@article{
 title = {Accurately inferring physical activity levels and sleep from wrist-worn actigraphy recordings with sample rates as low as 10 Hz},
 type = {article},
 year = {2025},
 pages = {27257 - 27267},
 volume = {13},
 websites = {https://ieeexplore.ieee.org/document/10876132},
 id = {026adcc7-afe4-36fb-aecf-5d538a39fb70},
 created = {2025-05-17T19:46:17.685Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2025-05-21T09:20:15.807Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Inferring longitudinal Physical Activity (PA) levels and sleep timings from wrist-worn sensors may facilitate personalized insights into day-today profile assessments and can be used to monitor a range of physical-and mental-health outcomes, including towards symptom monitoring and rehabilitation. We used the publicly available CAPTURE-24 dataset, comprising 148 participants with ~24-hour concurrent three-dimensional wrist-worn accelerometer data and minute-by-minute labels used as ground truth: sleep, sedentary, light, moderate-vigorous PA. First, we down-sampled the raw accelerometry data to 10 Hz to ensure the generalizability of our methodology across longitudinal studies which typically use similarly low sample rates for actigraphy. Subsequently, we computed four complementary acceleration summary measures and 10 additional smoothened outputs for each acceleration summary measure to derive 44 features characterizing minute-by-minute PA. These features were presented into different classifiers casted as a 4-class classification problem. We trained the model using the first 98 participants and assessed model performance and generalization on the remaining 50 participants. Using a random forest classifier, we demonstrated accurately estimating PA levels and sleep with 87% overall accuracy (F1-score=0.80) including 98.6% correct sleep detection. These findings processing the down-sampled actigraphy data to 10 Hz match or exceed state-of-art results recently reported in the literature achieved using considerably more sophisticated and time-consuming methods (including deep learning) which required actigraphy data sampled at 100 Hz. Collectively, these findings support the deployment of longitudinal, large-scale actigraphy data with sample rates as low as 10 Hz, towards accurately estimating personalized day-today PA and sleep profiles in healthcare community studies. INDEX TERMS 24-hour activity profile, actigraphy, Axivity AX3, CAPTURE-24, physical activity, sleep, smartwatch, wrist-worn wearable sensor},
 bibtype = {article},
 author = {Tsanas, Athanasios},
 doi = {10.1109/ACCESS.2025.3539278},
 journal = {IEEE Access}
}

Inferring longitudinal Physical Activity (PA) levels and sleep timings from wrist-worn sensors may facilitate personalized insights into day-today profile assessments and can be used to monitor a range of physical-and mental-health outcomes, including towards symptom monitoring and rehabilitation. We used the publicly available CAPTURE-24 dataset, comprising 148 participants with ~24-hour concurrent three-dimensional wrist-worn accelerometer data and minute-by-minute labels used as ground truth: sleep, sedentary, light, moderate-vigorous PA. First, we down-sampled the raw accelerometry data to 10 Hz to ensure the generalizability of our methodology across longitudinal studies which typically use similarly low sample rates for actigraphy. Subsequently, we computed four complementary acceleration summary measures and 10 additional smoothened outputs for each acceleration summary measure to derive 44 features characterizing minute-by-minute PA. These features were presented into different classifiers casted as a 4-class classification problem. We trained the model using the first 98 participants and assessed model performance and generalization on the remaining 50 participants. Using a random forest classifier, we demonstrated accurately estimating PA levels and sleep with 87% overall accuracy (F1-score=0.80) including 98.6% correct sleep detection. These findings processing the down-sampled actigraphy data to 10 Hz match or exceed state-of-art results recently reported in the literature achieved using considerably more sophisticated and time-consuming methods (including deep learning) which required actigraphy data sampled at 100 Hz. Collectively, these findings support the deployment of longitudinal, large-scale actigraphy data with sample rates as low as 10 Hz, towards accurately estimating personalized day-today PA and sleep profiles in healthcare community studies. INDEX TERMS 24-hour activity profile, actigraphy, Axivity AX3, CAPTURE-24, physical activity, sleep, smartwatch, wrist-worn wearable sensor

@article{
 title = {Insights into endometriosis symptom trajectories and assessment of surgical intervention outcomes using longitudinal actigraphy},
 type = {article},
 year = {2025},
 pages = {e236},
 volume = {8},
 publisher = {Springer US},
 id = {06516778-c675-3ec7-a9db-44a9e7ef548c},
 created = {2025-06-17T19:57:57.135Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2025-06-22T16:47:29.124Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Endometriosis is a common, chronic condition associated with debilitating pain, fatigue, and heterogeneous symptom presentation. In this exploratory study, 68 participants with confirmed endometriosis were monitored for up to three 4–6-week smartwatch cycles. We collected daily self-reports of pain and fatigue as well as retrospective questionnaires assessing quality of life, and we extracted daily measures of physical activity (PA), sleep, and diurnal rhythms from wrist-worn actigraphy data. We found that daily PA was strongly negatively correlated with self-reported fatigue (repeated measures correlations R<−0.3) and that participants with more severe or variable symptom trajectories displayed lower levels of PA, greater sleep disturbance, and more disrupted sleep and activity rhythms (Spearman’s |R|>0.3). Lastly, we found evidence of sleep and PA changes following surgery for endometriosis that reflected change in self-reported symptoms. Collectively, our findings suggest that passive data collection using wrist-worn wearables in endometriosis could facilitate individualized objective insights into symptom trajectories.},
 bibtype = {article},
 author = {Edgley, Katherine and Saunders, Philippa T.K. and Whitaker, Lucy H.R. and Horne, Andrew W. and Tsanas, Athanasios},
 doi = {10.1038/s41746-025-01629-8},
 journal = {npj Digital Medicine},
 number = {1}
}

Endometriosis is a common, chronic condition associated with debilitating pain, fatigue, and heterogeneous symptom presentation. In this exploratory study, 68 participants with confirmed endometriosis were monitored for up to three 4–6-week smartwatch cycles. We collected daily self-reports of pain and fatigue as well as retrospective questionnaires assessing quality of life, and we extracted daily measures of physical activity (PA), sleep, and diurnal rhythms from wrist-worn actigraphy data. We found that daily PA was strongly negatively correlated with self-reported fatigue (repeated measures correlations R<−0.3) and that participants with more severe or variable symptom trajectories displayed lower levels of PA, greater sleep disturbance, and more disrupted sleep and activity rhythms (Spearman’s |R|>0.3). Lastly, we found evidence of sleep and PA changes following surgery for endometriosis that reflected change in self-reported symptoms. Collectively, our findings suggest that passive data collection using wrist-worn wearables in endometriosis could facilitate individualized objective insights into symptom trajectories.

@article{
 title = {Statistical learning to identify and characterise neurodevelopmental outcomes at 2 years in babies born preterm: model development and validation using population-level data from England and Wales},
 type = {article},
 year = {2025},
 keywords = {birth cohorts,machine learning,neonatal,neurocognitive,neurodevelopmental impairments,preterm},
 pages = {105811},
 volume = {117},
 websites = {https://doi.org/10.1016/j.ebiom.2025.105811},
 publisher = {The Author(s)},
 id = {7505851f-1a18-3ecb-839b-4e492cc7ad9d},
 created = {2025-06-17T19:57:57.141Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2025-06-22T16:47:27.740Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Haider, Sadia and Tsanas, Athanasios and Batty, G David and Reynolds, Rebecca M and Whalley, Heather C and Cox, Simon R and Marioni, Riccardo E},
 doi = {10.1016/j.ebiom.2025.105811},
 journal = {eBioMedicine}
}

@article{
 title = {Temperature and Sleep Data Using Wrist-Worn Wearables},
 type = {article},
 year = {2023},
 keywords = {actigraphy,clinical decision support tool,sleep,stroke,wearable sensor},
 pages = {1069},
 volume = {23},
 websites = {https://www.mdpi.com/1424-8220/23/3/1069},
 id = {39143adb-c659-3491-a852-58b39c46c290},
 created = {2023-01-28T17:45:03.222Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-03-27T20:31:46.921Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Edgley, Katherine and Chun, Ho Yan Yvonne and Whiteley, William N. and Tsanas, Athanasios},
 doi = {https://doi.org/10.3390/s23031069},
 journal = {Sensors},
 number = {3}
}

@article{
 title = {Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects},
 type = {article},
 year = {2023},
 keywords = {chronic pain,digital technology,endometriosis,patient empowerment,smartphone app,smartwatch,symptom tracking,wearable technology},
 pages = {101192},
 volume = {4},
 websites = {https://doi.org/10.1016/j.xcrm.2023.101192},
 publisher = {The Authors},
 id = {acc8e9df-3c29-31cc-b211-31978a303634},
 created = {2023-09-19T18:05:44.156Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2023-09-19T18:05:47.553Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking. We postulate that these pervasive and increasingly affordable technologies present promising opportunities toward developing decision-support tools assisting healthcare professionals and empowering patients with endometriosis to make better-informed choices about symptom management.},
 bibtype = {article},
 author = {Edgley, Katherine and Horne, Andrew W. and Saunders, Philippa T.K. and Tsanas, Athanasios},
 doi = {10.1016/j.xcrm.2023.101192},
 journal = {Cell Reports Medicine},
 number = {9}
}

Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking. We postulate that these pervasive and increasingly affordable technologies present promising opportunities toward developing decision-support tools assisting healthcare professionals and empowering patients with endometriosis to make better-informed choices about symptom management.

@article{
 title = {Computational Approaches to Explainable Artificial Intelligence: Advances in Theory, Applications and Trends},
 type = {article},
 year = {2023},
 keywords = {C,Data science,Explainable Artificial Intelligence},
 pages = {101945},
 volume = {100},
 websites = {https://doi.org/10.1016/j.inffus.2023.101945},
 publisher = {Elsevier B.V.},
 id = {44f832c9-95d9-3baf-804a-4d17dbb4d8ea},
 created = {2023-09-19T18:05:44.178Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-03-25T08:34:44.472Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Gorriz, Juan and Álvarez-Illán, I and Álvarez-Marquina, Agustín and Arcoa, J E and Atzmueller, M and Ballarini, F and Barakova, Emilia and Bologna, Guido and Bonomini, María and Castellanos-Dominguez, German and Castillo-Barnes, Diego and Cho, S B and Contreras, Ricardo and Cuadra, J M and Domínguez, E and Mateos, Francisco and Duro, R J and Elizondo, D and Fernández-Caballero, Antonio and Ferrández, Jose},
 doi = {10.1016/j.inffus.2023.101945},
 journal = {Information Fusion}
}

@article{
 title = {Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions},
 type = {article},
 year = {2023},
 keywords = {Adherence,Asthma,Compliance,Corticosteroid,Electronic Health Records},
 pages = {167},
 volume = {23},
 websites = {https://doi.org/10.1186/s12874-023-01935-3},
 publisher = {BioMed Central},
 id = {46e03085-f414-319d-89c0-72fcc56cb4d2},
 created = {2023-09-19T18:05:44.192Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2023-10-28T20:28:01.018Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {BACKGROUND: Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study. METHODS: Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared. RESULTS: Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures. CONCLUSIONS: We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.},
 bibtype = {article},
 author = {Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios},
 doi = {10.1186/s12874-023-01935-3},
 journal = {BMC medical research methodology},
 number = {1}
}

BACKGROUND: Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study. METHODS: Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared. RESULTS: Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures. CONCLUSIONS: We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.

@article{
 title = {Relevance, redundancy, and complementarity trade- off (RRCT): a principled, generic, robust feature selection tool},
 type = {article},
 year = {2022},
 pages = {100471},
 volume = {3},
 websites = {https://doi.org/10.1016/j.patter.2022.100471},
 publisher = {The Author(s)},
 id = {31b19d73-68d2-3f64-a4fe-22234f47dd61},
 created = {2022-04-02T07:09:47.863Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2025-09-07T00:01:59.635Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios},
 doi = {10.1016/j.patter.2022.100471},
 journal = {Patterns}
}

@inproceedings{
 title = {Characterization of hypokinetic dysarthria using a convolutional neural netwok based on auditory receptive fields ⋆},
 type = {inproceedings},
 year = {2022},
 pages = {in press},
 city = {Tenerife, Canary Islands},
 id = {3b9df517-6713-340a-b6f0-fdfd17e8665a},
 created = {2022-04-02T07:18:50.303Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-04-02T07:21:55.047Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Gomez-Vilda, Pedro and Gomez-Rodellar, Andres and Palacios-Alonso, Daniel and Alvarez-Marquina, Agustin and Tsanas, Athanasios},
 booktitle = {IWINAC}
}

@inproceedings{
 title = {Characterizing Masseter Surface Electromyography on EEG-related Frequency Bands in Parkinson ’ s Disease Neuromotor},
 type = {inproceedings},
 year = {2022},
 keywords = {eases,eeg,entropy,hypokinetic dysarthria,neuromotor dis-,parkinson,s disease,surface electromyography},
 pages = {in press},
 city = {Tenerife, Canary Islands},
 id = {c87ef1d3-4cf0-33a5-b5dc-d4a8eb1096c9},
 created = {2022-04-02T07:18:50.303Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-04-02T07:19:05.420Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Gomez-Rodellar, Andres and Gomez-Vilda, Andres and Ferrandez-Vicente, Jose Manuel and Tsanas, Athanasios},
 booktitle = {IWINAC}
}

@inproceedings{
 title = {Estimating Medication Adherence from Electronic Health Records using Rolling Averages of Single Refill-based Estimates},
 type = {inproceedings},
 year = {2022},
 pages = {in press},
 city = {Glasgow, UK},
 id = {675b4a5a-171a-3baa-bacb-d24e8be69679},
 created = {2022-04-02T07:18:50.358Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-04-02T07:18:55.011Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios},
 booktitle = {IEEE EMBC}
}

@inproceedings{
 title = {Exploring feature selection and feature transformation techniques to improve telephone-based biomedical speech signal processing towards Parkinson ’ s assessment Acoustic Characterization of Sustained},
 type = {inproceedings},
 year = {2022},
 pages = {311-318},
 city = {Vienna, Austria},
 id = {1db1c696-3f11-32fa-9937-edb6777da1e0},
 created = {2022-04-02T07:25:09.652Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-04-02T07:27:02.858Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 booktitle = {BIOSIGNALS 2022 - 15th International Conference on Bio-Inspired Systems and Signal Processing; Part of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022}
}

@article{
 title = {Data driven subtyping of Parkinson’s using acoustic analysis of sustained vowels and cluster analysis: findings in the Parkinson’s voice initiative study},
 type = {article},
 year = {2022},
 keywords = {acoustic analysis,clustering,parkinson,s disease,s subtypes,sustained vowels},
 pages = {232},
 volume = {3},
 websites = {https://doi.org/10.1007/s42979-022-01123-y},
 publisher = {Springer Nature Singapore},
 id = {e6fe5415-83f8-3b47-87e7-dd74c506aca2},
 created = {2022-04-20T13:16:46.426Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2023-03-14T12:10:46.310Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 doi = {10.1007/s42979-022-01123-y},
 journal = {SN Computer Science}
}

@article{
 title = {Validation of the myocardial-ischemic-injury-index (MI3) machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population},
 type = {article},
 year = {2022},
 pages = {e300-e308},
 volume = {4},
 id = {15920fc5-4ee7-3dcf-a20a-576600a06c9f},
 created = {2022-04-21T05:15:52.369Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-03-17T06:13:16.567Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Doudesis, Dimitrios and Lee, Kuan Ken and Yang, Jason and Wereski, Ryan and Shah, Anoop S.V. and Tsanas, Athanasios and Anand, Atul and Pickering, John. W. and Than, Martin P. and Mills, Nicholas L. and Investigators, on behalf of the HIGH-STEACS},
 doi = {10.1016/S2589-7500(22)00025-5},
 journal = {Lancet Digital Health}
}

@article{
 title = {Investigating Wrist-Based Acceleration Summary Measures across Different Sample Rates towards 24-Hour Physical Activity and Sleep Profile Assessment},
 type = {article},
 year = {2022},
 keywords = {24-hour activity profile,actigraphy,activity,axivity ax3,metabolic equivalents,mets,physical,smartwatch,wrist-worn wearable sensor},
 pages = {6152},
 volume = {22},
 websites = {https://www.mdpi.com/1424-8220/22/16/6152/htm},
 id = {07d74e4b-00ef-3895-9559-43f0feeb7207},
 created = {2022-08-17T17:37:54.741Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-11-15T21:05:14.662Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios},
 doi = {https://doi.org/10.3390/s22166152},
 journal = {Sensors},
 number = {16}
}

@article{
 title = {An EMG-based Eating Behaviour Monitoring System with Haptic Feedback to Promote Mindful Eating},
 type = {article},
 year = {2022},
 keywords = {Eating behaviour monitoring,Haptic feedback,Mindful eating,Mobile and wearable devices,eating behaviour monitoring},
 pages = {106068},
 volume = {149},
 websites = {http://arxiv.org/abs/1907.10917},
 publisher = {Elsevier Ltd},
 id = {9190cc12-d630-3d98-bf04-38fca85e9c22},
 created = {2022-09-08T06:35:20.705Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-09-08T06:36:19.562Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.},
 bibtype = {article},
 author = {Nicholls, Ben and Ang, Chee Siang and Kanjo, Eiman and Siriaraya, Panote and Bafti, Saber Mirzaee and Yeo, Woon-Hong and Tsanas, Athanasios},
 doi = {10.1016/j.compbiomed.2022.106068},
 journal = {Computers in Biology and Medicine}
}

Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.

@article{
 title = {Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network},
 type = {article},
 year = {2021},
 pages = {11024-11036},
 volume = {9},
 id = {e32fadd6-c8f7-3d97-8fbf-0196f24099cf},
 created = {2021-02-02T17:37:36.591Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-02-02T17:37:43.369Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios and Little, Max A and Ramig, Lorraine O},
 doi = {10.1109/ACCESS.2021.3050524},
 journal = {IEEE Access}
}

@article{
 title = {A neuromotor to acoustical jaw-tongue projection model with application in Parkinson’s disease hypokinetic dysarthria},
 type = {article},
 year = {2021},
 keywords = {hypokinetic dysarthria,neuromechanics,neuromotor disorders,speech kinematics,surface electromyography},
 pages = {622825},
 volume = {15},
 id = {14d08147-ca7b-3e0a-a02c-79d519514e4c},
 created = {2021-03-15T10:23:42.726Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-09-14T20:08:29.891Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Gómez, Andrés and Gómez, Pedro and Palacios, Daniel and Rodellar, Victoria and Nieto, Víctor and Álvarez, Agustín and Tsanas, Athanasios},
 doi = {10.3389/fnhum.2021.622825},
 journal = {Frontiers in human neuroscience}
}

@article{
 title = {Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease},
 type = {article},
 year = {2021},
 pages = {44813-44824},
 volume = {9},
 websites = {https://ieeexplore.ieee.org/document/9349436},
 id = {bc7f15c5-10f4-3e5e-8626-f2dd9c93936a},
 created = {2021-03-15T10:46:42.193Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-08-15T07:43:16.349Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Speech impairment in Parkinson's Disease (PD) has been extensively studied. Our understanding of speech in people who are at an increased risk of developing PD is, however, rather limited. It is known that isolated Rapid Eye Movement (REM) sleep Behavior Disorder (RBD) is associated with a high risk of developing PD. The aim of this study is to investigate smartphone speech testing to: (1) distinguish participants with RBD from controls and PD, and (2) predict a range of self-or researcher-administered clinical scores that quantify participants' motor symptoms, cognition, daytime sleepiness, depression, and the overall state of health. The rationale of our analyses is to test an initial hypothesis that speech can be used to detect and quantify the symptoms associated with RBD and PD. We analyzed 4242 smartphone voice recordings collected in clinic and at home from 92 Controls, 112 RBD and 335 PD participants. We used acoustic signal analysis and machine learning, employing 337 features that quantify different properties of speech impairment. Using a leave-one-subject-out cross-validation scheme, we were able to distinguish RBD from controls (sensitivity 60.7%, specificity 69.6%) and RBD from PD participants (sensitivity 74.9%, specificity 73.2%), and predict clinical assessments with clinically useful accuracy. These promising findings warrant further investigation in using speech as a digital biomarker for PD and RBD to facilitate intervention in the early and prodromal stages of PD. INDEX TERMS Digital biomarkers, Parkinson's disease, REM sleep behavior disorder, speech analysis, statistical learning, smartphones, telemedicine.},
 bibtype = {article},
 author = {Arora, Siddharth and Lo, Christine and Hu, Michele and Tsanas, Athanasios},
 doi = {10.1109/ACCESS.2021.3057715},
 journal = {IEEE Access}
}

Speech impairment in Parkinson's Disease (PD) has been extensively studied. Our understanding of speech in people who are at an increased risk of developing PD is, however, rather limited. It is known that isolated Rapid Eye Movement (REM) sleep Behavior Disorder (RBD) is associated with a high risk of developing PD. The aim of this study is to investigate smartphone speech testing to: (1) distinguish participants with RBD from controls and PD, and (2) predict a range of self-or researcher-administered clinical scores that quantify participants' motor symptoms, cognition, daytime sleepiness, depression, and the overall state of health. The rationale of our analyses is to test an initial hypothesis that speech can be used to detect and quantify the symptoms associated with RBD and PD. We analyzed 4242 smartphone voice recordings collected in clinic and at home from 92 Controls, 112 RBD and 335 PD participants. We used acoustic signal analysis and machine learning, employing 337 features that quantify different properties of speech impairment. Using a leave-one-subject-out cross-validation scheme, we were able to distinguish RBD from controls (sensitivity 60.7%, specificity 69.6%) and RBD from PD participants (sensitivity 74.9%, specificity 73.2%), and predict clinical assessments with clinically useful accuracy. These promising findings warrant further investigation in using speech as a digital biomarker for PD and RBD to facilitate intervention in the early and prodromal stages of PD. INDEX TERMS Digital biomarkers, Parkinson's disease, REM sleep behavior disorder, speech analysis, statistical learning, smartphones, telemedicine.

@article{
 title = {Language function following preterm birth : prediction using machine learning},
 type = {article},
 year = {2021},
 pages = {1-10},
 websites = {http://dx.doi.org/10.1038/s41390-021-01779-x},
 publisher = {Springer US},
 id = {aa817790-b918-35e6-a86e-0ca36062c15e},
 created = {2021-10-12T08:43:27.039Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-13T13:55:14.304Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Valavani, Evdoxia and Blesa, Manuel and Galdi, Paola and Sullivan, Gemma and Dean, Bethan and Cruickshank, Hilary and Sitko-rudnicka, Magdalena and Bastin, Mark E and Chin, Richard F M and Macintyre, Donald J and Fletcher-watson, Sue and Boardman, James P and Tsanas, Athanasios},
 doi = {10.1038/s41390-021-01779-x},
 journal = {Pediatric Research}
}

@article{
 title = {Measuring and reporting treatment adherence: What can we learn by comparing two respiratory conditions?},
 type = {article},
 year = {2021},
 keywords = {adherence,asthma,compliance,persistence,tuberculosis},
 pages = {825-836},
 volume = {87},
 id = {76f78815-ca15-3553-992b-da30ee619b78},
 created = {2021-10-14T09:04:23.026Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:13.321Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Medication non-adherence, defined as any deviation from the regimen recommended by their healthcare provider, can increase morbidity, mortality and side effects, while reducing effectiveness. Through studying two respiratory conditions, asthma and tuberculosis (TB), we thoroughly review the current understanding of the measurement and reporting of medication adherence. In this paper, we identify major methodological issues in the standard ways that adherence has been conceptualised, defined and studied in asthma and TB. Between and within the two diseases there are substantial variations in adherence reporting, linked to differences in dosing intervals and treatment duration. Critically, the communicable nature of TB has resulted in dose-by-dose monitoring becoming a recommended treatment standard. Through the lens of these similarities and contrasts, we highlight contemporary shortcomings in the generalised conceptualisation of medication adherence. Furthermore, we outline elements in which knowledge could be directly transferred from one condition to the other, such as the application of large-scale cost-effective monitoring methods in TB to resource-poor settings in asthma. To develop a more robust evidence-based approach, we recommend the use of standard taxonomies detailed in the ABC taxonomy when measuring and discussing adherence. Regimen and intervention development and use should be based on sufficient evidence of the commonality and type of adherence behaviours displayed by patients with the relevant condition. A systematic approach to the measurement and reporting of adherence could improve the value and generalisability of research across all health conditions.},
 bibtype = {article},
 author = {Tibble, Holly and Flook, Mary and Sheikh, Aziz and Tsanas, Athanasios and Horne, Rob and Vrijens, Bernard and De Geest, Sabina and Stagg, Helen R.},
 doi = {10.1111/bcp.14458},
 journal = {British Journal of Clinical Pharmacology},
 number = {3}
}

Medication non-adherence, defined as any deviation from the regimen recommended by their healthcare provider, can increase morbidity, mortality and side effects, while reducing effectiveness. Through studying two respiratory conditions, asthma and tuberculosis (TB), we thoroughly review the current understanding of the measurement and reporting of medication adherence. In this paper, we identify major methodological issues in the standard ways that adherence has been conceptualised, defined and studied in asthma and TB. Between and within the two diseases there are substantial variations in adherence reporting, linked to differences in dosing intervals and treatment duration. Critically, the communicable nature of TB has resulted in dose-by-dose monitoring becoming a recommended treatment standard. Through the lens of these similarities and contrasts, we highlight contemporary shortcomings in the generalised conceptualisation of medication adherence. Furthermore, we outline elements in which knowledge could be directly transferred from one condition to the other, such as the application of large-scale cost-effective monitoring methods in TB to resource-poor settings in asthma. To develop a more robust evidence-based approach, we recommend the use of standard taxonomies detailed in the ABC taxonomy when measuring and discussing adherence. Regimen and intervention development and use should be based on sufficient evidence of the commonality and type of adherence behaviours displayed by patients with the relevant condition. A systematic approach to the measurement and reporting of adherence could improve the value and generalisability of research across all health conditions.

@inproceedings{
 title = {Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fluency distributions},
 type = {inproceedings},
 year = {2021},
 keywords = {Hypokinetic Dysarthria,Parkinson’s Disease,Speech Diadochokinetics},
 pages = {114-123},
 volume = {4},
 issue = {Biostec},
 id = {f4b5f272-13e1-3f4f-b6b7-b126fc36007c},
 created = {2021-10-14T09:04:23.328Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:58.357Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Hypokinetic Dysarthria (HD) is a major debilitating symptom in the vast majority of people diagnosed with Parkinson's Disease (PD). It has been traditionally evaluated using diadochokinetic exercises to estimate its degree of severity, among them, the fast repetition of monosyllables as [pa], [ta], and [ka] and multisyllable sequences as [pataka], [pakata], [badaga] and others alike. However, the real efficiency of these exercises in differentiating the participant behaviour as pathological or normative has not been investigated in depth. The aim of the present work is to explore the timely responsive performance of two of these exercises (a monosyllabic [ta] vs a multisyllabic [pataka]). A method to characterize statistically syllabic and inter-syllabic interval durations in the execution of these diadochokinetic exercises, based on Kolmogorov-Smirnov approximations and Jensen-Shannon Divergence has been used to assess the efficiency of both types of exercises. The results from the evaluation of 24 gender-balanced participants (12 PD and 12 controls) show that the monosyllabic exercise does not seem to differentiate well, whereas the multisyllabic exercise has a better differentiation performance. These findings, although relatively preliminary due to the limited sample size, underline the need to carefully consider the battery of tests towards assessing HD.},
 bibtype = {inproceedings},
 author = {Gómez-Vilda, Pedro and Gómez-Rodellar, Andrés and Palacios-Alonso, Daniel and Tsanas, Athanasios},
 doi = {10.5220/0010380301140123},
 booktitle = {BIOSIGNALS 2021 - 14th International Conference on Bio-Inspired Systems and Signal Processing; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021}
}

Hypokinetic Dysarthria (HD) is a major debilitating symptom in the vast majority of people diagnosed with Parkinson's Disease (PD). It has been traditionally evaluated using diadochokinetic exercises to estimate its degree of severity, among them, the fast repetition of monosyllables as [pa], [ta], and [ka] and multisyllable sequences as [pataka], [pakata], [badaga] and others alike. However, the real efficiency of these exercises in differentiating the participant behaviour as pathological or normative has not been investigated in depth. The aim of the present work is to explore the timely responsive performance of two of these exercises (a monosyllabic [ta] vs a multisyllabic [pataka]). A method to characterize statistically syllabic and inter-syllabic interval durations in the execution of these diadochokinetic exercises, based on Kolmogorov-Smirnov approximations and Jensen-Shannon Divergence has been used to assess the efficiency of both types of exercises. The results from the evaluation of 24 gender-balanced participants (12 PD and 12 controls) show that the monosyllabic exercise does not seem to differentiate well, whereas the multisyllabic exercise has a better differentiation performance. These findings, although relatively preliminary due to the limited sample size, underline the need to carefully consider the battery of tests towards assessing HD.

@article{
 title = {Acoustic to kinematic projection in Parkinson's disease dysarthria},
 type = {article},
 year = {2021},
 keywords = {Hypokinetic dysarthria,Neuromotor diseases,Remote monitoring,Speech articulation biomechanics,Speech kinematics,Speech neuromotor degeneration},
 pages = {e102422},
 volume = {66},
 id = {d3eef14b-199e-34c6-9673-0421d82a83a1},
 created = {2021-10-14T09:04:23.349Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:23.349Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2021 Elsevier Ltd Speech signal analysis is a powerful tool that facilitates the monitoring and tracking of symptom deterioration caused by neurodegenerative disorders, typically achieved using either sustained vowels, diadochokinetic exercises or running speech. This study expands our previous work on the study of the movement produced by the jaw-tongue biomechanical system. The aim is to further investigate the effects of neuromotor activity during muscular exertion that translates formant acoustics into speech articulatory movements affected by hypokinetic dysarthria in Parkinson's Disease (PD). The objective of this study is to estimate the parameters of an inverse acoustic-to-kinematic projection model that takes as an input the variations of the first and second formants and estimates as output the spatial variation of the jaw-tongue biomechanical system. The spatial variations have been extracted from 3D accelerometry (3DAcc). These serve as ground truth for comparison with the estimated activity projected from speech kinematics, as a measure of fitness of the inverse model. The estimation method is a two step process: first initial weight values are produced using multiple regression between each of the formant dynamic signals (acoustical analysis) and the estimated spatial variations (accelerometry). The second step uses a weight refinement method based on gradient-descent. Additionally, a time-realignment study has been carried out on the acoustic-to-kinematic projection model, based on the estimation of relative time displacements as to maximize the cross-correlation between signals. The study is complemented with an estimation of the model weights on a dataset from PD participants and Healthy Controls (HC). This methodology opens up new ways to investigate the underlying physiological voice production mechanism which may offer new insights into PD symptoms.},
 bibtype = {article},
 author = {Gómez, A. and Tsanas, A. and Gómez, P. and Palacios-Alonso, D. and Rodellar, V. and Álvarez, A.},
 doi = {10.1016/j.bspc.2021.102422},
 journal = {Biomedical Signal Processing and Control}
}

© 2021 Elsevier Ltd Speech signal analysis is a powerful tool that facilitates the monitoring and tracking of symptom deterioration caused by neurodegenerative disorders, typically achieved using either sustained vowels, diadochokinetic exercises or running speech. This study expands our previous work on the study of the movement produced by the jaw-tongue biomechanical system. The aim is to further investigate the effects of neuromotor activity during muscular exertion that translates formant acoustics into speech articulatory movements affected by hypokinetic dysarthria in Parkinson's Disease (PD). The objective of this study is to estimate the parameters of an inverse acoustic-to-kinematic projection model that takes as an input the variations of the first and second formants and estimates as output the spatial variation of the jaw-tongue biomechanical system. The spatial variations have been extracted from 3D accelerometry (3DAcc). These serve as ground truth for comparison with the estimated activity projected from speech kinematics, as a measure of fitness of the inverse model. The estimation method is a two step process: first initial weight values are produced using multiple regression between each of the formant dynamic signals (acoustical analysis) and the estimated spatial variations (accelerometry). The second step uses a weight refinement method based on gradient-descent. Additionally, a time-realignment study has been carried out on the acoustic-to-kinematic projection model, based on the estimation of relative time displacements as to maximize the cross-correlation between signals. The study is complemented with an estimation of the model weights on a dataset from PD participants and Healthy Controls (HC). This methodology opens up new ways to investigate the underlying physiological voice production mechanism which may offer new insights into PD symptoms.

@inproceedings{
 title = {Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s voice initiative study},
 type = {inproceedings},
 year = {2021},
 keywords = {Acoustic Analysis,Clustering,Parkinson’s Disease,Parkinson’s Voice Initiative (PVI)},
 pages = {124-131},
 id = {5ca72a8e-01f3-3478-94e2-0a5828a97647},
 created = {2021-10-14T09:04:23.490Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-02-16T20:29:44.769Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Progress in exploring speech and Parkinson’s Disease (PD) has been hindered due to the use of different protocols across research labs/countries, single-site studies with relatively small numbers, and no external validation. We had recently reported on the Parkinson’s Voice Initiative (PVI), a large study where we collected 19,000+ sustained vowel phonations (control and PD groups) across seven countries, under acoustically non-controlled conditions. In this study, we explored how well findings generalize in the three English-speaking PVI cohorts (data collected in Boston, Oxford, and Toronto). We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We used the previously identified feature subset from the Boston cohort and explored hierarchical clustering with Ward’s linkage combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. Furthermore, we computed feature weights using LOGO to assess feature selection consistency towards differentiating PD from controls. Overall, findings are very consistent across the three cohorts, strongly suggesting the presence of four main PD clusters, and consistent identification of key contributing features. Collectively, these findings support the generalization of sustained vowels and robustness of the presented methodology across the English-speaking PVI cohorts.},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 doi = {10.5220/0010383001240131},
 booktitle = {BIOSIGNALS 2021 - 14th International Conference on Bio-Inspired Systems and Signal Processing; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021}
}

Progress in exploring speech and Parkinson’s Disease (PD) has been hindered due to the use of different protocols across research labs/countries, single-site studies with relatively small numbers, and no external validation. We had recently reported on the Parkinson’s Voice Initiative (PVI), a large study where we collected 19,000+ sustained vowel phonations (control and PD groups) across seven countries, under acoustically non-controlled conditions. In this study, we explored how well findings generalize in the three English-speaking PVI cohorts (data collected in Boston, Oxford, and Toronto). We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We used the previously identified feature subset from the Boston cohort and explored hierarchical clustering with Ward’s linkage combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. Furthermore, we computed feature weights using LOGO to assess feature selection consistency towards differentiating PD from controls. Overall, findings are very consistent across the three cohorts, strongly suggesting the presence of four main PD clusters, and consistent identification of key contributing features. Collectively, these findings support the generalization of sustained vowels and robustness of the presented methodology across the English-speaking PVI cohorts.

@article{
 title = {Eye-tracking for longitudinal assessment of social cognition in children born preterm},
 type = {article},
 year = {2021},
 keywords = {Social cognition,development,eye gaze,prematurity},
 pages = {470-480},
 volume = {62},
 id = {195dea31-948d-3a21-98b2-6e2b1a2a58aa},
 created = {2021-10-14T09:04:23.500Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:15.922Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Background and objectives: Preterm birth is associated with atypical social cognition in infancy, and cognitive impairment and social difficulties in childhood. Little is known about the stability of social cognition through childhood, and its relationship with neurodevelopment. We used eye-tracking in preterm and term-born infants to investigate social attentional preference in infancy and at 5 years, its relationship with neurodevelopment and the influence of socioeconomic deprivation. Methods: A cohort of 81 preterm and 66 term infants with mean (range) gestational age at birth 28+5 (23+2–33+0) and 40+0 (37+0–42+1) respectively, completed eye-tracking at 7–9 months, with a subset re-assessed at 5 years. Three free-viewing social tasks of increasing stimulus complexity were presented, and a social preference score was derived from looking time to socially informative areas. Socioeconomic data and the Mullen Scales of Early Learning at 5 years were collected. Results: Preterm children had lower social preference scores at 7–9 months compared with term-born controls. Term-born children’s scores were stable between time points, whereas preterm children showed a significant increase, reaching equivalent scores by 5 years. Low gestational age and socioeconomic deprivation were associated with reduced social preference scores at 7–9 months. At 5 years, preterm infants had lower Early Learning Composite scores than controls, but this was not associated with social attentional preference in infancy or at 5 years. Conclusions: Preterm children have reduced social attentional preference at 7–9 months compared with term-born controls, but catch up by 5 years. Infant social cognition is influenced by socioeconomic deprivation and gestational age. Social cognition and neurodevelopment have different trajectories following preterm birth.},
 bibtype = {article},
 author = {Dean, Bethan and Ginnell, Lorna and Ledsham, Victoria and Tsanas, Athanasios and Telford, Emma and Sparrow, Sarah and Fletcher-Watson, Sue and Boardman, James P.},
 doi = {10.1111/jcpp.13304},
 journal = {Journal of Child Psychology and Psychiatry and Allied Disciplines},
 number = {4}
}

Background and objectives: Preterm birth is associated with atypical social cognition in infancy, and cognitive impairment and social difficulties in childhood. Little is known about the stability of social cognition through childhood, and its relationship with neurodevelopment. We used eye-tracking in preterm and term-born infants to investigate social attentional preference in infancy and at 5 years, its relationship with neurodevelopment and the influence of socioeconomic deprivation. Methods: A cohort of 81 preterm and 66 term infants with mean (range) gestational age at birth 28+5 (23+2–33+0) and 40+0 (37+0–42+1) respectively, completed eye-tracking at 7–9 months, with a subset re-assessed at 5 years. Three free-viewing social tasks of increasing stimulus complexity were presented, and a social preference score was derived from looking time to socially informative areas. Socioeconomic data and the Mullen Scales of Early Learning at 5 years were collected. Results: Preterm children had lower social preference scores at 7–9 months compared with term-born controls. Term-born children’s scores were stable between time points, whereas preterm children showed a significant increase, reaching equivalent scores by 5 years. Low gestational age and socioeconomic deprivation were associated with reduced social preference scores at 7–9 months. At 5 years, preterm infants had lower Early Learning Composite scores than controls, but this was not associated with social attentional preference in infancy or at 5 years. Conclusions: Preterm children have reduced social attentional preference at 7–9 months compared with term-born controls, but catch up by 5 years. Infant social cognition is influenced by socioeconomic deprivation and gestational age. Social cognition and neurodevelopment have different trajectories following preterm birth.

@article{
 title = {Smartphone ‑ recorded physical activity for estimating cardiorespiratory fitness},
 type = {article},
 year = {2021},
 pages = {14851},
 volume = {11},
 websites = {https://doi.org/10.1038/s41598-021-94164-x},
 publisher = {Nature Publishing Group UK},
 id = {4fbd7b80-7968-3807-bb4b-e97318b9fe61},
 created = {2021-10-14T09:04:23.593Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:35.854Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Eades, Micah T and Tsanas, Athanasios and Juraschek, Stephen P and Kramer, Daniel B and Gervino, Ernest and Mukamal, Kenneth J},
 doi = {10.1038/s41598-021-94164-x},
 journal = {Scientific Reports}
}

@article{
 title = {Assessing Parkinson’s disease at scale using telephone-recorded speech: insights from the Parkinson’s Voice Initiative},
 type = {article},
 year = {2021},
 keywords = {acoustic measures,biomarker,clinical decision support tool,dysphonia measures,parkinson,s disease,sustained vowel phonations,telemonitoring},
 pages = {e1892},
 volume = {11},
 id = {6c604d81-9650-3b40-8802-fb216cae34d0},
 created = {2021-10-14T09:04:23.622Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-16T11:08:15.718Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Arora, Siddharth and Tsanas, Athanasios},
 doi = {https://doi.org/10.3390/ diagnostics11101892},
 journal = {Diagnostics},
 number = {10}
}

@article{
 title = {Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review},
 type = {article},
 year = {2021},
 pages = {157},
 volume = {4},
 publisher = {Springer US},
 id = {1ac68032-d696-3745-8b48-7059f550f8ca},
 created = {2024-03-16T14:59:50.691Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-03-16T14:59:52.482Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Complications following surgery are common and frequently occur the following discharge. Mobile and wearable digital health interventions (DHI) provide an opportunity to monitor and support patients during their postoperative recovery. Lack of high-quality evidence is often cited as a barrier to DHI implementation. This review captures and appraises the current use, evidence base and reporting quality of mobile and wearable DHI following surgery. Keyword searches were performed within Embase, Cochrane Library, Web of Science and WHO Global Index Medicus databases, together with clinical trial registries and Google scholar. Studies involving patients undergoing any surgery requiring skin incision where postoperative outcomes were measured using a DHI following hospital discharge were included, with DHI defined as mobile and wireless technologies for health to improve health system efficiency and health outcomes. Methodological reporting quality was determined using the validated mobile health evidence reporting and assessment (mERA) guidelines. Bias was assessed using the Cochrane Collaboration tool for randomised studies or MINORS depending on study type. Overall, 6969 articles were screened, with 44 articles included. The majority (n = 34) described small prospective study designs, with a high risk of bias demonstrated. Reporting standards were suboptimal across all domains, particularly in relation to data security, prior patient engagement and cost analysis. Despite the potential of DHI to improve postoperative patient care, current progress is severely restricted by limitations in methodological reporting. There is an urgent need to improve reporting for DHI following surgery to identify patient benefit, promote reproducibility and encourage sustainability.},
 bibtype = {article},
 author = {Knight, Stephen R. and Ng, Nathan and Tsanas, Athanasios and Mclean, Kenneth and Pagliari, Claudia and Harrison, Ewen M.},
 doi = {10.1038/s41746-021-00525-1},
 journal = {npj Digital Medicine},
 number = {1}
}

Complications following surgery are common and frequently occur the following discharge. Mobile and wearable digital health interventions (DHI) provide an opportunity to monitor and support patients during their postoperative recovery. Lack of high-quality evidence is often cited as a barrier to DHI implementation. This review captures and appraises the current use, evidence base and reporting quality of mobile and wearable DHI following surgery. Keyword searches were performed within Embase, Cochrane Library, Web of Science and WHO Global Index Medicus databases, together with clinical trial registries and Google scholar. Studies involving patients undergoing any surgery requiring skin incision where postoperative outcomes were measured using a DHI following hospital discharge were included, with DHI defined as mobile and wireless technologies for health to improve health system efficiency and health outcomes. Methodological reporting quality was determined using the validated mobile health evidence reporting and assessment (mERA) guidelines. Bias was assessed using the Cochrane Collaboration tool for randomised studies or MINORS depending on study type. Overall, 6969 articles were screened, with 44 articles included. The majority (n = 34) described small prospective study designs, with a high risk of bias demonstrated. Reporting standards were suboptimal across all domains, particularly in relation to data security, prior patient engagement and cost analysis. Despite the potential of DHI to improve postoperative patient care, current progress is severely restricted by limitations in methodological reporting. There is an urgent need to improve reporting for DHI following surgery to identify patient benefit, promote reproducibility and encourage sustainability.

@article{
 title = {Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder},
 type = {article},
 year = {2020},
 keywords = {actigraphy,geneactiv,posttraumatic stress disorder,sleep,wearable technology},
 pages = {e14306},
 volume = {8},
 id = {1afa1650-bb20-39fc-b523-0d79f62cece0},
 created = {2020-04-30T12:11:42.294Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-06-03T13:52:31.134Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios and Woodward, Elizabeth and Ehlers, Anke},
 doi = {10.2196/14306},
 journal = {JMIR mHealth and uHealth},
 number = {4}
}

@article{
 title = {Artificial intelligence within the interplay between natural and artificial Computation : advances in data science , trends and applications},
 type = {article},
 year = {2020},
 pages = {237-270},
 volume = {410},
 id = {19c309d4-f6de-3739-837a-d1bc1cc13765},
 created = {2020-05-27T09:42:18.944Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-03-15T10:46:42.428Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Juan, M G and Ram, Javier and Suckling, John and Leming, Matthew and Zhang, Yu-dong and Ram, Jose and Bonomini, Paula and Casado, Fernando E and Charte, David and Charte, Francisco and Contreras, Ricardo and Duro, Richard J and Fern, Antonio and Mart, Rafael},
 journal = {Neurocomputing}
}

@article{
 title = {Beyond mobile apps: a survey of technologies for mental well-being},
 type = {article},
 year = {2020},
 keywords = {Diagnosis or assessment,Machine learning,Pervasive computing,Physiological Measures,Ubiquitous computing},
 volume = {(in press)},
 websites = {https://ieeexplore.ieee.org/document/9162435},
 id = {98ee39a3-baa2-3d82-92ca-d40c25c31208},
 created = {2020-11-01T23:59:00.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-03-12T09:24:13.340Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {true},
 abstract = {Copyright © 2019, arXiv, All rights reserved. Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.},
 bibtype = {article},
 author = {Woodward, K. and Kanjo, E. and Brown, D. and McGinnity, T.M. and Inkster, B. and Macintyre, D.J. and Tsanas, A.},
 doi = {10.1109/TAFFC.2020.3015018},
 journal = {IEEE Transactions Affective Computing}
}

Copyright © 2019, arXiv, All rights reserved. Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.

@article{
 title = {Telemedicine cognitive behavioral therapy for anxiety after stroke: proof-of-concept randomized controlled trial},
 type = {article},
 year = {2020},
 keywords = {anxiety,psychotherapy,stroke,telemedicine,workflow},
 pages = {2297-2306},
 volume = {51},
 id = {e6fe6845-18f8-39eb-8d48-85b20cf42c46},
 created = {2021-06-03T10:58:48.816Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-06-03T19:35:57.550Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Background and Purpose: Disabling anxiety affects a quarter of stroke survivors but access to treatment is poor. We developed a telemedicine model for delivering guided self-help cognitive behavioral therapy (CBT) for anxiety after stroke (TASK-CBT). We aimed to evaluate the feasibility of TASK-CBT in a randomized controlled trial workflow that enabled all trial procedures to be carried out remotely. In addition, we explored the feasibility of wrist-worn actigraphy sensor as a way of measuring objective outcomes in this clinical trial. Methods: We recruited adult community-based stroke patients (n=27) and randomly allocated them to TASK-CBT (n=14) or relaxation therapy (TASK-Relax), an active comparator (n=13). Results: In our sample (mean age 65 [±10]; 56% men; 63% stroke, 37% transient ischemic attacks), remote self-enrolment, electronic signature, intervention delivery, and automated follow-up were feasible. All participants completed all TASK-CBT sessions (14/14). Lower levels of anxiety were observed in TASK-CBT when compared with TASK-Relax at both weeks 6 and 20. Mean actigraphy sensor wearing-time was 33 days (±15). Conclusions: Our preliminary feasibility data from the current study support a larger definitive clinical trial and the use of wrist-worn actigraphy sensor in anxious stroke survivors. Registration: URL: Https://www.clinicaltrials.gov. Unique identifier: NCT03439813.},
 bibtype = {article},
 author = {Chun, Ho Yan Yvonne and Carson, Alan J. and Tsanas, Athanasios and Dennis, Martin S. and Mead, Gillian E. and Calabria, Clementina and Whiteley, William N.},
 doi = {10.1161/STROKEAHA.120.029042},
 journal = {Stroke},
 number = {8}
}

Background and Purpose: Disabling anxiety affects a quarter of stroke survivors but access to treatment is poor. We developed a telemedicine model for delivering guided self-help cognitive behavioral therapy (CBT) for anxiety after stroke (TASK-CBT). We aimed to evaluate the feasibility of TASK-CBT in a randomized controlled trial workflow that enabled all trial procedures to be carried out remotely. In addition, we explored the feasibility of wrist-worn actigraphy sensor as a way of measuring objective outcomes in this clinical trial. Methods: We recruited adult community-based stroke patients (n=27) and randomly allocated them to TASK-CBT (n=14) or relaxation therapy (TASK-Relax), an active comparator (n=13). Results: In our sample (mean age 65 [±10]; 56% men; 63% stroke, 37% transient ischemic attacks), remote self-enrolment, electronic signature, intervention delivery, and automated follow-up were feasible. All participants completed all TASK-CBT sessions (14/14). Lower levels of anxiety were observed in TASK-CBT when compared with TASK-Relax at both weeks 6 and 20. Mean actigraphy sensor wearing-time was 33 days (±15). Conclusions: Our preliminary feasibility data from the current study support a larger definitive clinical trial and the use of wrist-worn actigraphy sensor in anxious stroke survivors. Registration: URL: Https://www.clinicaltrials.gov. Unique identifier: NCT03439813.

@article{
 title = {Large-scale clustering of people diagnosed with Parkinson’s disease using acoustic analysis of sustained vowels: Findings in the Parkinson’s voice initiative study},
 type = {article},
 year = {2020},
 keywords = {Acoustic Analysis,Clustering,Parkinson’s Disease,Parkinson’s Voice Initiative (PVI)},
 pages = {369-376},
 id = {b59bcab2-ac13-3032-b55c-8d899f71bc68},
 created = {2021-08-12T05:43:08.234Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-08-12T05:43:12.881Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The heterogeneity of symptoms in Parkinson’s Disease (PD) has motivated investigating PD subtypes using cluster analysis techniques. Previous studies investigating PD clustering have typically focused on symptoms assessed using standardized clinical evaluations and patient reported outcome measures. Here, we explore PD subtype delineation using speech signals. We used data from the recently concluded Parkinson’s Voice Initiative (PVI) study where sustained vowels were solicited and collected under non-controlled acoustic conditions. We acoustically characterized 2097 sustained vowel /a/ recordings from 1138 PD participants using 307 dysphonia measures which had previously been successfully used in applications including differentiating healthy controls from PD participants, and matching speech dysphonia to the standard PD clinical metric quantifying symptom severity. We applied unsupervised feature selection to obtain a concise subset of the originally computed dysphonia measures and explored hierarchical clustering combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. We computed four main clusters which provide tentative insights into different dominating speech-associated pathologies. Collectively, these findings provide new insights into the nature of PD towards exploring speech-PD data-driven subtyping.},
 bibtype = {article},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 doi = {10.5220/0009361203690376},
 journal = {BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020}
}

The heterogeneity of symptoms in Parkinson’s Disease (PD) has motivated investigating PD subtypes using cluster analysis techniques. Previous studies investigating PD clustering have typically focused on symptoms assessed using standardized clinical evaluations and patient reported outcome measures. Here, we explore PD subtype delineation using speech signals. We used data from the recently concluded Parkinson’s Voice Initiative (PVI) study where sustained vowels were solicited and collected under non-controlled acoustic conditions. We acoustically characterized 2097 sustained vowel /a/ recordings from 1138 PD participants using 307 dysphonia measures which had previously been successfully used in applications including differentiating healthy controls from PD participants, and matching speech dysphonia to the standard PD clinical metric quantifying symptom severity. We applied unsupervised feature selection to obtain a concise subset of the originally computed dysphonia measures and explored hierarchical clustering combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. We computed four main clusters which provide tentative insights into different dominating speech-associated pathologies. Collectively, these findings provide new insights into the nature of PD towards exploring speech-PD data-driven subtyping.

@article{
 title = {Data-driven insights towards risk assessment of postpartum depression},
 type = {article},
 year = {2020},
 keywords = {Feature Selection,Postpartum Depression,Random Forests},
 pages = {382-389},
 id = {312c38de-0730-3b6e-ad9f-e3d468cfb839},
 created = {2021-08-12T05:43:08.244Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-08-12T05:43:11.066Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Postpartum depression is defined as depressive episodes that occur during pregnancy or within 12 months of parturition. The goal of this study is the exploration of the birth features and maternal traits which affect the risk of postpartum depression for mothers with preterm neonates. We analysed data from 144 women (63 mothers of term and 81 mothers of preterm infants) who completed the Edinburgh Postnatal Depression Scale (EPDS) in the postpartum period. We used three feature selection algorithms: ReliefF, Random Forests (RF) variable importance, and Boruta, in order to select the most predictive feature subsets, which were subsequently mapped onto the binarized EPDS total score (a threshold of 10 was used to binarize the EPDS total scores) using RF. We found that positive affectivity (rs=-0.467, p<0.001), and the Apgar score at 5 minutes (rs=-0.430, p<0.001) are the most statistically strongly associated features with the risk of postpartum depression. We used 10-fold cross-validation with 100 iterations and report out-of-sample balanced accuracy (median±IQR): 75.0±16.7, sensitivity: 66.7±16.7, specificity: 100±16.7, and F1 score: 0.8±0.2. Collectively, these findings highlight the potential of using a data-driven process to automate risk prediction using standard clinical characteristics and motivate the deployment of the developed tool using larger-scale datasets.},
 bibtype = {article},
 author = {Valavani, Evdoxia and Doudesis, Dimitrios and Kourtesis, Ioannis and Chin, Richard F.M. and MacIntyre, Donald J. and Fletcher-Watson, Sue and Boardman, James P. and Tsanas, Athanasios},
 doi = {10.5220/0009369303820389},
 journal = {BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020}
}

Postpartum depression is defined as depressive episodes that occur during pregnancy or within 12 months of parturition. The goal of this study is the exploration of the birth features and maternal traits which affect the risk of postpartum depression for mothers with preterm neonates. We analysed data from 144 women (63 mothers of term and 81 mothers of preterm infants) who completed the Edinburgh Postnatal Depression Scale (EPDS) in the postpartum period. We used three feature selection algorithms: ReliefF, Random Forests (RF) variable importance, and Boruta, in order to select the most predictive feature subsets, which were subsequently mapped onto the binarized EPDS total score (a threshold of 10 was used to binarize the EPDS total scores) using RF. We found that positive affectivity (rs=-0.467, p<0.001), and the Apgar score at 5 minutes (rs=-0.430, p<0.001) are the most statistically strongly associated features with the risk of postpartum depression. We used 10-fold cross-validation with 100 iterations and report out-of-sample balanced accuracy (median±IQR): 75.0±16.7, sensitivity: 66.7±16.7, specificity: 100±16.7, and F1 score: 0.8±0.2. Collectively, these findings highlight the potential of using a data-driven process to automate risk prediction using standard clinical characteristics and motivate the deployment of the developed tool using larger-scale datasets.

@inproceedings{
 title = {Assessing preferred proximity between different types of embryonic stem cells},
 type = {inproceedings},
 year = {2020},
 keywords = {Different Types of Stem Cells,Embryonic Stem Cells,Minimum Spanning Tree,Statistical Analysis},
 pages = {377-381},
 id = {a5e73a47-0cbf-3522-8878-92fff6d68948},
 created = {2021-10-14T09:04:23.180Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:11.003Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Embryonic stem cells (ESCs) studies play an important role for understanding the molecular events that underlie cell lineage commitment and serve as models for the development of disease. However, the interactions between neighboring embryonic stem cells are not fully understood. Assessing proximity between different types of embryonic stem cells might provide more information about distinct behaviors of embryonic stem cells. In this study, we processed 186 cell colonies on disc constrained microdomains and 152 cell colonies on ellipse. We grouped cell colonies based on different observed patterns and grouped cells by their locations. By applying two measurements on embryonic stem cell colonies, minimum spanning tree and average distance to the five closest objects, we investigated the difference of proximity between different types of embryonic stem cells, the difference between grouped cell colonies and the difference between grouped cells. We found one type of ESC has a smaller average path based on minimum spanning tree and higher proximity than the other type. We report consistent results for different types of embryonic stem cells: these findings may be useful to set benchmarks for empirical models which replicate ESC behaviors.},
 bibtype = {inproceedings},
 author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},
 doi = {10.5220/0009368903770381},
 booktitle = {BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020}
}

Embryonic stem cells (ESCs) studies play an important role for understanding the molecular events that underlie cell lineage commitment and serve as models for the development of disease. However, the interactions between neighboring embryonic stem cells are not fully understood. Assessing proximity between different types of embryonic stem cells might provide more information about distinct behaviors of embryonic stem cells. In this study, we processed 186 cell colonies on disc constrained microdomains and 152 cell colonies on ellipse. We grouped cell colonies based on different observed patterns and grouped cells by their locations. By applying two measurements on embryonic stem cell colonies, minimum spanning tree and average distance to the five closest objects, we investigated the difference of proximity between different types of embryonic stem cells, the difference between grouped cell colonies and the difference between grouped cells. We found one type of ESC has a smaller average path based on minimum spanning tree and higher proximity than the other type. We report consistent results for different types of embryonic stem cells: these findings may be useful to set benchmarks for empirical models which replicate ESC behaviors.

@article{
 title = {Challenges of clustering multimodal clinical data: Review of applications in asthma subtyping},
 type = {article},
 year = {2020},
 keywords = {Asthma,Cluster analysis,Data mining,Machine learning,Unsupervised machine learning},
 pages = {e16452},
 volume = {8},
 id = {892f86ab-67a6-32b2-83fb-295de994fa1b},
 created = {2021-10-14T09:04:23.234Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2024-03-31T05:40:13.856Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Background: In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. Objective: This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. Methods: We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. Results: Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. Conclusions: This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.},
 bibtype = {article},
 author = {Horne, Elsie and Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios},
 doi = {10.2196/16452},
 journal = {JMIR Medical Informatics},
 number = {5}
}

Background: In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. Objective: This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. Methods: We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. Results: Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. Conclusions: This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.

@article{
 title = {Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model},
 type = {article},
 year = {2020},
 pages = {e16209},
 volume = {10},
 month = {12},
 publisher = {Nature Research},
 day = {1},
 id = {a814bdff-d0b1-320b-8703-a0082adf45f3},
 created = {2021-10-14T09:04:23.264Z},
 accessed = {2021-05-13},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:03.958Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments.},
 bibtype = {article},
 author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},
 doi = {10.1038/s41598-020-73228-4},
 journal = {Scientific Reports},
 number = {1}
}

The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments.

@article{
 title = {Artificial intelligence within the interplay between natural and artificial Computation : advances in data science , trends and applications},
 type = {article},
 year = {2020},
 pages = {237-270},
 volume = {410},
 id = {590715a5-3102-389f-b4ba-5d64cc1f0e52},
 created = {2021-10-14T09:04:23.316Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:48.707Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Juan, M G and Ram, Javier and Suckling, John and Leming, Matthew and Zhang, Yu-dong and Ram, Jose and Bonomini, Paula and Casado, Fernando E and Charte, David and Charte, Francisco and Contreras, Ricardo and Duro, Richard J and Fern, Antonio and Mart, Rafael},
 journal = {Neurocomputing}
}

@article{
 title = {A data-driven typology of asthma medication adherence using cluster analysis},
 type = {article},
 year = {2020},
 pages = {e14999},
 volume = {10},
 websites = {https://doi.org/10.1038/s41598-020-72060-0},
 publisher = {Nature Publishing Group UK},
 id = {7f0f32be-eaf0-35a5-a95b-6ecb66ecf00e},
 created = {2021-10-14T09:04:23.411Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:25.561Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).},
 bibtype = {article},
 author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Elsie and Doudesis, Dimitrios and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},
 doi = {10.1038/s41598-020-72060-0},
 journal = {Scientific Reports},
 number = {1}
}

Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).

@article{
 title = {Linkage of primary care prescribing records and pharmacy dispensing Records in the Salford Lung Study: application in asthma},
 type = {article},
 year = {2020},
 pages = {e303},
 volume = {20},
 publisher = {BMC Medical Research Methodology},
 id = {cb6abeb1-264e-3aab-94d6-6f45f8738068},
 created = {2021-10-14T09:04:23.533Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:17.452Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Background: Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence, which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence; medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward. Methods: We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology. Results: We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched – approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence). Conclusions: We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.},
 bibtype = {article},
 author = {Tibble, Holly and Lay-Flurrie, James and Sheikh, Aziz and Horne, Rob and Mizani, Mehrdad A. and Tsanas, Athanasios},
 doi = {10.1186/s12874-020-01184-8},
 journal = {BMC Medical Research Methodology},
 number = {1}
}

Background: Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence, which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence; medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward. Methods: We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology. Results: We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched – approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence). Conclusions: We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.

@inproceedings{
 title = {Parkinson’s Disease Glottal Source Characterization: Phonation Feature Distributions vs Amplitude Probability Density Functions},
 type = {inproceedings},
 year = {2020},
 pages = {359-368},
 city = {Valetta, Malta},
 id = {652900a2-2fff-3a4d-974a-e4895ac3438e},
 created = {2021-10-14T09:04:23.638Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:32.729Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Álvarez, Agustín and Palacios, Daniel},
 booktitle = {BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020}
}

@inproceedings{
 title = {New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity},
 type = {inproceedings},
 year = {2019},
 pages = {(in press)},
 id = {7a794db4-f391-3711-ab59-256978475684},
 created = {2019-04-17T09:13:10.792Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2019-08-09T15:31:55.339Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios},
 booktitle = {41st IEEE Engineering in Medicine and Biology Conference}
}

@misc{
 title = {An EMG-based eating behaviour monitoring system with haptic feedback to promote mindful eating},
 type = {misc},
 year = {2019},
 source = {arXiv},
 keywords = {Eating behaviour monitoring,Haptic feedback,Mindful eating,Mobile and wearable devices},
 id = {bf07925b-f3fb-3afb-bb68-b37b6bbbe872},
 created = {2020-11-03T23:59:00.000Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2020-11-04T13:23:04.248Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {Copyright © 2019, arXiv, All rights reserved. Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38) = 58.243, p <0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.},
 bibtype = {misc},
 author = {Nicholls, B. and Ang, C.S. and Eiman, K. and Siriaraya, P. and Yeo, W.-H. and Tsanas, A.}
}

Copyright © 2019, arXiv, All rights reserved. Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38) = 58.243, p <0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.

@book{
 title = {Assessing an Application of Spontaneous Stressed Speech - Emotions Portal},
 type = {book},
 year = {2019},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Characterizing stress,Cooperative framework,Data acquisition,Emotional stress,Stress behavior in human-computer interaction},
 volume = {11486 LNCS},
 id = {9fdf26ec-9b41-3813-8a29-5be7fd5efab1},
 created = {2019-05-29T23:59:00.000Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2020-12-23T11:40:06.905Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019, Springer Nature Switzerland AG. Detecting and identifying emotions expressed in speech signals is a very complex task that generally requires processing a large sample size to extract intricate details and match the diversity of human expression in speech. There is not an emotional dataset commonly accepted as a standard test bench to evaluate the performance of the supervised machine learning algorithms when presented with extracted speech characteristics. This work proposes a generic platform to capture and validate emotional speech. The aim of the platform is collaborative-crowdsourcing and it can be used for any language (currently, it is available in four languages such as Spanish, English, German and French). As an example, a module for elicitation of stress in speech through a set of online interviews and other module for labeling recorded speech have been developed. This study is envisaged as the beginning of an effort to establish a large, cost-free standard speech corpus to assess emotions across multiple languages.},
 bibtype = {book},
 author = {Palacios-Alonso, D. and Lázaro-Carrascosa, C. and López-Arribas, A. and Meléndez-Morales, G. and Gómez-Rodellar, A. and Loro-Álavez, A. and Nieto-Lluis, V. and Rodellar-Biarge, V. and Tsanas, A. and Gómez-Vilda, P.},
 doi = {10.1007/978-3-030-19591-5_16}
}

© 2019, Springer Nature Switzerland AG. Detecting and identifying emotions expressed in speech signals is a very complex task that generally requires processing a large sample size to extract intricate details and match the diversity of human expression in speech. There is not an emotional dataset commonly accepted as a standard test bench to evaluate the performance of the supervised machine learning algorithms when presented with extracted speech characteristics. This work proposes a generic platform to capture and validate emotional speech. The aim of the platform is collaborative-crowdsourcing and it can be used for any language (currently, it is available in four languages such as Spanish, English, German and French). As an example, a module for elicitation of stress in speech through a set of online interviews and other module for labeling recorded speech have been developed. This study is envisaged as the beginning of an effort to establish a large, cost-free standard speech corpus to assess emotions across multiple languages.

@article{
 title = {Quantifying ultrasonic mouse vocalizations using acoustic analysis in a supervised statistical machine learning framework},
 type = {article},
 year = {2019},
 pages = {8100},
 volume = {9},
 websites = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542843/pdf/41598_2019_Article_44221.pdf},
 id = {c44782c9-4d7b-3ce3-9657-c651db46a58f},
 created = {2019-06-12T23:59:00.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-17T17:42:58.662Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019, The Author(s). Examination of rodent vocalizations in experimental conditions can yield valuable insights into how disease manifests and progresses over time. It can also be used as an index of social interest, motivation, emotional development or motor function depending on the animal model under investigation. Most mouse communication is produced in ultrasonic frequencies beyond human hearing. These ultrasonic vocalizations (USV) are typically described and evaluated using expert defined classification of the spectrographic appearance or simplistic acoustic metrics resulting in nine call types. In this study, we aimed to replicate the standard expert-defined call types of communicative vocal behavior in mice by using acoustic analysis to characterize USVs and a principled supervised learning setup. We used four feature selection algorithms to select parsimonious subsets with maximum predictive accuracy, which are then presented into support vector machines (SVM) and random forests (RF). We assessed the resulting models using 10-fold cross-validation with 100 repetitions for statistical confidence and found that a parsimonious subset of 8 acoustic measures presented to RF led to 85% correct out-of-sample classification, replicating the experts’ labels. Acoustic measures can be used by labs to describe USVs and compare data between groups, and provide insight into vocal-behavioral patterns of mice by automating the process on matching the experts’ call types.},
 bibtype = {article},
 author = {Vogel, A.P. and Tsanas, A. and Scattoni, M.L.},
 doi = {10.1038/s41598-019-44221-3},
 journal = {Scientific Reports}
}

© 2019, The Author(s). Examination of rodent vocalizations in experimental conditions can yield valuable insights into how disease manifests and progresses over time. It can also be used as an index of social interest, motivation, emotional development or motor function depending on the animal model under investigation. Most mouse communication is produced in ultrasonic frequencies beyond human hearing. These ultrasonic vocalizations (USV) are typically described and evaluated using expert defined classification of the spectrographic appearance or simplistic acoustic metrics resulting in nine call types. In this study, we aimed to replicate the standard expert-defined call types of communicative vocal behavior in mice by using acoustic analysis to characterize USVs and a principled supervised learning setup. We used four feature selection algorithms to select parsimonious subsets with maximum predictive accuracy, which are then presented into support vector machines (SVM) and random forests (RF). We assessed the resulting models using 10-fold cross-validation with 100 repetitions for statistical confidence and found that a parsimonious subset of 8 acoustic measures presented to RF led to 85% correct out-of-sample classification, replicating the experts’ labels. Acoustic measures can be used by labs to describe USVs and compare data between groups, and provide insight into vocal-behavioral patterns of mice by automating the process on matching the experts’ call types.

@inproceedings{
 title = {Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling},
 type = {inproceedings},
 year = {2019},
 keywords = {Agent-based modelling,Pattern formation,Pluripotent stem cells},
 id = {8bc12559-61fc-3e68-b174-54c28910ed56},
 created = {2020-02-04T23:59:00.000Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-03-06T03:34:14.315Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019 IEEE. Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.},
 bibtype = {inproceedings},
 author = {Wang, M. and Tsanas, A. and Blin, G. and Robertson, D.},
 doi = {10.1109/BIBE.2019.00170},
 booktitle = {Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019}
}

© 2019 IEEE. Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.

@inproceedings{
 title = {Heterogeneity in asthma medication adherence measurement},
 type = {inproceedings},
 year = {2019},
 keywords = {Adherence,Asthma,Electronic monitoring,Medication,Pediatric},
 id = {54fd93ff-7636-3003-8ae6-20a19a18850e},
 created = {2020-02-04T23:59:00.000Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-03-06T03:36:14.109Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019 IEEE. Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).},
 bibtype = {inproceedings},
 author = {Tibble, H. and Chan, A. and Mitchell, E.A. and Horne, R. and Mizani, M.A. and Sheikh, A. and Tsanas, A.},
 doi = {10.1109/BIBE.2019.00168},
 booktitle = {Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019}
}

© 2019 IEEE. Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).

@article{
 title = {Applications of Machine Learning in Real-life Digital Health Interventions : A Review of the Literature},
 type = {article},
 year = {2019},
 keywords = {1,2019,21,4,artificial intelligence,data mining,digital health,e12286,https,iss,j med internet res,jmir,machine learning,org,p,review,telemedicine,vol,www},
 pages = {e12286},
 volume = {(in press)},
 id = {e7048ef5-ef18-38b9-b941-d66cbed53fe4},
 created = {2021-06-04T16:34:56.379Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-06-04T16:37:11.634Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Triantafyllidis, Andreas K and Tsanas, Athanasios},
 doi = {http://dx.doi.org/10.2196/12286},
 journal = {Journal of Medical Internet Research},
 number = {4}
}

@inproceedings{
 title = {Biomedical speech signal insights from a large scale cohort across seven countries: The Parkinson’s voice initiative study},
 type = {inproceedings},
 year = {2019},
 keywords = {Parkinson’s Disease (PD),Parkinson’s Voice Initiative (PVI),Speech signal processing,Sustained vowel phonations},
 pages = {45-48},
 city = {Florence, Italy},
 id = {ecec1d90-fe39-335a-aec9-e59a5adbd80c},
 created = {2021-08-11T17:28:52.460Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-08-11T17:29:58.610Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Previous work has demonstrated the enormous potential of speech signals collected under highly controlled acoustic conditions in biomedical applications. These include accurately differentiating people diagnosed with Parkinson’s Disease (PD) from Healthy Controls (HC), and longitudinal telemonitoring of PD symptom severity. The generalizability and scalability of these findings need to be investigated when speech signals are not recorded under optimal, carefully controlled acoustic conditions. In this regard, we recently completed the Parkinson’s Voice Initiative (PVI) study collecting data from a very large cohort comprising 1483 PD and 8300 HC participants from seven countries. Specifically, we collected 19,303 sustained vowel /a/ recordings: 144 (Argentina), 227 (Brazil), 1521 (Canada), 75 (Mexico), 573 (Spain), 4088 (UK) and 12,675 (USA). We acoustically characterized these recordings using 307 dysphonia measures which we had previously investigated in related PD studies. We draw comparisons against previous studies which processed high quality speech data, and their generalizability in this large-scale cohort. We found that many of the state-of-art nonlinear dysphonia measures do not differentiate PD and HC sufficiently well, likely because of the reduced signal bandwidth. These exploratory findings provide new insights into understanding the challenges in the PVI dataset, underlining the need for further speech signal processing development.},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 booktitle = {Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA)}
}

Previous work has demonstrated the enormous potential of speech signals collected under highly controlled acoustic conditions in biomedical applications. These include accurately differentiating people diagnosed with Parkinson’s Disease (PD) from Healthy Controls (HC), and longitudinal telemonitoring of PD symptom severity. The generalizability and scalability of these findings need to be investigated when speech signals are not recorded under optimal, carefully controlled acoustic conditions. In this regard, we recently completed the Parkinson’s Voice Initiative (PVI) study collecting data from a very large cohort comprising 1483 PD and 8300 HC participants from seven countries. Specifically, we collected 19,303 sustained vowel /a/ recordings: 144 (Argentina), 227 (Brazil), 1521 (Canada), 75 (Mexico), 573 (Spain), 4088 (UK) and 12,675 (USA). We acoustically characterized these recordings using 307 dysphonia measures which we had previously investigated in related PD studies. We draw comparisons against previous studies which processed high quality speech data, and their generalizability in this large-scale cohort. We found that many of the state-of-art nonlinear dysphonia measures do not differentiate PD and HC sufficiently well, likely because of the reduced signal bandwidth. These exploratory findings provide new insights into understanding the challenges in the PVI dataset, underlining the need for further speech signal processing development.

@inproceedings{
 title = {New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity},
 type = {inproceedings},
 year = {2019},
 pages = {3412-3415},
 websites = {https://ieeexplore.ieee.org/document/8856559},
 city = {Berlin, Germany},
 id = {eed2bb09-9232-36f6-8923-fff9ded851fe},
 created = {2021-10-14T09:04:23.048Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:33.742Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios},
 doi = {10.1109/EMBC.2019.8856559},
 booktitle = {41st IEEE Engineering in Medicine and Biology Conference}
}

@article{
 title = {Applications of Machine Learning in Real-life Digital Health Interventions: Review of the Literature},
 type = {article},
 year = {2019},
 pages = {e12286},
 volume = {21},
 id = {6ae8fc68-8337-3fda-8071-0f57b1d08927},
 created = {2021-10-14T09:04:23.093Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:34.381Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Triantafyllidis, Andreas K and Tsanas, Athanasios},
 journal = {Journal of Medical Internet Research},
 number = {4}
}

@article{
 title = {Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.},
 type = {article},
 year = {2019},
 keywords = {acute coronary syndrome,infarction,machine learning,myocardial,see page 908,sources of funding,troponin},
 pages = {899-909},
 volume = {140},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/31416346},
 id = {b90cbdbb-0f03-30ca-b166-fe303eb2b8e5},
 created = {2021-10-14T09:04:23.491Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:05:22.935Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.},
 bibtype = {article},
 author = {Than, Martin P and Pickering, John W and Sandoval, Yader and Shah, Anoop S V and Tsanas, Athanasios and Apple, Fred S and Blankenberg, Stefan and Cullen, Louise and Mueller, Christian and Neumann, Johannes T and Twerenbold, Raphael and Westermann, Dirk and Beshiri, Agim and Mills, Nicholas L and MI3 collaborative, undefined},
 doi = {10.1161/CIRCULATIONAHA.119.041980},
 journal = {Circulation}
}

BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.

@article{
 title = {Predicting asthma attacks in primary care: Protocol for developing a machine learning-based prediction model},
 type = {article},
 year = {2019},
 keywords = {asthma,asthma attacks,machine learning,prediction,primary care},
 pages = {e028375},
 volume = {9},
 websites = {https://pubmed.ncbi.nlm.nih.gov/31292179/},
 id = {36f3437e-f170-3b7d-be4f-6cb973cef221},
 created = {2021-10-14T09:04:23.526Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:23.526Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019 Author(s). Introduction: Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data. Methods and analysis: We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprising 500 000 individuals across 75 Scottish general practices, with linked longitudinal primary care prescribing records, primary care Read codes, accident and emergency records, hospital admissions and deaths. Models will be compared on a partition of the dataset reserved for validation, and the final model will be tested in both an unseen partition of the derivation dataset and an external dataset from the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study. Ethics and dissemination: Permissions for the ALHS project were obtained from the South East Scotland Research Ethics Committee 02 [16/SS/0130] and the Public Benefit and Privacy Panel for Health and Social Care (1516-0489). Permissions for the SIVE II project were obtained from the Privacy Advisory Committee (National Services NHS Scotland) [68/14] and the National Research Ethics Committee West Midlands-Edgbaston [15/WM/0035]. The subsequent research paper will be submitted for publication to a peer-reviewed journal and code scripts used for all components of the data cleaning, compiling, and analysis will be made available in the open source GitHub website (https://github.com/hollytibble).},
 bibtype = {article},
 author = {Tibble, H. and Tsanas, A. and Horne, E. and Horne, R. and Mizani, M. and Simpson, C.R. and Sheikh, A.},
 doi = {10.1136/bmjopen-2018-028375},
 journal = {BMJ Open},
 number = {7}
}

© 2019 Author(s). Introduction: Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data. Methods and analysis: We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprising 500 000 individuals across 75 Scottish general practices, with linked longitudinal primary care prescribing records, primary care Read codes, accident and emergency records, hospital admissions and deaths. Models will be compared on a partition of the dataset reserved for validation, and the final model will be tested in both an unseen partition of the derivation dataset and an external dataset from the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study. Ethics and dissemination: Permissions for the ALHS project were obtained from the South East Scotland Research Ethics Committee 02 [16/SS/0130] and the Public Benefit and Privacy Panel for Health and Social Care (1516-0489). Permissions for the SIVE II project were obtained from the Privacy Advisory Committee (National Services NHS Scotland) [68/14] and the National Research Ethics Committee West Midlands-Edgbaston [15/WM/0035]. The subsequent research paper will be submitted for publication to a peer-reviewed journal and code scripts used for all components of the data cleaning, compiling, and analysis will be made available in the open source GitHub website (https://github.com/hollytibble).

@inproceedings{
 title = {Exploring telephone-quality speech signals towards parkinson's disease assessment in a large acoustically non-controlled study},
 type = {inproceedings},
 year = {2019},
 keywords = {Data visualization,Dimensionality reduction,Parkinson's Disease (PD),Sustained vowels},
 pages = {953-956},
 city = {Athens, Greece},
 id = {64c0873d-bbe2-3606-b394-3adc85509424},
 created = {2021-10-14T09:04:23.526Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:23.526Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019 IEEE. The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.},
 bibtype = {inproceedings},
 author = {Tsanas, A. and Arora, S.},
 doi = {10.1109/BIBE.2019.00178},
 booktitle = {Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019}
}

© 2019 IEEE. The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.

@article{
 title = {Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice},
 type = {article},
 year = {2019},
 pages = {2871-2884},
 volume = {145},
 websites = {http://dx.doi.org/10.1121/1.5100272},
 id = {20c9496b-4798-3b2a-9505-24949b00dc12},
 created = {2024-03-30T05:21:37.112Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2025-01-12T07:17:06.277Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HCs). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, 2759 telephone-quality voice recordings from 1483 PD and 15 321 recordings from 8300 HC participants were analyzed. To account for variations in phonetic backgrounds, data were acquired from seven countries. A statistical framework for analyzing voice was developed, whereby 307 dysphonia measures that quantify different properties of voice impairment, such as breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor, were computed. Feature selection algorithms were used to identify robust parsimonious feature subsets, which were used in combination with a random forests (RFs) classifier to accurately distinguish PD from HC. The best tenfold cross-validation performance was obtained using Gram-Schmidt orthogonalization and RF, leading to mean sensitivity of 64.90% (standard deviation, SD, 2.90%) and mean specificity of 67.96% (SD 2.90%). This large scale study is a step forward toward assessing the development of a reliable, cost-effective, and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.},
 bibtype = {article},
 author = {Arora, Siddharth and Baghai-Ravary, Ladan and Tsanas, Athanasios},
 doi = {10.1121/1.5100272},
 journal = {The Journal of the Acoustical Society of America},
 number = {5}
}

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HCs). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, 2759 telephone-quality voice recordings from 1483 PD and 15 321 recordings from 8300 HC participants were analyzed. To account for variations in phonetic backgrounds, data were acquired from seven countries. A statistical framework for analyzing voice was developed, whereby 307 dysphonia measures that quantify different properties of voice impairment, such as breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor, were computed. Feature selection algorithms were used to identify robust parsimonious feature subsets, which were used in combination with a random forests (RFs) classifier to accurately distinguish PD from HC. The best tenfold cross-validation performance was obtained using Gram-Schmidt orthogonalization and RF, leading to mean sensitivity of 64.90% (standard deviation, SD, 2.90%) and mean specificity of 67.96% (SD 2.90%). This large scale study is a step forward toward assessing the development of a reliable, cost-effective, and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.

@article{
 title = {Investigating voice as a biomarker for leucine-rich repeat kinase 2-associated Parkinson’s disease},
 type = {article},
 year = {2018},
 pages = {503-510},
 volume = {8},
 id = {1d0310ac-be83-36a9-909f-a16a0af2f247},
 created = {2021-06-04T16:34:56.069Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-02-16T13:54:02.333Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.},
 bibtype = {article},
 author = {Arora, Siddharth and Visanji, Naomi P and Mestre, Tiago A and Tsanas, Athanasios and Aldakheel, Amaal and Connolly, Barbara S and Gasca-salas, Carmen and Kern, Drew S and Jain, Jennifer and Slow, Elizabeth J and Faust-Socher, Achinoam and Lang, Anthony E and Little, Max A. and Marras, Connie},
 doi = {10.3233/JPD-181389},
 journal = {Journal of Parkinson's Disease},
 number = {4}
}

We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.

@article{
 title = {High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial},
 type = {article},
 year = {2018},
 pages = {919-928},
 volume = {392},
 id = {914f252f-8a30-322f-9630-a43ce2955fe3},
 created = {2021-06-04T16:34:56.218Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-06-04T16:38:29.166Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Shah, Anoop S.V. V and Anand, Atul and Strachan, Fiona E. and Ferry, Amy V. and Lee, Kuan Ken and Chapman, Andrew R. and Sandeman, Dennis and Stables, Catherine L and Adamson, Philip D and Andrews, Jack P M and Anwar, Mohamed S and Hung, John and Moss, Alastair Alistair J and O'Brien, Rachel and Berry, Colin and Findlay, Iain and Walker, Simon and Cruickshank, Anne and Reid, Alan and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and McAllister, David A and Maguire, Donogh and Fox, Keith A A and Newby, David E and Tuck, Christopher and Harkess, Ronald Ronnie and Parker, Richard A and Keerie, Catriona and Weir, Christopher J and Mills, Nicholas L and Investigators, on behalf of the HIGH-STEACS and Mills, Nicholas L and Strachan, Fiona E. and Tuck, Christopher and Shah, Anoop S.V. V and Anand, Atul and Ferry, Amy V. and Lee, Kuan Ken and Chapman, Andrew R. and Sandeman, Dennis and Adamson, Philip D and Stables, Catherine L and Marshall, Lucy and Stewart, Stacey D and Fujisawa, Takeshi and Vallejos, Catalina A and Tsanas, Athanasios and Hautvast, Mischa and McPherson, Jean and McKinlay, Lynn and Newby, David E and Fox, Keith A A and Berry, Colin and Walker, Simon and Weir, Christopher J and Ford, Ian and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and Reid, Alan and Cruikshank, Anne and Findlay, Iain and Amoils, Shannon and McAllister, David A and Maguire, Donogh and Stevens, Jennifer and Norrie, John and Andrews, Jack P M and Adamson, Philip D and Moss, Alastair Alistair J and Anwar, Mohamed S and Hung, John and Malo, Jonathan and Fischbacher, Colin M and Croal, Bernard L and Leslie, Stephen J and Keerie, Catriona and Parker, Richard A and Walker, Allan and Harkess, Ronald Ronnie and Wackett, Tony and Armstrong, Roma and Flood, Marion and Stirling, Laura and MacDonald, Claire and Sadat, Imran and Finlay, Frank and Charles, Heather and Linksted, Pamela and Young, Stephen and Alexander, Bill and Duncan, Chris and Moss, Alastair Alistair J and Stewart, Stacey D and Marshall, Lucy and Stables, Catherine L and Fox, Keith A A and Reid, Alan and McAllister, David A and McKinlay, Lynn and Alexander, Bill and Berry, Colin and Findlay, Iain and Leslie, Stephen J and Walker, Simon and O'Brien, Rachel and Cruickshank, Anne and Young, Stephen and Apple, Fred S and Strachan, Fiona E. and Gray, Alasdair and Tsanas, Athanasios and Croal, Bernard L and Sandeman, Dennis and Maguire, Donogh and Anand, Atul and Shah, Anoop S.V. V and Fujisawa, Takeshi and Anwar, Mohamed S and Linksted, Pamela and Chapman, Andrew R. and Wackett, Tony and Mills, Nicholas L and McPherson, Jean and MacDonald, Claire and Harkess, Ronald Ronnie and Stirling, Laura and Weir, Christopher J and Andrews, Jack P M and Finlay, Frank and Fischbacher, Colin M and Ferry, Amy V. and Newby, David E and Sadat, Imran and Armstrong, Roma and Charles, Heather and Duncan, Chris and Hung, John and Parker, Richard A and Adamson, Philip D and Lee, Kuan Ken and Hautvast, Mischa and Vallejos, Catalina A and Keerie, Catriona and Malo, Jonathan},
 doi = {10.1016/s0140-6736(18)31923-8},
 journal = {The Lancet},
 number = {10151}
}

@article{
 title = {Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder},
 type = {article},
 year = {2018},
 pages = {1649},
 volume = {8},
 websites = {http://dx.doi.org/10.1038/s41598-018-19888-9},
 publisher = {Springer US},
 id = {b233e730-9309-354d-92bf-e86194b91f12},
 created = {2021-06-04T16:34:56.539Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-06-04T16:35:35.289Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Carr, Oliver and Saunders, Kate E.A. A. and Tsanas, Athanasios and Palmius, Niclas and Geddes, John R. and Foster, Russell and Goodwin, Guy M. and De Vos, Maarten},
 doi = {10.1038/s41598-018-19888-9},
 journal = {Scientific reports}
}

@article{
 title = {Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder},
 type = {article},
 year = {2018},
 pages = {79},
 volume = {8},
 id = {10553923-a06b-3f3a-9666-4cd652aae3cd},
 created = {2021-10-14T09:04:23.584Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-10-14T09:04:40.017Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.},
 bibtype = {article},
 author = {Carr, O. and Saunders, K.E.A. and Bilderbeck, A.C. and Tsanas, A. and Palmius, N. and Geddes, J.R. and Foster, R. and De Vos, M. and Goodwin, G.M.},
 doi = {10.1038/s41398-018-0125-7},
 journal = {Translational Psychiatry}
}

© 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.

@inproceedings{
 title = {Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins},
 type = {inproceedings},
 year = {2017},
 keywords = {contour,forensic phonetics,fundamental frequency,pause fillers,speech signal processing},
 issue = {July},
 id = {c557413d-de93-391e-82a8-1031267727d8},
 created = {2019-02-22T11:10:00.214Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2019-02-22T11:10:14.831Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, A. and San Segundo, E. and Gómez-Vilda, P.},
 doi = {10.1049/cp.2017.0161},
 booktitle = {8th International Conference of Pattern Recognition Systems}
}

@article{
 title = {Detecting Bipolar Depression from Geographic Location Data},
 type = {article},
 year = {2017},
 pages = {1761-1771},
 volume = {64},
 websites = {http://ieeexplore.ieee.org/document/7676335/},
 id = {641fb1db-6f1f-3558-bfb8-cf62e368c5db},
 created = {2021-06-04T16:34:56.589Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-08-30T15:31:45.501Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Palmius, Niclas and Tsanas, Athanasios and Saunders, Kate E. A. and Bilderbeck, Amy C. and Geddes, John R. and Goodwin, Guy M. and De Vos, Maarten},
 doi = {10.1109/TBME.2016.2611862},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {8}
}

@article{
 title = {Euclidean Distances as measures of speaker similarity including identical twin pairs: a forensic investigation using source and filter voice characteristics},
 type = {article},
 year = {2017},
 keywords = {Acoustic analysis,Forensic phonetics,Pause fillers,Perceptual assessment,Twins,Voice quality},
 pages = {25-38},
 volume = {270},
 websites = {http://dx.doi.org/10.1016/j.forsciint.2016.11.020},
 publisher = {Elsevier Ireland Ltd},
 id = {af66f581-d911-3ab5-84c5-6b190dd30c18},
 created = {2021-06-04T16:37:47.260Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2021-09-14T20:14:03.523Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual???acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.},
 bibtype = {article},
 author = {San Segundo, Eugenia and Tsanas, Athanasios and Gomez-Vilda, Pedro},
 doi = {10.1016/j.forsciint.2016.11.020},
 journal = {Forensic Science International}
}

There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual???acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.

@article{
 title = {Insomnia, Nightmares, and Chronotype as Markers of Risk for Severe Mental Illness: Results from a Student Population},
 type = {article},
 year = {2016},
 pages = {173-181},
 volume = {39},
 id = {3f864d64-6f8d-37e4-bd9d-e94fd32d3dfb},
 created = {2016-02-04T19:11:27.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2017-03-25T13:41:06.256Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {STUDY OBJECTIVES: To group participants according to markers of risk for severe mental illness based on subsyndromal symptoms reported in early adulthood and evaluate attributes of sleep across these risk categories.\n\nDESIGN: Cross-sectional survey data were used to cluster participants based on dimensional measures of psychiatric symptoms (hallucinations, paranoia, depression, anxiety, and (hypo)mania). High, medium, and low symptom groups were compared across sleep parameters: insomnia symptoms, nightmares, chronotype, and social jet lag.\n\nSETTING: An online survey of sleep and psychiatric symptomatology (The Oxford Sleep Survey) recruiting from one UK university.\n\nPARTICIPANTS: 1403 Oxford University students (undergraduate and postgraduate). There were no inclusion and exclusion criteria. The median age was 21 (interquartile range = 20-23) and 55.60% were female.\n\nMEASUREMENTS AND RESULTS: Insomnia symptoms, nightmares frequency, and nightmare-related distress increased in a dose-response manner with higher reported subsyndromal psychiatric symptoms (low, medium, and high). The high-risk group exhibited a later chronotype (mid sleep point for free days) than the medium- or low-risk group. The majority of participants (71.7%) in the high-risk group screened positive for insomnia and the median nightmare frequency was two per fortnight (moderately severe pathology).\n\nCONCLUSIONS: Insomnia, nightmares, and circadian phase delay are associated with increased subsyndromal psychiatric symptoms in young people. Each is a treatable sleep disorder and might be a target for early intervention to modify the subsequent progression of psychiatric disorder.},
 bibtype = {article},
 author = {Sheaves, B and Porcheret, K and Tsanas, A and Espie, Colin and Foster, Russell G and Freeman, Daniel and Harrison, Paul and Wulff, Katharina and Goodwin, Guy},
 journal = {Sleep}
}

STUDY OBJECTIVES: To group participants according to markers of risk for severe mental illness based on subsyndromal symptoms reported in early adulthood and evaluate attributes of sleep across these risk categories.\n\nDESIGN: Cross-sectional survey data were used to cluster participants based on dimensional measures of psychiatric symptoms (hallucinations, paranoia, depression, anxiety, and (hypo)mania). High, medium, and low symptom groups were compared across sleep parameters: insomnia symptoms, nightmares, chronotype, and social jet lag.\n\nSETTING: An online survey of sleep and psychiatric symptomatology (The Oxford Sleep Survey) recruiting from one UK university.\n\nPARTICIPANTS: 1403 Oxford University students (undergraduate and postgraduate). There were no inclusion and exclusion criteria. The median age was 21 (interquartile range = 20-23) and 55.60% were female.\n\nMEASUREMENTS AND RESULTS: Insomnia symptoms, nightmares frequency, and nightmare-related distress increased in a dose-response manner with higher reported subsyndromal psychiatric symptoms (low, medium, and high). The high-risk group exhibited a later chronotype (mid sleep point for free days) than the medium- or low-risk group. The majority of participants (71.7%) in the high-risk group screened positive for insomnia and the median nightmare frequency was two per fortnight (moderately severe pathology).\n\nCONCLUSIONS: Insomnia, nightmares, and circadian phase delay are associated with increased subsyndromal psychiatric symptoms in young people. Each is a treatable sleep disorder and might be a target for early intervention to modify the subsequent progression of psychiatric disorder.

@article{
 title = {Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder},
 type = {article},
 year = {2016},
 keywords = {Bipolar disorder,Borderline personality disorder,Digital health,Mood assessment,Mood monitoring,Patient reported outcome measures},
 pages = {225-233},
 volume = {205},
 websites = {http://dx.doi.org/10.1016/j.jad.2016.06.065},
 publisher = {Elsevier},
 id = {4628b175-285b-3be8-b6ab-0461eb6c484a},
 created = {2016-10-10T14:45:25.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2025-07-31T14:48:49.025Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {BACKGROUND\nTraditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. \n\nMETHODS\nWe introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customized smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorder (BPD) and 51 Healthy Control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. \n\nRESULTS\nMZ correlated well (|R|>0.5,&#x000A0;p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,&#x000A0;p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. \n\nLIMITATIONS\nAll reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. \n\nCONCLUSIONS\nMZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.},
 bibtype = {article},
 author = {Tsanas, A. and Saunders, K.E.A. and Bilderbeck, A.C. and Palmius, N. and Osipov, M. and Clifford, G.D. and Goodwin, G.Μm. and De Vos, M.},
 doi = {10.1016/j.jad.2016.06.065},
 journal = {Journal of Affective Disorders}
}

BACKGROUND\nTraditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. \n\nMETHODS\nWe introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customized smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorder (BPD) and 51 Healthy Control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. \n\nRESULTS\nMZ correlated well (|R|>0.5, p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3, p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. \n\nLIMITATIONS\nAll reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. \n\nCONCLUSIONS\nMZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.

@article{
 title = {The power of data mining in diagnosis of childhood pneumonia},
 type = {article},
 year = {2016},
 keywords = {childhood pneumonia,diagnostics,machine learning},
 pages = {20160266},
 volume = {13},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/27466436},
 id = {e932d838-e062-3330-9e92-83724aa66158},
 created = {2017-03-24T23:03:03.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2019-01-19T20:37:37.243Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (C-reactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians.},
 bibtype = {article},
 author = {Naydenova, Elina and Tsanas, Athanasios and Howie, Stephen and Casals-Pascual, Climent and De Vos, Maarten},
 doi = {10.1098/rsif.2016.0266},
 journal = {Journal of the Royal Society, Interface / the Royal Society},
 number = {120}
}

Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (C-reactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians.

@inbook{
 type = {inbook},
 year = {2016},
 keywords = {Aging voice,Dysarthria,Neurologic disease,Parkinson’s disease (PD),Speech neuromotor activity},
 pages = {93-102},
 volume = {48},
 publisher = {Springer},
 city = {Berlin},
 id = {aa287ff8-0a5b-3f5a-91b4-0ac284c8dfd0},
 created = {2018-02-28T16:13:00.169Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2022-05-06T21:09:13.801Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {true},
 abstract = {© Springer International Publishing Switzerland 2016. It is known that Parkinson’s Disease (PD) leaves marks in phonation dystonia and tremor. These marks can be expressed as a function of biomechanical characteristics monitoring vocal fold tension and imbalance. These features may assist tracing the neuromotor activity of laryngeal pathways. Therefore these features may be used in grading the stage of a PD patient efficiently, frequently and remotely by telephone or VoIP channels. The present work is devoted to describe and compare the PD symptom severity quantification from neuromotor-sensitive features with respect to other features on a telephone-recorded database. The results of these comparisons are presented and discussed.},
 bibtype = {inbook},
 author = {Gómez-Vilda, P. and Álvarez-Marquina, A. and Tsanas, A. and Lázaro-Carrascosa, C.A. and Rodellar-Biarge, V. and Nieto-Llui, V. and Martínez-Olalla, R.},
 doi = {10.1007/978-3-319-28109-4_10},
 chapter = {Phonation biomechanics in quantifying parkinson’s disease symptom severity},
 title = {Recent Advances in Nonlinear Speech Processing}
}

© Springer International Publishing Switzerland 2016. It is known that Parkinson’s Disease (PD) leaves marks in phonation dystonia and tremor. These marks can be expressed as a function of biomechanical characteristics monitoring vocal fold tension and imbalance. These features may assist tracing the neuromotor activity of laryngeal pathways. Therefore these features may be used in grading the stage of a PD patient efficiently, frequently and remotely by telephone or VoIP channels. The present work is devoted to describe and compare the PD symptom severity quantification from neuromotor-sensitive features with respect to other features on a telephone-recorded database. The results of these comparisons are presented and discussed.

@inproceedings{
 title = {Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings},
 type = {inproceedings},
 year = {2015},
 keywords = {Childhood pneumonia,Random forests,diagnostics,machine learning},
 id = {9be2c673-a31c-3667-b0d4-2ca4ca6305be},
 created = {2018-02-28T16:13:00.049Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2018-02-28T16:13:00.049Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2015 IEEE. Pneumonia is the leading cause of death in children under five, with 1.1 million deaths annually more than the combined burden of HIV/AIDS, malaria, and tuberculosis for this age group; the majority of these deaths occur in resource-constrained settings. Accurate diagnosis of pneumonia relies on expensive human expertise and requires the evaluation of multiple clinical characteristics, measured using advanced diagnostic tools. The shortage of clinical experts and appropriate diagnostic tools in many low and middle income countries impedes timely and accurate diagnosis. We demonstrate that the diagnostic process can be automated using machine learning techniques, processing several clinical measurements that could be obtained with affordable and easy-to-operate point-of-care tools. We evaluated our findings on a dataset of 1093 children, comprising 777 diagnosed with pneumonia and 316 healthy controls, on the basis of 47 clinical characteristics. Seven feature selection techniques were used to identify robust, parsimonious subsets of clinical characteristics, which could be measured reliably and affordably. Standard machine learning techniques, such as support vector machines and random forests, were used to develop a predictive algorithm based on the four jointly most predictive characteristics (temperature, respiratory rate, heart rate and oxygen saturation); this approach led to 96.6% sensitivity, 96.4% specificity, and an Area Under the Curve (AUC) of 97.8%. The proposed approach can be easily embedded in a mobile phone application, allowing for point-of-care assessment and identification of children in need of clinical attention by basically trained healthcare workers in resource-constrained settings.},
 bibtype = {inproceedings},
 author = {Naydenova, E. and Tsanas, A. and Casals-Pascual, C. and De Vos, M. and Howie, S.},
 doi = {10.1109/GHTC.2015.7344000},
 booktitle = {Proceedings of the 5th IEEE Global Humanitarian Technology Conference, GHTC 2015}
}

© 2015 IEEE. Pneumonia is the leading cause of death in children under five, with 1.1 million deaths annually more than the combined burden of HIV/AIDS, malaria, and tuberculosis for this age group; the majority of these deaths occur in resource-constrained settings. Accurate diagnosis of pneumonia relies on expensive human expertise and requires the evaluation of multiple clinical characteristics, measured using advanced diagnostic tools. The shortage of clinical experts and appropriate diagnostic tools in many low and middle income countries impedes timely and accurate diagnosis. We demonstrate that the diagnostic process can be automated using machine learning techniques, processing several clinical measurements that could be obtained with affordable and easy-to-operate point-of-care tools. We evaluated our findings on a dataset of 1093 children, comprising 777 diagnosed with pneumonia and 316 healthy controls, on the basis of 47 clinical characteristics. Seven feature selection techniques were used to identify robust, parsimonious subsets of clinical characteristics, which could be measured reliably and affordably. Standard machine learning techniques, such as support vector machines and random forests, were used to develop a predictive algorithm based on the four jointly most predictive characteristics (temperature, respiratory rate, heart rate and oxygen saturation); this approach led to 96.6% sensitivity, 96.4% specificity, and an Area Under the Curve (AUC) of 97.8%. The proposed approach can be easily embedded in a mobile phone application, allowing for point-of-care assessment and identification of children in need of clinical attention by basically trained healthcare workers in resource-constrained settings.

@article{
 title = {Objective automatic assessment of rehabilitative speech treatment in Parkinson's disease},
 type = {article},
 year = {2014},
 keywords = {Decision support tool,Lee Silverman voice treatment (LSVT),Parkinson's disease (PD),Speech rehabilitation},
 pages = {181-190},
 volume = {22},
 id = {4eb17dad-c568-3bb4-93fa-6f745fe424df},
 created = {2015-05-11T16:50:17.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2019-03-23T22:39:16.502Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as “acceptable” (a clinician would allow persisting during in-person rehabilitation treatment) or “unacceptable” (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as “acceptable” or “unacceptable.” We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool.},
 bibtype = {article},
 author = {Tsanas, Athanasios and Little, Max A. and Fox, Cynthia and Ramig, Lorraine O.},
 doi = {10.1109/TNSRE.2013.2293575},
 journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
 number = {1}
}

Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as “acceptable” (a clinician would allow persisting during in-person rehabilitation treatment) or “unacceptable” (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as “acceptable” or “unacceptable.” We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool.

@article{
 title = {Current Impact, Future Prospects and Implications of Mobile Healthcare in India},
 type = {article},
 year = {2014},
 month = {11},
 day = {4},
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 bibtype = {article},
 author = {Kappal, Rishi and Mehndiratta, Amit and Anandaraj, Prabu and Tsanas, Athanasios},
 doi = {10.5195/cajgh.2014.116},
 journal = {cajgh}
}

@article{
 title = {Robust fundamental frequency estimation in sustained vowels: detailed algorithmic comparisons and information fusion with adaptive Kalman filtering.},
 type = {article},
 year = {2014},
 pages = {2885-901},
 volume = {135},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4032429&tool=pmcentrez&rendertype=abstract,http://scitation.aip.org/content/asa/journal/jasa/135/5/10.1121/1.4870484},
 id = {fb624a38-f84d-3fd6-b0f8-ed9afb01db5e},
 created = {2021-06-04T16:34:56.172Z},
 file_attached = {true},
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 last_modified = {2025-09-03T01:14:30.511Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required.},
 bibtype = {article},
 author = {Tsanas, Athanasios and Zañartu, Matías and Little, Max A. and Fox, Cynthia and Ramig, Lorraine O. and Clifford, Gari D.},
 doi = {10.1121/1.4870484},
 journal = {The Journal of the Acoustical Society of America},
 number = {5}
}

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required.

@inbook{
 type = {inbook},
 year = {2013},
 pages = {113-125},
 publisher = {Springer},
 chapter = {A methodology for the analysis of medical data},
 id = {337328fe-f43d-35b1-bcb8-6c4984812193},
 created = {2015-05-11T16:50:17.000Z},
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 bibtype = {inbook},
 author = {Tsanas, Athanasios and Little, Max A. and McSharry, Patrick E},
 editor = {Sturmberg, J.P. and Martin, C.M.},
 title = {Handbook of Systems and Complexity in Health}
}

@article{
 title = {Increased expression of phosphorylated NBS1, a key molecule of the DNA damage response machinery, is an adverse prognostic factor in patients with de novo myelodysplastic syndromes},
 type = {article},
 year = {2013},
 keywords = {DNA damage response,Myelodysplastic syndromes,PATM,PNBS1,γH2AX},
 pages = {1576-1582},
 volume = {37},
 websites = {http://dx.doi.org/10.1016/j.leukres.2013.08.018},
 publisher = {Elsevier Ltd},
 id = {05485315-1254-3bb8-b0bc-ed0d5ce9156a},
 created = {2016-03-07T16:38:37.000Z},
 file_attached = {true},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 last_modified = {2017-03-25T13:41:06.256Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The expression of activated forms of key proteins of the DNA damage response machinery (pNBS1, pATM and γH2AX) was assessed by means of immunohistochemistry in bone marrow biopsies of 74 patients with de novo myelodysplastic syndromes (MDS) and compared with 15 cases of de novo acute myeloid leukemia (AML) and 20 with reactive bone marrow histology. Expression levels were significantly increased in both MDS and AML, compared to controls, being higher in high-risk than in low-risk MDS. Increased pNBS1 and γH2AX expression possessed a significant negative prognostic impact for overall survival in MDS patients, whereas pNBS1 was an independent marker of poor prognosis. © 2013 Elsevier Ltd.},
 bibtype = {article},
 author = {Kefala, Maria and Papageorgiou, Sotirios G. and Kontos, Christos K. and Economopoulou, Panagiota and Tsanas, Athanasios and Pappa, Vasiliki and Panayiotides, Ioannis G. and Gorgoulis, Vassilios G. and Patsouris, Eustratios and Foukas, Periklis G.},
 doi = {10.1016/j.leukres.2013.08.018},
 journal = {Leukemia Research},
 number = {11}
}

The expression of activated forms of key proteins of the DNA damage response machinery (pNBS1, pATM and γH2AX) was assessed by means of immunohistochemistry in bone marrow biopsies of 74 patients with de novo myelodysplastic syndromes (MDS) and compared with 15 cases of de novo acute myeloid leukemia (AML) and 20 with reactive bone marrow histology. Expression levels were significantly increased in both MDS and AML, compared to controls, being higher in high-risk than in low-risk MDS. Increased pNBS1 and γH2AX expression possessed a significant negative prognostic impact for overall survival in MDS patients, whereas pNBS1 was an independent marker of poor prognosis. © 2013 Elsevier Ltd.

@article{
 title = {Novel speech signal processing algorithms for high-accuracy classification of Parkinsons disease},
 type = {article},
 year = {2012},
 keywords = {Decision support tool,Parkinsons disease (PD),feature selection (FS),nonlinear speech signal processing,random forests (RF),support vector machines (SVM)},
 pages = {1264-1271},
 volume = {59},
 id = {15a7c5cb-fb0b-3f7c-a46c-eadcf5ff8f6d},
 created = {2021-06-04T16:37:47.129Z},
 file_attached = {true},
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 last_modified = {2022-02-18T17:23:01.032Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {21151807-05f9-4060-8027-4e549a9ef4fc,32cb27e8-8fa5-41ca-aff1-a5102f58e5f8},
 private_publication = {false},
 abstract = {There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.},
 bibtype = {article},
 author = {Tsanas, Athanasios and Little, Max A and McSharry, Patrick E and Spielman, Jennifer and Ramig, Lorraine O},
 doi = {10.1109/TBME.2012.2183367},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {5}
}

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

@inproceedings{
 title = {Robust parsimonious selection of dysphonia measures for telemonitoring of parkinson's disease symptom severity},
 type = {inproceedings},
 year = {2011},
 keywords = {Feature selection,Parkinson's disease,Telemedicine,Unified parkinson's disease rating scale},
 pages = {169-172},
 id = {a3cf3b8e-b754-338d-9fce-0febb7b2c7cc},
 created = {2018-02-28T16:13:00.042Z},
 file_attached = {false},
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 last_modified = {2021-01-22T08:15:48.404Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2011 Firenze University Press. Parkinson's disease (PD) symptom severity is typically quantified using the standard clinical metric Unified Parkinson's Disease Rating Scale (UPDRS) which spans the range 0-176 (0 denotes healthy). This assessment requires the patient's physical presence in the clinic, is time consuming, and relies on the clinical rater's subjective evaluation and experience; practice has shown that expert clinicians might differ by as much as 4-5 UPDRS points in their evaluations. We had previously developed a statistical machine learning framework which enables accurate and objective quantification of average PD symptom severity using exclusively speech signals. for this purpose, we evaluated 132 speech signal processing algorithms (dysphonia measures), which attempt to capture distinctive characteristics in PD subjects' voice. on a very large database of about 6,000 phonations, we could replicate the clinical experts' assessments within less than two UPDRS points' error. in this paper, we focus on identifying the most successful of the original 132 dysphonia measures in estimating UPDRS using five robust feature selection techniques. We demonstrate that we can improve on our previous findings using only 15 dysphonia measures, where the selected measures also tentatively indicate the most representative pathophysiological characteristics in male and female PD voices.},
 bibtype = {inproceedings},
 author = {Tsanas, A. and Little, M.A. and McSharry, P.E. and Ramig, L.O.},
 booktitle = {Models and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011}
}

© 2011 Firenze University Press. Parkinson's disease (PD) symptom severity is typically quantified using the standard clinical metric Unified Parkinson's Disease Rating Scale (UPDRS) which spans the range 0-176 (0 denotes healthy). This assessment requires the patient's physical presence in the clinic, is time consuming, and relies on the clinical rater's subjective evaluation and experience; practice has shown that expert clinicians might differ by as much as 4-5 UPDRS points in their evaluations. We had previously developed a statistical machine learning framework which enables accurate and objective quantification of average PD symptom severity using exclusively speech signals. for this purpose, we evaluated 132 speech signal processing algorithms (dysphonia measures), which attempt to capture distinctive characteristics in PD subjects' voice. on a very large database of about 6,000 phonations, we could replicate the clinical experts' assessments within less than two UPDRS points' error. in this paper, we focus on identifying the most successful of the original 132 dysphonia measures in estimating UPDRS using five robust feature selection techniques. We demonstrate that we can improve on our previous findings using only 15 dysphonia measures, where the selected measures also tentatively indicate the most representative pathophysiological characteristics in male and female PD voices.

@article{
 title = {Accurate Telemonitoring of Parkinson’s Disease Progression by Noninvasive Speech Tests},
 type = {article},
 year = {2010},
 pages = {884-893},
 volume = {57},
 id = {9f02fedc-2811-3f84-baa4-d6a36ee39e33},
 created = {2013-01-21T21:22:54.000Z},
 file_attached = {true},
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 bibtype = {article},
 author = {Tsanas, Athanasios and Little, Max A and Mcsharry, Patrick E and Member, Senior and Ramig, Lorraine O},
 journal = {IEEE transactions on biomedical engineering},
 number = {4}
}

@phdthesis{
 title = {Practical telemonitoring of Parkinson’ s disease using nonlinear onlinear speech signal processing},
 type = {phdthesis},
 year = {2010},
 issue = {January},
 id = {1f173e42-2400-3227-81d7-db3d5b779aa0},
 created = {2020-05-08T22:10:42.936Z},
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}