Selection of Intrinsic Mode Functions for Epileptic EEG Classification Using Ensemble Empirical Mode Decomposition

In this study, it is aimed to select the Intrinsic mode functions that can best distinguish pre-seizure and seizure segments of epileptic EEG signals by using the Intrinsic mode functions (IMF) obtained by Ensemble Empirical Mode Decomposition (EEMD) method. In our study, a hybrid method was proposed based on various IMF selection methods, and the first 3 IMFs were found to have the highest priority. In order to determine the contribution of IMF selection to the classification accuracy, various spectral features were calculated and the classification was performed by using Support Vector Machines, Naive Bayes, K-Nearest Neighbor, and Linear Discriminant Analysis methods. Upon checking the classification results obtained using the first 3 IMFs, it is observed that the classification accuracy is higher with the features obtained using first MF which was found to have the highest priority at the IMF selection process.