Sleep and Wake Classification With ECG and Respiratory Effort Signals

Sleep and Wake Classification With ECG and Respiratory Effort Signals. Karlen, W., Mattiussi, C., & Floreano, D. IEEE Transactions on Biomedical Circuits and Systems, 3(2):71-8, 2009.

Paper doi abstract bibtex

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

@article{
 title = {Sleep and Wake Classification With ECG and Respiratory Effort Signals},
 type = {article},
 year = {2009},
 keywords = {Biomedical signal analysis,electrocardiography,neural classifier,respiratory effort,sleep and wake classification,wearable computing},
 pages = {71-8},
 volume = {3},
 id = {f6be7f1e-5996-3ac0-a2cb-ef342110226c},
 created = {2009-07-30T21:38:41.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:12:10.253Z},
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 authored = {true},
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 citation_key = {Karlen2009},
 notes = {IF-2018: 4.25},
 folder_uuids = {4afa9434-d8d6-102e-ac9a-0024e85ead87,f1f67efc-95a7-4f1a-b181-c3670c667a34,470e1c72-5ded-4e47-a429-6f5172e01dcb,9aa961a4-04a6-4a65-b081-d534c7a81a35,4afa90e2-d8d6-102e-ac9a-0024e85ead87,60555479-b7f0-45f5-aa97-a3920f93c426,4afa922c-d8d6-102e-ac9a-0024e85ead87,4afa909c-d8d6-102e-ac9a-0024e85ead87,7ecba661-0765-42d5-a37e-6a57b331e28c,284ba051-45b0-400f-9d1a-c93fb454df3b,4afa8d9a-d8d6-102e-ac9a-0024e85ead87},
 private_publication = {false},
 abstract = {We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a fast Fourier transform as the main feature extraction tool and a feedforward artificial neural network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify, on average, 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, receiver operating characteristic analysis shows that compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.},
 bibtype = {article},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 doi = {10.1109/TBCAS.2008.2008817},
 journal = {IEEE Transactions on Biomedical Circuits and Systems},
 number = {2}
}

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