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

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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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 IEEE. Personal use is permitted, but republication or redistribution requires IEEE permission.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
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 : 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
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/JBHI.2014.2303991

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