Adaptive Sleep-Wake Discrimination for Wearable Devices

Sleep/wake classification systems that rely on phys- iological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line 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 electrocardiogram and respiratory effort signals for the classification task and applied behavioral mea- surements (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 on- line sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.