Correlation and Relief Attribute Rank-based Feature Selection Methods for Detection of Alcoholic Disorder Using Electroencephalogram Signals

Electroencephalogram signals capture the brain electrical activity and provide factual cues to examine the current condition of a person which can be efficacious to understand and analyze the performance of the brain's functioning. EEG signal is used in the diagnosis and monitoring of many brain-related diseases and mental disorders such as seizure detection, sleep disorders, alcoholism, etc. The incessant and uncontrolled alcohol consumption can critically affect the brain's functionality and inevitably lead to an Alcoholic Disorder (AD). The prime objective of this paper is to classify alcoholic and controlled subjects based on the detailed interpretation of their recorded EEG signals. In this paper, an alcoholism detection model is proposed using the combination of linear and non-linear features. The most descriptive features are extracted from EEG signals and two techniques namely Correlation-based and Relief attribute rank-based feature selection methods are being used to select the most prominent features to fulfil the objective. The selected features are considered as input to the various classifiers including SVM, LS-SVM, k-NN and Weighted k-NN to discriminate the alcoholic and controlled group. The performance of the proposed methodology is assessed using accuracy, sensitivity, specificity, confusion matrix and ROC metrices. The obtained results indicate that correlation-based selected features outperformed using LS-SVM classifiers with the highest sensitivity, specificity and accuracy of 100%, 99% and 99.5%, respectively. The area under curve for the LS-SVM classifier by implementing the features selected through correlation rank was found to be 1 which specify the best classification result.