Automatic sleep staging with photoplethysmography and accelerometer in a community-based population
Journal
Sleep
Journal Volume
44
Journal Issue
Supplement_2
Start Page
A109
End Page
A109
ISSN
0161-8105
1550-9109
Date Issued
2021-05-01
Author(s)
Abstract
Abstract Introduction The study aims to validate the automatic sleep staging system (ASSS) with photoplethysmography (PPG) and accelerometers embedded in smart watches in community-based population Methods 75 healthy subjects were randomly recruited form 304 staffs in an industrial firm who volunteered for this study. A four-stage classifier was designed based on Linear Discriminant Analysis using PPG and accelerometers. To better validate the system performance, the leave-one-out approach was applied in this study. The performance of ASSS was assessed with the epoch-by-epoch and whole-night agreement for sleep staging against manual scoring of overnight polysomnography. Results The mean agreement of four stages across all subjects was 61.1% (95% CI, 58.9-63.2) with kappa 0.55 (0.52-0.58). The mean agreement for wake, light sleep (LS), deep sleep (DS), and REM was 53.4%, 84.1%, 40.3%, 75.6%, respectively. The whole-night agreement was good-excellent (Intra-class correlation coefficient, 0.74 to 0.84) for total sleep time, sleep efficiency, wake after sleep onset, and duration of wake and REM. The agreement was fair for sleep onset and LS duration, but poor for DS duration. Conclusion Our result showed that PPG and accelerometers based smart watches have proper validity for automatic sleep staging in the community-based population. Support (if any) “Center for electronics technology integration (NTU-107L900502, 108L900502, 109-2314-B-002-252)” from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan; MediaTek Inc (201802034 RIPD).
Publisher
Oxford University Press (OUP)
Type
journal article