Analysis of alcoholic EEG signals based on horizontal visibility graph entropy

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, $$C$$C1, $$C$$C3 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.