An EEG-based Sleep Staging method with hybrid entropy computation measures

Abstract

Sleep is an indispensable physiological need of the human body. Sleep staging is an effective method to objectively assess sleep quality and is helpful for research on sleep and sleep-related diseases. Electroencephalogram (EEG) signals are nonlinear and non-stationary time series, and entropy features are particularly sensitive to these nonlinear characteristics and can reveal information that is difficult to discover with traditional linear analysis methods. We proposed an automatic sleep staging method based on EEG entropy computation, inlcuding signal preprocessing, entropy feature extraction, feature selection and lassification modules. The experimental results show that the average accruacy is 91.3% through the fused entropy features.