Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography (original) (raw)

Study Objectives Multisensor wearable consumer devices allowing the collection of multiple data sources, such as heart rate and motion, for the evaluation of sleep in the home environment, are increasingly ubiquitous. However, the validity of such devices for sleep assessment has not been directly compared to alternatives such as wrist actigraphy or polysomnography (PSG). Methods Eight participants each completed four nights in a sleep laboratory, equipped with PSG and several wearable devices. Registered polysomnographic technologist-scored PSG served as ground truth for sleep–wake state. Wearable devices providing sleep–wake classification data were compared to PSG at both an epoch-by-epoch and night level. Data from multisensor wearables (Apple Watch and Oura Ring) were compared to data available from electrocardiography and a triaxial wrist actigraph to evaluate the quality and utility of heart rate and motion data. Machine learning methods were used to train and test sleep–wake...