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Neurology Research International
Volume 2018, Article ID 8192820, 8 pages
Research Article

Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?

1Department of Electronics and Communication Engineering, SCT College of Engineering, Thiruvananthapuram, Kerala, India
2Department of Neurology, SCTIMST, Thiruvananthapuram, Kerala, India
3Department of ECE, TKM College of Engineering, Kollam, Kerala, India
4Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India
5Department of Anatomy, Government Medical College, Thiruvananthapuram, Kerala, India

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

Received 21 January 2018; Revised 5 April 2018; Accepted 23 April 2018; Published 29 May 2018

Academic Editor: Vincenzo Di Lazzaro

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.


Chaotic analysis is a relatively novel area in the study of physiological signals. Chaotic features of electroencephalogram have been analyzed in various disease states like epilepsy, Alzheimer’s disease, sleep disorders, and depression. All these diseases have primary involvement of the brain. Our study examines the chaotic parameters in metabolic encephalopathy, where the brain functions are involved secondary to a metabolic disturbance. Our analysis clearly showed significant lower values for chaotic parameters, correlation dimension, and largest Lyapunov exponent for EEG in patients with metabolic encephalopathy compared to normal EEG. The chaotic features of EEG have been shown in previous studies to be an indicator of the complexity of brain dynamics. The smaller values of chaotic features for encephalopathy suggest that normal complexity of brain function is reduced in encephalopathy. To the best knowledge of the authors, no similar work has been reported on metabolic encephalopathy. This finding may be useful to understand the neurobiological phenomena in encephalopathy. These chaotic features are then utilized as feature sets for Support Vector Machine classifier to identify cases of encephalopathy from normal healthy subjects yielding high values of accuracy. Thus, we infer that chaotic measures are EEG parameters sensitive to functional alterations of the brain, caused by encephalopathy.