Key result
An SVM classifier using the Bhattacharyya ranking method achieved 95.89% accuracy, 94.43% sensitivity, and 96.67% specificity in distinguishing between sober and drunken EEG signals.
Why the study?
The study aimed to apply nonlinear dynamics theory to EEG signals to better comprehend the range of alcoholic mental states and automatically distinguish between sober and drunken EEG signals.
An SVM classifier using entropy-based features and Bhattacharyya ranking can accurately distinguish between sober and drunken EEG signals, offering a potential decision-support tool for diagnosing alcoholism.
Hypothesis-generating for EEG-based alcohol detection; requires prospective clinical validation before any practice change.
BACKGROUND: Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve. OBJECTIVE: The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. METHODS: The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity. RESULTS: Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method. CONCLUSION: From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.
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Shukla et al. (2023) studied Alcoholism / drunken vs sober mental states. SVM classifier using Bhattacharyya ranking method was evaluated on Classification accuracy for distinguishing sober and drunken EEG signals. An SVM classifier using the Bhattacharyya ranking method achieved 95.89% accuracy, 94.43% sensitivity, and 96.67% specificity in distinguishing between sober and drunken EEG signals.
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