Adaptive Sleep-Wake Discrimination for Wearable Devices

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 4; pp. 920 - 926
• Main Authors: Karlen, Walter, Floreano, Dario
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TBME.2010.2097261

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.