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Employment and Comparison of Different Artificial Neural Networks for Epilepsy Diagnosis from EEG Signals

Journal of Medical Systems volume 36, pages 347–362 (2012)Cite this article

Abstract

In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals’ automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.

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Acknowledgements

This study has been conducted as the Graduate Thesis of Esma SEZER from S.U. Institute of Science. We would like to give our special thanks to Selcuk University for their material support and contributions towards scientific research projects [34].

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Affiliations

  1. Department of Electronic and Computer Education, Technical Education Faculty, Selcuk University, 42005, Konya, Turkey

    Esma Sezer, Hakan Işik & Esra Saracoğlu

Authors
  1. Esma Sezer
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  2. Hakan Işik
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  3. Esra Saracoğlu
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Correspondence to Hakan Işik.

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Sezer, E., Işik, H. & Saracoğlu, E. Employment and Comparison of Different Artificial Neural Networks for Epilepsy Diagnosis from EEG Signals. J Med Syst 36, 347–362 (2012). https://doi.org/10.1007/s10916-010-9480-5

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  • DOI: https://doi.org/10.1007/s10916-010-9480-5

Keywords

  • Epilepsy
  • Electroencephalography (EEG)
  • Wavelet transform
  • Principal Component Analysis (PCA)
  • Artificial Neural Networks (ANN)
  • Roc analysis