A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals

Alcoholism is a critical disorder related to the central nervous system, caused due to repeated and excessive consumption of alcohol. The electroencephalogram (EEG) signals are used to depict brain activities. It can also be employed for diagnosis of subjects consuming excessive alcohol. In this study, we have developed an automated system for the classification of alcoholic and normal EEG signals using a recently designed duration-bandwidth product (DBP), optimized three-band orthogonal wavelet filter bank (TBOWFB), and log-energy (LE). First, we obtain sub-bands (SBs) of EEG signals using the TBOWFB. Then, we use logarithms of the energies of the SBs as the discriminating features which are fed to the least square support vector machine (LS-SVM) for the discrimination of normal and alcoholic EEG signals. We have achieved a classification accuracy (CA) of 97.08%, with ten-fold cross validation strategy. The proposed model presents a promising performance, and therefore it can be used in a practical setup to assist the medical professionals in the diagnosis of alcoholism using EEG signals automatically.

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