Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Neurology Research International |
| Online Access: | http://dx.doi.org/10.1155/2018/8192820 |
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| author | Jisu Elsa Jacob Ajith Cherian K. Gopakumar Thomas Iype Doris George Yohannan K. P. Divya |
| author_facet | Jisu Elsa Jacob Ajith Cherian K. Gopakumar Thomas Iype Doris George Yohannan K. P. Divya |
| author_sort | Jisu Elsa Jacob |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9b49f892b334e78bd69f883373e698e |
| institution | Kabale University |
| issn | 2090-1852 2090-1860 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neurology Research International |
| spelling | doaj-art-e9b49f892b334e78bd69f883373e698e2025-02-03T01:10:22ZengWileyNeurology Research International2090-18522090-18602018-01-01201810.1155/2018/81928208192820Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?Jisu Elsa Jacob0Ajith Cherian1K. Gopakumar2Thomas Iype3Doris George Yohannan4K. P. Divya5Department of Electronics and Communication Engineering, SCT College of Engineering, Thiruvananthapuram, Kerala, IndiaDepartment of Neurology, SCTIMST, Thiruvananthapuram, Kerala, IndiaDepartment of ECE, TKM College of Engineering, Kollam, Kerala, IndiaDepartment of Neurology, Government Medical College, Thiruvananthapuram, Kerala, IndiaDepartment of Anatomy, Government Medical College, Thiruvananthapuram, Kerala, IndiaDepartment of Neurology, SCTIMST, Thiruvananthapuram, Kerala, IndiaChaotic 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.http://dx.doi.org/10.1155/2018/8192820 |
| spellingShingle | Jisu Elsa Jacob Ajith Cherian K. Gopakumar Thomas Iype Doris George Yohannan K. P. Divya Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy? Neurology Research International |
| title | Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy? |
| title_full | Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy? |
| title_fullStr | Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy? |
| title_full_unstemmed | Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy? |
| title_short | Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy? |
| title_sort | can chaotic analysis of electroencephalogram aid the diagnosis of encephalopathy |
| url | http://dx.doi.org/10.1155/2018/8192820 |
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