Analysis of alcoholic EEG signals based on horizontal visibility graph entropy

Zhu, Guohun and Li, Yan and Wen, Peng and Wang, Shuaifang (2014) Analysis of alcoholic EEG signals based on horizontal visibility graph entropy. Brain Informatics, 1. pp. 19-25. ISSN 2198-4018

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Abstract

This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, C1, C3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold crossvalidation is 87.5 % with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 %. In contrast, SaE features associatedcannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 % even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version made available in accordance with Creative Commons Attribution License 4.0.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 29 Jun 2016 04:52
Last Modified: 28 Apr 2017 02:31
Uncontrolled Keywords: multi-channel EEG; alcoholism; graph entropy; slow waves; classification
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080607 Information Engineering and Theory
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
Identification Number or DOI: 10.1007/s40708-014-0003-x
URI: http://eprints.usq.edu.au/id/eprint/28812

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