Key result
The horizontal visibility graph entropy method achieved 95.8% classification accuracy using 13 optimal channels, outperforming the maximum 95.2% accuracy of the sample entropy method using all channels.
Why the study?
Does a horizontal visibility graph entropy (HVGE) approach improve the classification accuracy of EEG signals for identifying alcoholism compared to sample entropy?
Does a horizontal visibility graph entropy (HVGE) approach improve the classification accuracy of EEG signals for identifying alcoholism compared to sample entropy?
Absolute Event Rate: 95.8% vs 95.2%
The HVGE method provides high accuracy for identifying alcoholism from EEG signals, outperforming traditional sample entropy methods.
May support efficient high-accuracy classification in research; leaves open prospective clinical validation.
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, [Formula: see text]1, [Formula: see text]3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 [Formula: see text] with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 [Formula: see text]. 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 [Formula: see text] 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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Zhu et al. (2014) studied Alcoholism (n=122). Horizontal visibility graph entropy (HVGE) vs. Sample entropy (SaE) was evaluated on Classification accuracy. The horizontal visibility graph entropy method achieved 95.8% classification accuracy using 13 optimal channels, outperforming the maximum 95.2% accuracy of the sample entropy method using all channels.
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