{"title":"Classification Control for Discrimination between Interictal Epileptic and Non \u2013 Epileptic Pathological EEG Events","authors":"Sozon H. Papavlasopoulos, Marios S. Poulos, George D. Bokos, Angelos M. Evangelou","volume":2,"journal":"International Journal of Psychological and Behavioral Sciences","pagesStart":144,"pagesEnd":152,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/12795","abstract":"
In this study, the problem of discriminating between interictal epileptic and non- epileptic pathological EEG cases, which present episodic loss of consciousness, investigated. We verify the accuracy of the feature extraction method of autocross-correlated coefficients which extracted and studied in previous study. For this purpose we used in one hand a suitable constructed artificial supervised LVQ1 neural network and in other a cross-correlation technique. To enforce the above verification we used a statistical procedure which based on a chi- square control. The classification and the statistical results showed that the proposed feature extraction is a significant accurate method for diagnostic discrimination cases between interictal and non-interictal EEG events and specifically the classification procedure showed that the LVQ neural method is superior than the cross-correlation one.<\/p>\r\n","references":"
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