Dual-Tree Complex Wavelet Transform-Based Features for Automated Alcoholism Identification

Abstract

A novel automated system for the identification of alcoholic subjects using electroencephalography (EEG) signals is proposed in this study. The proposed system employed dual-tree complex wavelet transform (DTCWT)-based features and sequential minimal optimization support vector machine (SMO-SVM), least square support vector machine (LS-SVM), and fuzzy Sugeno classifiers (FSC) for the automated identification of alcoholic EEG signals. The EEG signals are decomposed into several sub-bands (SBs) using DTCWT. The features extracted from DTCWT-based SBs are fed to FSC, SMO-SVM, and LS-SVM classifiers to evaluate the best performing classifier. The tenfold cross-validation scheme is used to mitigate the overfitting of the model. We have obtained the highest classification accuracy (CAC) of 97.91%, the area under receiver operating characteristic curve (AU-ROC) of 0.999 and Matthews correlation coefficient (MCC) of 0.958 for our proposed alcoholic diagnosis model. Our alcoholism detection system performed better than the existing systems in terms of all three measures: CAC, AU-ROC, and MCC.