Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification.

ABSTRACT:

Background

Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification.

Methods

To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-leng

SUBMITTER: Choi J 

PROVIDER: S-EPMC10017951 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature