Classification of EEG signals to detect alcoholism using machine learning techniques

• Published in: Pattern recognition letters 2019-07, Vol.125, p.140-149
• Main Authors: Rodrigues, Jardel das C., Filho, Pedro P. Rebouças, Peixoto, Eugenio, N, Arun Kumar, de Albuquerque, Victor Hugo C.
• Format: Article
• Language: English
• Subjects: Alcoholic signals; Electroencephalogram (EEG); Multi-layer Perceptron (MLP); Nave Bayes; Optimum Path Forest (OPF); Support Vector Machine (SVM); Wavelet packet decomposition
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2019.04.019

•EEG signals decomposition using Biorthogonal, Coiflet, Daubechies, and Symlets wavelet family.•Assessment of five machine learning techniques to detect alcoholism through EEG signal classification.•Comparison between the SVM, OPF, MLP, k-NN, and Bayesian classifiers.•The results confirm that it is possible diagnose alcoholism with high precision. The diagnosis of alcoholism is of great importance not only due to its effects on the individual and society but also the costs to the national health systems. Moreover, there are a large number of people suffering from this disease worldwide. Alcoholism has critical pathological effects on the liver, immune system, brain, and heart. Machine learning techniques are already well known for the classification of biosignals as they offer an efficient way to assist professionals in the automated diagnosis of various diseases, with high accuracy rates. This work presents the classification of alcoholic electroencephalographic (EEG) signals using Wavelet Packet Decomposition (WPD) and machine learning techniques. The experiments were realized using the minimum value, maximum value, mean, standard deviation, power value, the ratio of absolute mean and the absolute mean as features to feed the classifiers. These features were combined with the objective of exploring the feasibility of such features to classify alcoholism. The classification task was performed using Support Vector Machine (SVM), Optimum-Path Forest (OPF), Nave Bayes, k-Nearest Neighbors (k-NN) and Multi-layer Perceptron (MLP). The results showed maximum values of 99.87% for specificity, sensitivity, positive predictive value (PPV), and accuracy. These results were generated using the Nave Bayes classifier and the Biorthogonal wavelet family. A comparison with other techniques was performed aiming to validate our approach. The promising results, the inclusion of OPF classifier, and the specific combinations involving the chosen classifiers and wavelet families are the main contributions of this work. Finally, our strategy proved to be very effective in classifying alcoholic EEG signals.