Non Linear Autoregressive Model for Detecting Chronic Alcoholism
In this study, the Non Linear Autoregressive model of the resting electroencephalogram (EEG) was examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic (n = 20) conditions and the control group (n = 20). Data were taken from motor cortex region. The dimension of the extracted features are reduced by linear discrimination analysis (LDA) and classified by Support Vector Machine (SVM). The 600 sample from each group gave the best result using Support Vector Machine classifier. The maximum classification accuracy (90 %) with SVM clustering was achieved with the EEG Fz channel. More alterations are identified in the left hemisphere. Considering the good classification accuracy with SVM on Fz electrode, it can be suggested that the non-invasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.
KeywordsAlcohol Cerebral motor cortex Electroencephalogram LDA Non linear autoregressive model Support vector machine
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