Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy.

ABSTRACT:

Study objectives

The aim of this study was to develop a sleep staging classification model capable of accurately performing on different wearable devices.

Methods

Twenty-three healthy participants underwent a full-night type I polysomnography and used two device combinations: (A) flexible single-channel electroencephalogram (EEG) headband + actigraphy (n = 12) and (B) rigid single-channel EEG headband + actigraphy (n = 11). The signals were segmented into 30-second epochs according to polysomnographic stages (scored by a board-certified sleep technologist; model ground truth) and 18 frequency and time features were extracted. The model consisted of an ensemble of bagged decision trees. Bagging refers to bootstrap aggregation to reduce overfitting and improve generalization.

SUBMITTER: Melo MC 

PROVIDER: S-EPMC11145037 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature