article{
 title = {Medical intelligence article: capillary refill time: is it still a useful clinical sign?},
 type = {article},
 year = {2011},
 keywords = {review},
 pages = {120-3},
 volume = {113},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21519051},
 month = {7},
 publisher = {IARS},
 id = {6c7944e3-ad95-323a-ab46-0991bcd4d413},
 created = {2011-09-29T23:34:53.000Z},
 accessed = {2011-07-01},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:18.148Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pickard2011a},
 notes = {IF-2018: 3.49},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa922c-d8d6-102e-ac9a-0024e85ead87,6474b937-06bf-4f1e-b2ea-031526d25d55},
 private_publication = {false},
 abstract = {Capillary refill time (CRT) is widely used by health care workers as part of the rapid, structured cardiopulmonary assessment of critically ill patients. Measurement involves the visual inspection of blood returning to distal capillaries after they have been emptied by pressure. It is hypothesized that CRT is a simple measure of alterations in peripheral perfusion. Evidence for the use of CRT in anesthesia is lacking and further research is required, but understanding may be gained from evidence in other fields. In this report, we examine this evidence and factors affecting CRT measurement. Novel approaches to the assessment of CRT are under investigation. In the future, CRT measurement may be achieved using new technologies such as digital videography or modified oxygen saturation probes; these new methods would remove the limitations associated with clinical CRT measurement and may even be able to provide an automated CRT measurement.},
 bibtype = {article},
 author = {Pickard, Amelia and Karlen, Walter and Ansermino, J Mark},
 doi = {10.1213/ANE.0b013e31821569f9},
 journal = {Anesthesia and analgesia},
 number = {1}
}

Capillary refill time (CRT) is widely used by health care workers as part of the rapid, structured cardiopulmonary assessment of critically ill patients. Measurement involves the visual inspection of blood returning to distal capillaries after they have been emptied by pressure. It is hypothesized that CRT is a simple measure of alterations in peripheral perfusion. Evidence for the use of CRT in anesthesia is lacking and further research is required, but understanding may be gained from evidence in other fields. In this report, we examine this evidence and factors affecting CRT measurement. Novel approaches to the assessment of CRT are under investigation. In the future, CRT measurement may be achieved using new technologies such as digital videography or modified oxygen saturation probes; these new methods would remove the limitations associated with clinical CRT measurement and may even be able to provide an automated CRT measurement.

@inproceedings{
 title = {Automated Validation of Capillary Refill Time Measurements Using Photo-Plethysmogram from a Portable Device for Effective Triage in Children},
 type = {inproceedings},
 year = {2011},
 pages = {66-71},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6103610},
 month = {10},
 publisher = {IEEE},
 id = {42f2b5e1-18bc-33f4-b3cc-5189bd034667},
 created = {2021-11-19T07:52:28.196Z},
 accessed = {2012-01-22},
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 last_modified = {2022-09-04T18:12:24.341Z},
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 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,20f4663c-5015-44ee-8c35-394df252dd32},
 private_publication = {false},
 abstract = {Capillary refill time (CRT) is an important tool for the clinical assessment of trauma and dehydration. Indeed, it has been incorporated into advanced life support guidelines as part of the rapid assessment of critically ill patients. However, digitalized CRT techniques are not readily available and the standard assessment based on the visual inspection of CRT lacks standardization and is prone to a high inter-observer variability. We present an algorithm for the automatic validation of the CRT measurement on the finger using photo-plethysmogram recordings on a small portable device. It is based on a set of deterministic rules for the classification of finger pressure and regular plethysmographic pulses. Validation studies using the classification of 93 pediatric recordings from Canada and Uganda showed that the novel algorithm reliably detects invalid CRT measurements (sensitivity 98.4%). This includes patterns such as insufficient pressure, low perfusion signals, and artifacts. Since our device consists of widely available components already in use, the promising results suggest that the algorithm could be readily integrated in operating rooms and intensive care units around the world. This more robust assessment of CRT would produce a more powerful diagnostic tool for clinical triage in critical care settings.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Pickard, Amelia and Daniels, Jeremy and Kwizera, Arthur and Ibingira, Charles and Dumont, Guy A and Ansermino, J. Mark},
 doi = {10.1109/GHTC.2011.19},
 booktitle = {2011 IEEE Global Humanitarian Technology Conference}
}

Capillary refill time (CRT) is an important tool for the clinical assessment of trauma and dehydration. Indeed, it has been incorporated into advanced life support guidelines as part of the rapid assessment of critically ill patients. However, digitalized CRT techniques are not readily available and the standard assessment based on the visual inspection of CRT lacks standardization and is prone to a high inter-observer variability. We present an algorithm for the automatic validation of the CRT measurement on the finger using photo-plethysmogram recordings on a small portable device. It is based on a set of deterministic rules for the classification of finger pressure and regular plethysmographic pulses. Validation studies using the classification of 93 pediatric recordings from Canada and Uganda showed that the novel algorithm reliably detects invalid CRT measurements (sensitivity 98.4%). This includes patterns such as insufficient pressure, low perfusion signals, and artifacts. Since our device consists of widely available components already in use, the promising results suggest that the algorithm could be readily integrated in operating rooms and intensive care units around the world. This more robust assessment of CRT would produce a more powerful diagnostic tool for clinical triage in critical care settings.

@inproceedings{
 title = {Human-centered Phone Oximeter Interface Design for the Operating Room},
 type = {inproceedings},
 year = {2011},
 keywords = {anesthesia,human-centered,interface design,mobile phones,photoplethysmography,pulse oximeter},
 pages = {433-8},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0003335204330438},
 publisher = {SciTePress - Science and and Technology Publications},
 city = {Rome, Italy},
 id = {fa515e2a-cd68-3337-b682-82e9f2839172},
 created = {2021-11-19T07:52:30.297Z},
 accessed = {2013-06-05},
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 last_modified = {2022-09-04T18:12:00.352Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011f},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,20f4663c-5015-44ee-8c35-394df252dd32},
 private_publication = {false},
 abstract = {Mobile phones offer huge potential as platforms for clinical decision making in resource-poor and remote areas. We present methods for the development of a human-centered interface for anesthesia monitoring that is targeted to remote operating rooms in developing countries. The Phone Oximeter is compatible with major PC and mobile phone operating systems and is optimized for small phone screens. It displays vital physiological parameters in the corresponding clinical colours. Combined with an easily identifiable icon, this guarantees that accessibility is language-independent. To evaluate the acceptance and usability of the initial prototype of the Phone Oximeter, the Think Aloud process while completing a specific Task List, followed by the Mobile Phone Usability Questionnaire (MPUQ) were tested on 20 subjects with varying medical and mobile phone experience. The acceptance rate of 81.9 % from the MPUQ questionnaire clearly demonstrates the usability of the Phone Oximeter. The incorporation of the most relevant errors and complaints into the design of the next iteration of the Phone Oximeter prototype enhanced its capabilities further.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Dumont, Guy A and Petersen, Chris and Gow, Jennifer and Lim, Joanne and Sleiman, Jules and Ansermino, J Mark},
 editor = {Traver, Vicente and Fred, Ana and Filipe, Joaquim and Gamboa, Hugo},
 doi = {10.5220/0003335204330438},
 booktitle = {Proceedings of the International Conference on Health Informatics}
}

Mobile phones offer huge potential as platforms for clinical decision making in resource-poor and remote areas. We present methods for the development of a human-centered interface for anesthesia monitoring that is targeted to remote operating rooms in developing countries. The Phone Oximeter is compatible with major PC and mobile phone operating systems and is optimized for small phone screens. It displays vital physiological parameters in the corresponding clinical colours. Combined with an easily identifiable icon, this guarantees that accessibility is language-independent. To evaluate the acceptance and usability of the initial prototype of the Phone Oximeter, the Think Aloud process while completing a specific Task List, followed by the Mobile Phone Usability Questionnaire (MPUQ) were tested on 20 subjects with varying medical and mobile phone experience. The acceptance rate of 81.9 % from the MPUQ questionnaire clearly demonstrates the usability of the Phone Oximeter. The incorporation of the most relevant errors and complaints into the design of the next iteration of the Phone Oximeter prototype enhanced its capabilities further.

@inproceedings{
 title = {Capillary Refill Time Assessment Using a Mobile Phone Application (iRefill)},
 type = {inproceedings},
 year = {2010},
 keywords = {automatic,capillary refill time,crt,mobile phone,pulse oximeter},
 pages = {A575},
 websites = {http://www.asaabstracts.com/strands/asaabstracts/printAbstract.htm;jsessionid=2971A813366416AB14723F09DA1C5AD0?year=2010&index=8&absnum=695&type=archive},
 publisher = {American Society of Anesthesiologists},
 city = {San Diego},
 id = {ed840441-cc31-364a-8112-2635d3686c13},
 created = {2010-10-21T22:19:47.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:21.485Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2010a},
 notes = {Best New Technological Innovation Award},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb},
 private_publication = {false},
 abstract = {Identifying capillary refill time (CRT) is an integral part of the clinical assessment of circulatory status and identification of dehydration in children. However, visual inspection of the finger to assess CRT has low inter-observer reliability, largely due to human limitations in estimating short time intervals. To improve precision, we have developed a mobile phone software application (iRefill) that automatically assesses CRT using a photo-plethysmogram (PPG) sensor. Commonly used to measure blood oxygen saturation and heart rate, this sensor can be adapted to replace the human eye to objectively measure CRT.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Petersen, Chris and Pickard, Amelia and Dumont, Guy A and Ansermino, J Mark},
 booktitle = {Proceedings of the 2010 Annual Meeting of the American Society Anesthesiologists}
}

Identifying capillary refill time (CRT) is an integral part of the clinical assessment of circulatory status and identification of dehydration in children. However, visual inspection of the finger to assess CRT has low inter-observer reliability, largely due to human limitations in estimating short time intervals. To improve precision, we have developed a mobile phone software application (iRefill) that automatically assesses CRT using a photo-plethysmogram (PPG) sensor. Commonly used to measure blood oxygen saturation and heart rate, this sensor can be adapted to replace the human eye to objectively measure CRT.

@inproceedings{
 title = {Enhancing pilot performance with a SymBodic system.},
 type = {inproceedings},
 year = {2010},
 keywords = {aerospace,fatigue management,haptic feedback,human performance,sleep / wake classification,symbodic},
 pages = {6599-602},
 volume = {1},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21096516},
 month = {1},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Buenos Aires},
 id = {eaba85fc-a736-3894-95fa-c5a93e32a69b},
 created = {2010-03-03T01:46:45.000Z},
 accessed = {2010-12-30},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:01.936Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2010c},
 folder_uuids = {f1f67efc-95a7-4f1a-b181-c3670c667a34,4afa922c-d8d6-102e-ac9a-0024e85ead87,4afa909c-d8d6-102e-ac9a-0024e85ead87,d9198259-8733-497d-ab87-d2a9518e0d30},
 private_publication = {false},
 abstract = {Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot's sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experiments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Cardin, Sylvain and Thalmann, Daniel and Floreano, Dario},
 doi = {10.1109/IEMBS.2010.5627127},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot's sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experiments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.

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 abstract = {Sleep/wake classification systems that rely on physiological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line adaptation technique that updates the sleep/wake classifier in real-time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed electrocardiogram and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subjectindependent classifier algorithm, the SleePic device was only able to correctly classify 74.94% 6.76 of the human rated sleep/wake data. By using the suggested automatic adaptation method the mean classification accuracy could be significantly improved to 92.98% 3.19. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44% 3.57. We demonstrated that subject-independent models used for online sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.},
 bibtype = {article},
 author = {Karlen, Walter and Floreano, Dario},
 doi = {10.1109/TBME.2010.2097261},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {4}
}

Sleep/wake classification systems that rely on physiological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line adaptation technique that updates the sleep/wake classifier in real-time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed electrocardiogram and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subjectindependent classifier algorithm, the SleePic device was only able to correctly classify 74.94% 6.76 of the human rated sleep/wake data. By using the suggested automatic adaptation method the mean classification accuracy could be significantly improved to 92.98% 3.19. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44% 3.57. We demonstrated that subject-independent models used for online sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

@inproceedings{
 title = {CapnoBase: Signal database and tools to collect, share and annotate respiratory signals},
 type = {inproceedings},
 year = {2010},
 keywords = {Capnogram,capnobase,database,flow,pressure,respiratory},
 pages = {25},
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 abstract = {The development of reliable and robust algorithms for the processing of biomedical signals in the operating room requires a series of high resolution signals recorded under different and known conditions. For algorithm tuning and validation, large datasets containing annotated clinical scenarios are required. These scenarios can be difficult to obtain, especially in the case of rare respiratory events recorded during anesthesia (e.g. rising end-tidal carbon dioxide (EtCO2) associated with malignant hyperthermia or anaphylaxis). The collection and annotation of data is very time consuming. In addition the comparative performance of an algorithm can only be assessed using a benchmark dataset. There is currently no public benchmarking dataset for respiratory signal analysis available. CapnoBase is a collaborative research project designed to provide easy to use research tools and a database of annotated respiratory signals including a benchmark dataset.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Turner, M and Cooke, Erin and Dumont, Guy A and Ansermino, J Mark},
 doi = {20.500.11850/87887},
 booktitle = {Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

The development of reliable and robust algorithms for the processing of biomedical signals in the operating room requires a series of high resolution signals recorded under different and known conditions. For algorithm tuning and validation, large datasets containing annotated clinical scenarios are required. These scenarios can be difficult to obtain, especially in the case of rare respiratory events recorded during anesthesia (e.g. rising end-tidal carbon dioxide (EtCO2) associated with malignant hyperthermia or anaphylaxis). The collection and annotation of data is very time consuming. In addition the comparative performance of an algorithm can only be assessed using a benchmark dataset. There is currently no public benchmarking dataset for respiratory signal analysis available. CapnoBase is a collaborative research project designed to provide easy to use research tools and a database of annotated respiratory signals including a benchmark dataset.

@article{
 title = {Adaptive Sleep-Wake Discrimination for Wearable Devices},
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 author = {Karlen, Walter and Floreano, D},
 doi = {10.1109/TBME.2010.2097261},
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Sleep/wake classification systems that rely on physiological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line adaptation technique that updates the sleep/wake classifier in real-time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed electrocardiogram and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subjectindependent classifier algorithm, the SleePic device was only able to correctly classify 74.94% 6.76 of the human rated sleep/wake data. By using the suggested automatic adaptation method the mean classification accuracy could be significantly improved to 92.98% 3.19. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44% 3.57. We demonstrated that subject-independent models used for online sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

@article{
 title = {Evolutionary Selection of Features for Neural Sleep/Wake Discrimination},
 type = {article},
 year = {2009},
 pages = {1-10},
 volume = {2009},
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 abstract = {In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on amicrocontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often requiremore computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications,where computational resources and energy are limited, this is especially detrimental. Neuroevolutionarymethods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of themobile system.},
 bibtype = {article},
 author = {Dürr, Peter and Karlen, Walter and Guignard, Jeremie and Mattiussi, Claudio},
 doi = {10.1155/2009/179680},
 journal = {Journal of Artificial Evolution and Applications}
}

In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on amicrocontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often requiremore computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications,where computational resources and energy are limited, this is especially detrimental. Neuroevolutionarymethods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of themobile system.

@article{
 title = {Sleep and Wake Classification With ECG and Respiratory Effort Signals},
 type = {article},
 year = {2009},
 keywords = {Biomedical signal analysis,electrocardiography,neural classifier,respiratory effort,sleep and wake classification,wearable computing},
 pages = {71-8},
 volume = {3},
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 abstract = {We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a fast Fourier transform as the main feature extraction tool and a feedforward artificial neural network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify, on average, 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, receiver operating characteristic analysis shows that compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.},
 bibtype = {article},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 doi = {10.1109/TBCAS.2008.2008817},
 journal = {IEEE Transactions on Biomedical Circuits and Systems},
 number = {2}
}

We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a fast Fourier transform as the main feature extraction tool and a feedforward artificial neural network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify, on average, 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, receiver operating characteristic analysis shows that compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.

@phdthesis{
 title = {Adaptive Wake and Sleep Detection for Wearable Systems},
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 abstract = {Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.},
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Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

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 bibtype = {phdthesis},
 author = {Karlen, Walter}
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Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

@inproceedings{
 title = {Improving actigraph sleep/wake classification with cardio-respiratory signals.},
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 year = {2008},
 keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer-Assisted,Electrocardiography,Electrocardiography: methods,Equipment Design,Equipment Failure Analysis,Humans,Motor Activity,Motor Activity: physiology,Pattern Recognition,Polysomnography,Polysomnography: methods,Reproducibility of Results,Sensitivity and Specificity,Signal Processing,Spirometry,Spirometry: methods,Wakefulness,Wakefulness: physiology},
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 abstract = {Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 doi = {10.1109/IEMBS.2008.4650401},
 booktitle = {Annual Int. Conference of the IEEE Engineering in Medicine and Biology Society.}
}

Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.

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 bibtype = {inproceedings},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 doi = {10.1109/IEMBS.2008.4650401},
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Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.

@article{
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This paper describes hardware and behavior implementation of a miniature robot in size of a match box that simulates the behavior of cockroaches in order to establish a social interaction with them. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication to monitor, log, supervise the biological experiment, and detect the other robots in short range. The behavioral model of the robot is a mixture of fusion in low-level and arbitration in high-level. In arbitration level a stochastic state machine selects the proper subtask. Then in fusion level, that subtask is decomposed to a hierarchy of sub-tasks. Each sub-task generates a potential field. The resultant force is then mapped to an action.

@inproceedings{
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 author = {Tâche, F and Asadpour, M and Caprari, G and Karlen, Walter and Siegwart, R},
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This paper describes the hardware and behavior implementation of a miniature robot, in size of a match box, that is able to interact with cockroaches. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. It is also equipped with 12 infra-red proximity sensors, 2 light sensors, a linear camera and a battery that allows 3 hours autonomy. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication: a wireless module for monitoring and logging, an IR remote receiver for fast supervision of biological experiment and a simple local communication protocol via infrared proximity sensors to detect robots in short range

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@inproceedings{
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