Open Access

ARTICLE

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

Jiahui Pan^1,6,*, Jianhao Zhang¹, Fei Wang^1,6, Wuhan Liu², Haiyun Huang^3,6, Weishun Tang³, Huijian Liao⁴, Man Li⁵, Jianhui Wu¹, Xueli Li², Dongming Quan², Yuanqing Li^3,6

1 School of Software, South China Normal University, Foshan, 528225, China
2 Department of Sleep Medicine, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510180, China
3 School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
4 Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
5 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
6 Pazhou Lab, Guangzhou, 510330, China

* Corresponding Author: Jiahui Pan. Email:

Intelligent Automation & Soft Computing 2021, 28(1), 53-71. https://doi.org/10.32604/iasc.2021.015970

Received 16 December 2020; Accepted 16 January 2021; Issue published 17 March 2021

Abstract

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.

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Citations