Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal

Zhu, Guohun and Li, Yan and Wen, Peng (Paul) (2014) Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE Journal of Biomedical and Health Informatics, 18 (6). pp. 1813-1821. ISSN 2168-2194

Text (Published Version) Zhu_Li_Wen_IJBHI_v18n6_PV.pdf Restricted

Abstract

The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P(k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of $P$ $(k)$ from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

Statistics for this ePrint Item

Item Type:	Article (Commonwealth Reporting Category C)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre:	Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre:	Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited:	31 Mar 2015 00:13
Last Modified:	18 Dec 2017 06:24
Uncontrolled Keywords:	classification; degree distribution (DD); difference visibility graph (DVG); electroencephalogram (EEG); single channel
Fields of Research (2008):	09 Engineering > 0903 Biomedical Engineering > 090304 Medical Devices 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing 08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software
Fields of Research (2020):	40 ENGINEERING > 4003 Biomedical engineering > 400308 Medical devices 40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460103 Applications in life sciences
Socio-Economic Objectives (2008):	E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI:	https://doi.org/10.1109/JBHI.2014.2303991
URI:	http://eprints.usq.edu.au/id/eprint/26986

Actions (login required)

Archive Repository Staff Only