APPLICATION OF EMPIRICAL MODE DECOMPOSITION (EMD) FOR AUTOMATED DETECTION OF EPILEPSY USING EEG SIGNALS

Author:

MARTIS ROSHAN JOY1,ACHARYA U. RAJENDRA12,TAN JEN HONG1,PETZNICK ANDREA3,YANTI RATNA1,CHUA CHUA KUANG1,NG E. Y. K.4,TONG LOUIS5

Affiliation:

1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore

2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia

3. Singapore Eye Research Institute, Singapore

4. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore

5. Singapore National Eye Centre, Singapore

Abstract

Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

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