Automatic Sleep Staging Based on EEG-EOG Signals for Depression Detection

In this paper, an automatic sleep scoring system based on electroencephalogram (EEG) and electrooculogram (EOG) signals was proposed for sleep stage classification and depression detection. Our automatic sleep stage classification method contained preprocessing based on independent component analysis, feature extraction including spectral features, spectral edge frequency features, absolute spectral power, statistical features, Hjorth features, maximum-minimum distance and energy features, and a modified ReliefF feature selection. Finally, a support vector machine was employed to classify four states (awake, light sleep [LS], slow-wave sleep [SWS] and rapid eye movement [REM]). The overall accuracy of the Sleep-EDF database reached 90.10 ? 2.68% with a kappa coefficient of 0.87 ? 0.04. Furthermore, a depression recognition method was developed to distinguish the patients with depression from healthy subjects. Specifically, according to the differences in sleep patterns between the two groups, REM latency, sleep latency, LS proportion, SWS proportion, sleep maintenance and arousal times were employed in this study. Sleep data from 12 healthy individuals and 19 patients with depression were applied to the system. The accuracy of the recognition results reached 95.24%, thus verifying the feasibility of our approach.

Automated summary

An automatic sleep scoring system based on EEG and EOG signals was proposed for sleep stage classification and depression detection, achieving an overall accuracy of 90.10% for sleep stage classification and 95.24% for depression recognition, validating the feasibility of the approach.