A new approach for EEG signal classification of schizophrenic and control participants

Authors:
M. Sabeti;S. D. Katebi;R. Boostani;G. W. Price
Affiliations:
Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran;School of Psychiatry and Clinical Neuroscience and Centre for Clinical Research in Neuropsychiatry, University of Western Australia and Graylands Hospital, Perth, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper is concerned with a two stage procedure for analysis and classification of electroencephalogram (EEG) signals for twenty schizophrenic patients and twenty age-matched control participants. For each case, 20 channels of EEG are recorded. First, the more informative channels are selected using the mutual information techniques. Then, genetic programming is employed to select the best features from the selected channels. Several features including autoregressive model parameters, band power and fractal dimension are used for the purpose of classification. Both linear discriminant analysis (LDA) and adaptive boosting (Adaboost) are trained using tenfold cross validation to classify the reduced feature set and a classification accuracy of 85.90% and 91.94% is obtained by LDA and Adaboost, respectively. Another interesting observation from the channel selection procedure is that most of the selected channels are located in the prefrontal and temporal lobes confirming neuropsychological and neuroanatomical findings. The results obtained by the proposed approach are compared with a one stage procedure, the principal component analysis (PCA)-based feature selection, utilizing only 100 features selected from all channels. It is illustrated that the two stage procedure consisting of channel selection followed by feature reduction gives a more enhanced results in an efficient computation time.