Automated detection of epileptic seizures using mixed- methodology: Wavelet-Chaos-KNN Classifier-Mutual Information

摘要

Electroencephalogram (EEG) is the brain signal that contains the valuable infomiation about different states of the brain. In this study EEG signals are analyzed for evaluating epileptic seizures in these signals and their sub-bands and comparing epileptic states with other states. A discrete wavelet transform is applied for decompose the EEGs into its sub-bands. The chaotic behavior of EEGs is evaluated by means of normalized Shannon and spectral entropies. Entropy method is presented for detection of epileptic seizures through the analysis of EEGs and their sub-bands. At the end the mixture K-nearest neighbor and mutual information method is applied as a classifier to classify the different states in EEGs and their sub-bands. This method is applied to three different groups of EEG signals: 1) healthy states, 2) epileptic states during a seizure-free interval (interictal EEG), 3) epileptic states during a seizure (ictal EEG). The proposed method could classify different states with 99% accuracy. 【Keywords】discrete wavelet transform (DWT); normalized Shannon entropy (NShEn) normalized spectral entropy (NSEn); k-nearest neighbor (KNN); mutual information, encephalography, epileptic seizures.%Elektroencefalografia EEG jest analizą sygnału mózgu. W artykule przedstawiono metody analizy sygnału EEG stosowane w celu wykrycia epilepsji. Zastosowano dyskretną transformatę falkową do dekompozycji sygnału EEG. Wykorzystano metodę entropii do detekcji sygnału związanego z epilepsją. Metody zastosowano do trzech grup pacjentów: zdrowych, chorych na epilepsję i chorych w czasie ataku epilepsji.