Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD

Abstract

With the rapid innovation in the field of healthcare, various biomedical signals, namely, electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), play a crucial role for accurate measurement of various diseases such as cardiovascular diseases, brain disorders, etc. In the present work, an efficient method based on empirical mode decomposition (EMD) has been proposed to detect the epileptic activity. The present study is composed of three parts. In the first part, EMD is used to decompose the EEG signal into a set of amplitude modulated and frequency modulated components, referred to as intrinsic mode functions (IMFs). In the second part, features such as standard deviation, kurtosis, and Hjorth parameters have been extracted from various IMFs. In the last stage, the features are employed as inputs to support vector machine classifier for classification between non-seizure and seizure EEG signals. The simulation results show that the proposed scheme has attained better classification accuracy when compared to existing state-of-the-art methods.