脑电图混沌分析有助于脑病的诊断吗?

IF 1.7 Q4 NEUROSCIENCES
Neurology Research International Pub Date : 2018-05-29 eCollection Date: 2018-01-01 DOI:10.1155/2018/8192820
Jisu Elsa Jacob, Ajith Cherian, K Gopakumar, Thomas Iype, Doris George Yohannan, K P Divya
{"title":"脑电图混沌分析有助于脑病的诊断吗?","authors":"Jisu Elsa Jacob,&nbsp;Ajith Cherian,&nbsp;K Gopakumar,&nbsp;Thomas Iype,&nbsp;Doris George Yohannan,&nbsp;K P Divya","doi":"10.1155/2018/8192820","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19124,"journal":{"name":"Neurology Research International","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2018-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/8192820","citationCount":"16","resultStr":"{\"title\":\"Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?\",\"authors\":\"Jisu Elsa Jacob,&nbsp;Ajith Cherian,&nbsp;K Gopakumar,&nbsp;Thomas Iype,&nbsp;Doris George Yohannan,&nbsp;K P Divya\",\"doi\":\"10.1155/2018/8192820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":19124,\"journal\":{\"name\":\"Neurology Research International\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2018-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1155/2018/8192820\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurology Research International\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2018/8192820\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurology Research International","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2018/8192820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 16

摘要

混沌分析是生理信号研究中一个相对较新的领域。在癫痫、阿尔茨海默病、睡眠障碍和抑郁症等各种疾病状态下,脑电图的混沌特征已被分析。所有这些疾病都主要累及大脑。我们的研究检查了代谢性脑病的混沌参数,其中脑功能涉及继发于代谢紊乱。我们的分析清楚地显示,与正常脑电图相比,代谢性脑病患者脑电图的混沌参数、相关维数和最大Lyapunov指数显著降低。脑电图的混沌特征在以往的研究中已被证明是脑动力学复杂性的一个指标。脑病的混沌特征值越小,表明脑病的正常脑功能复杂性降低。据作者所知,没有关于代谢性脑病的类似研究报道。这一发现可能有助于了解脑病的神经生物学现象。然后将这些混沌特征用作支持向量机分类器的特征集,以从正常健康受试者中识别脑病病例,从而获得较高的准确性。因此,我们推断混沌测量是脑电图参数对脑病引起的脑功能改变敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?

Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?

Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?

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 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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.50
自引率
0.00%
发文量
10
审稿时长
17 weeks
期刊介绍: Neurology Research International is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies focusing on diseases of the nervous system, as well as normal neurological functioning. The journal will consider basic, translational, and clinical research, including animal models and clinical trials.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信