Table of Contents
ISRN Biomedical Engineering
Volume 2013, Article ID 358108, 14 pages
http://dx.doi.org/10.1155/2013/358108
Research Article

A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

Electronics and Communication Department, Manipal Institute of Technology, Manipal 576104, India

Received 6 April 2013; Accepted 4 May 2013

Academic Editors: L. Faes and R. Grebe

Copyright © 2013 Chandrakar Kamath. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.