title: Adaptive sleep-wake discrimination for wearable devices.
abstract: Sleep/wake classification systems that rely on physiological signals suffer from intersubject 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.
authors.affiliation: Electrical and Computer Engineering in Medicine Group, the University of British Columbia, Vancouver, BC V6T 1Z4, Canada. walterk@ece.ubc.ca
authors.forename: Walter
authors.initials: W
authors.lastname: Karlen
authors.forename: Dario
authors.initials: D
authors.lastname: Floreano
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D017286:Polysomnography / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012890:Sleep / Q000502:physiology*; D014851:Wakefulness / Q000502:physiology*