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BioMed Research International
Volume 2015, Article ID 638036, 14 pages
http://dx.doi.org/10.1155/2015/638036
Research Article

Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy

1SMME, National University of Sciences & Technology, Islamabad 44000, Pakistan
2Department of Electrical Engineering, COMSATS Institute of IT, Islamabad 44000, Pakistan
3King Saud University, P.O. Box 92144, Riyadh 11543, Saudi Arabia
4Lahore University of Management Sciences, Lahore 54000, Pakistan
5Department of Computer Science, COMSATS Institute of IT, Islamabad 44000, Pakistan

Received 15 September 2014; Revised 21 December 2014; Accepted 18 January 2015

Academic Editor: Tobias Loddenkemper

Copyright © 2015 Malik Anas Ahmad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system’s incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.