Enhanced Classification of Focal and Generalized Epilepsy Using EEMD and CEEMDAN Methods

The rising occurrence of epilepsy along with the intricate nature of focal epilepsy and its potential to progress to generalized epilepsy requires the advancement of intelligent systems capable of delivering precise diagnoses.This paper developed a novel approach for the classification of patients with focal and generalized epilepsy based on the analysis of EEG signals using the Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) techniques.These adaptive methods are applied to a comprehensive database consisting of EEG signals captured during sleep from patients with focal and generalized epilepsy.Feature extraction is performed on the resulting Intrinsic Mode Functions (IMFs) obtained from EEMD and CEEMDAN methods, four statistical features being computed for each extracted IMF.Finally, the K-nearest neighbors (KNN) and Naï ve Bayes (NB) classifiers are employed to accurately classify the EEG signals into focal and generalized categories.The combination of CEEMDAN-based approach and KNN classifier achieved the highest classification rate of 94.54%, exceeding the EEMD-based approach and KNN classifier, which attained a maximum classification rate of 93.32%.By means of the proposed methods, we aim to contribute to the development of effective and efficient diagnostic systems for epilepsy.