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The Scientific World Journal
Volume 2015, Article ID 945689, 15 pages
Research Article

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

1Department of Computer Science & Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
2Computer Science Department, Graduate School of Computing, University Utara Malaysia (UUM), 06010 Sintok, Kedah, Malaysia

Received 8 June 2014; Revised 24 November 2014; Accepted 26 December 2014

Academic Editor: Juan R. Rabuñal

Copyright © 2015 Khalid Abualsaud 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.


Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when  dB, 84% when  dB, and 88% when  dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.