Adaptive Sleep-Wake Discrimination for Wearable Devices

Sleep/wake classification systems that rely on physiological signals suffer from inter subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique tha...

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Vydáno v:	IEEE transactions on biomedical engineering Ročník 58; číslo 4; s. 920 - 926
Hlavní autoři:	Karlen, Walter, Floreano, Dario
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.04.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adaptation Algorithm design and analysis Algorithms Applied sciences Artificial intelligence Artificial neural networks Biological and medical sciences Cardiology Computer science; control theory; systems context awareness Diagnosis, Computer-Assisted - instrumentation Diagnosis, Computer-Assisted - methods Electrodiagnosis. Electric activity recording Equipment Design Equipment Failure Analysis Exact sciences and technology Humans Information, signal and communications theory Investigative techniques, diagnostic techniques (general aspects) Medical sciences personal health physiological signal classification point-of-care Polysomnography - instrumentation Polysomnography - methods Reproducibility of Results Sensitivity and Specificity Signal and communications theory Signal Processing, Computer-Assisted - instrumentation Signal representation. Spectral analysis Signal, noise Sleep Sleep - physiology Studies Telecommunications and information theory Topology Training Wakefulness - physiology wearable wearable physiological signal classification Neural network Algorithm Electrodiagnosis Sleep Wakefulness Point of care testing Accelerometer Electrocardiography Portable equipment context awareness personal health Sleep wake cycle Respiration Artificial intelligence Signal analysis point-of-care Biomedical engineering Adaptation
ISSN:	0018-9294, 1558-2531, 1558-2531
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Shrnutí:	Sleep/wake classification systems that rely on physiological signals suffer from inter subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 ObjectType-Conference-1 SourceType-Conference Papers & Proceedings-2
ISSN:	0018-9294 1558-2531 1558-2531
DOI:	10.1109/TBME.2010.2097261