Epileptic Seizure Prediction in EEG Signals using EMD and DWT

Abstract

In this study, normal (healthy), pre-seizure and seizure states were analyzed using EEG data from BONN database. In this paper, we propose a new method of using SVM, KNN and Decision Tree for classification analysis in order to improve the detection accuracy of seizure. First, due to the presence of significant noise in the EEG signals, for signal pre-processing we performed noise removal. Second, two methods of frequency space transformation were used such as Discrete Wavelet Transform (DWT), separating signal into sub-bands and Empirical Mode Decomposition (EMD) technique to decompose signal into the Intrinsic Mode Functions (IMFs). Before the classification, the statistical moments of the signals in the frequency domain were obtained for feature extraction. Using these features, the performances of Support Vector Machines (SVM), Decision Tree, K-nearest Neighborhood (KNN) classifiers were analyzed. The experiment results show that the most accurate detection of epilepsy was obtained by applying EMD method with classifiers SVM, KNN and Decision Tree, and such algorithm with EMD can achieve accuracy for normal, pre-seizure, and seizure equal to 100%, 100% and 96.67%, respectively.