Daubechies Wavelet Neural Network Classifier for the Diagnosis of Epilepsy
نویسندگان
چکیده
Epilepsy is one of the major fields of application of EEG. Now a days, identification of epilepsy is accomplished manually by skilled neurologist. Those are very small in number. In this work, we propose a methodology for automatic detection of normal, interictal and ictal conditions from recorded of EEG signals. We used the wavelet transform for the feature extraction and obtained statistical parameters from the decomposed wavelet coefficients. The Generalized Feed Forward Neural Network (GFFNN), Multilayer Perceptron (MLP), Elman Neural Network (ENN) and Support Vector Machine (SVM) are used for the classification. The performance of the proposed system was evaluated in terms of classification accuracy, sensitivity, specificity and overall accuracy.
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