Employment and comparison of different Artificial Neural Networks for epilepsy diagnosis from EEG signals.
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.
Sezer
Esma
E
Department of Electronic and Computer Education, Technical Education Faculty, Selcuk University, Konya, Turkey. sezer_esma@hotmail.com
Işik
Hakan
H
Saracoğlu
Esra
E
D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis