A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.

ABSTRACT: Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep-wake classification with a high sensitivity (>90%) and specificity (>8

SUBMITTER: Ode KL 

PROVIDER: S-EPMC8784328 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature