Analysis of alcoholic EEG signals based on horizontal visibility graph entropy
Article
Article Title | Analysis of alcoholic EEG signals based on horizontal visibility graph entropy |
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ERA Journal ID | 211938 |
Article Category | Article |
Authors | Zhu, Guohun (Author), Li, Yan (Author), Wen, Peng (Author) and Wang, Shuaifang (Author) |
Journal Title | Brain Informatics |
Journal Citation | 1, pp. 19-25 |
Number of Pages | 7 |
Year | 2014 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2198-4018 |
2198-4026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s40708-014-0003-x |
Web Address (URL) | https://link.springer.com/article/10.1007/s40708-014-0003-x |
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. |
Keywords | multi-channel EEG; alcoholism; graph entropy; slow waves; classification |
ANZSRC Field of Research 2020 | 460912. Knowledge and information management |
400607. Signal processing | |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | University of Southern Queensland |
https://research.usq.edu.au/item/q35wy/analysis-of-alcoholic-eeg-signals-based-on-horizontal-visibility-graph-entropy
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