Classification of alcohol abusers: an intelligent approach

In this paper we propose a novel method to classify alcohol abusers. The method described efficiently estimated total power in gamma band spectral power (GBSP) of multi-channel visual evoked potential (VEP) signals in the time domain, circumventing power spectrum computation. Then, the total power extracted are used as features to classify alcohol abusers from control subjects using multilayer perceptron-back propogation (MLP-BP) neural network classifier. As a comparison study the total power using GBSP feature extraction is repeated for four types of infinite impulse response (IIR) filters. Experimental study is conducted with 20 subjects totaling 800 VEP signals, which are extracted while subjects are seeing pictures from Snodgrass and Vanderwart set. Maximum classification of 91% is obtained for Elliptic filter for 20 hidden units. Also Elliptic filter shows the best performance for the averaged values of all the filters and it also has the lower order when compared to other filters.