Electrodermal activity based autonomic sleep staging using wrist wearable

A.S. Anusha,S.P. Preejith,Tony J. Akl,Mohanasankar Sivaprakasam

Biomedical Signal Processing and Control（2022）

引用 3|浏览6

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摘要

•Electrodermal Activity (EDA) recorded from the wrist can be an effective biomarker for different stages of sleep.•Skin temperature (ST) measured from the same skin site as EDA can enhance the performance of EDA-based sleep staging algorithms.•Utilizing the natural sequencing information of sleep stages (wake followed by light, deep, and REM sleep phases in that order) in machine. learning based sleep staging models can improve the classification performance.

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关键词

Autonomic sleep staging,Electrodermal activity (EDA),Feature selection,Machine learning models,Wearables

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