A Novel Automated Empirical Mode Decomposition (EMD) Based Method and Spectral Feature Extraction for Epilepsy EEG Signals Classification

The increasing incidence of epilepsy has led to the need for automatic systems that can provide accurate diagnoses in order to improve the life quality of people suffering from this neurological disorder. This paper proposes a method to automatically classify epilepsy types using EEG recordings from two databases. This approach uses the spectral power density of intrinsic mode functions (IMFs) that are obtained through the empirical mode decomposition (EMD) of EEG signals. The spectral power density of IMFs has been applied as features for the classification of focal and non-focal, as well as of focal and generalized EEG signals. The data are then classified using K-nearest Neighbor (KNN) and Naive Bayes (NB) classifiers. The focal and non-focal data were classified with high accuracy, with KNN and NB classifiers achieving a maximum classification rate of 99.90% and 99.80%, respectively. Focal and generalized epilepsy data were classified with high rates of accuracy during wakefulness and sleep stages, with KNN achieving a maximum rate of 99.49% and NB achieving 99.20%. This method shows significant improvements in the classification of EEG signals in epilepsy compared to previous studies. It could potentially aid clinical decisions for epilepsy patients.