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Neurology Research International
Volume 2018, Article ID 1613456, 9 pages
https://doi.org/10.1155/2018/1613456
Research Article

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

1Department of Electronics and Communication Engineering, SCT College of Engineering, Thiruvananthapuram, Kerala, India
2Department of Electronics and Communication Engineering, TKM College of Engineering, Kollam, Kerala, India
3Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India
4Department of Neurology, SCTIMST, Thiruvananthapuram, Kerala, India

Correspondence should be addressed to Jisu Elsa Jacob; moc.liamg@asleusij

Received 28 January 2018; Revised 7 May 2018; Accepted 29 May 2018; Published 2 July 2018

Academic Editor: Herbert Brok

Copyright © 2018 Jisu Elsa Jacob et al. 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

EEG analysis in the field of neurology is customarily done using frequency domain methods like fast Fourier transform. A complex biomedical signal such as EEG is best analysed using a time-frequency algorithm. Wavelet decomposition based analysis is a relatively novel area in EEG analysis and for extracting its subbands. This work aims at exploring the use of discrete wavelet transform for extracting EEG subbands in encephalopathy. The subband energies were then calculated and given as feature sets to SVM classifier for identifying cases of encephalopathy from normal healthy subjects. Out of various combinations of subband energies, energy of delta subband yielded highest performance parameters for SVM classifier with an accuracy of 90.4% in identifying encephalopathy cases.