Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

ABSTRACT:

Background

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes.

Methods

We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent labo

SUBMITTER: Yuan H 

PROVIDER: S-EPMC10350137 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature