Analysis of alcoholic EEG signals based on horizontal visibility graph entropy
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, \(C\)1, \(C\)3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation 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 associated cannot 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.
alcoholism graph entropy classification multi-channel eeg slow waves