Computer Based Detection of Normal and Alcoholic Signals Using Discrete Fourier Transform

Abstract

Alcoholism is a severe, disorder that affects; the functionality of neurons in the central nervous system and leads to the loss of .health and wealth. The suggested technique applies statistical and fractal dimension (FD) features to classify alcoholic and normal subjects using eight channels under an SF-based machine learning architecture. Electroencephalogram (EEG) signals are placed in a framework and separated into different EEG bands using an orthogonal wavelet filter. The following three classification approaches are used to classify the alcoholic and normal patterns of EEG data: least-square support vector machine, vector machine (SVM), and Naïve Bayesian. Results showed that the best classification method was SVM with a sensitivity of 0.9267%, an accuracy of 0.9892%, and a specificity of 0.9916%.

Keywords

• alcoholism , electroencephalogram (eeg) , svm , lls-svm , naiva bayes classification