Abstract
In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.
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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 Selçuk University for their material support and contributions towards scientific research projects [5].
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Işik, H., Sezer, E. Diagnosis of Epilepsy from Electroencephalography Signals Using Multilayer Perceptron and Elman Artificial Neural Networks and Wavelet Transform. J Med Syst 36, 1–13 (2012). https://doi.org/10.1007/s10916-010-9440-0
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DOI: https://doi.org/10.1007/s10916-010-9440-0