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Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent

文献类型：期刊论文

作者Li, Qiong1; Gao, Jianbo2,3,4; Huang, Qi5; Wu, Yuan5; Xu, Bo3
刊名FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
出版日期2020-09-08
卷号8页码:14
关键词EEG epileptiform discharges power spectral density (PSD) scale-dependent Lyapunov exponent (SDLE) random forest classifier support vector machine (SVM)
ISSN号2296-4185
DOI10.3389/fbioe.2020.01006
通讯作者Gao, Jianbo(jbgao.pmb@gmail.com) ; Wu, Yuan(nwuyuan@stu.gxmu.edu.cn)
英文摘要Epileptiform discharges are of fundamental importance in understanding the physiology of epilepsy. To aid in the clinical diagnosis, classification, prognosis, and treatment of epilepsy, it is important to develop automated computer programs to distinguish epileptiform discharges from normal electroencephalogram (EEG). This is a challenging task as clinically used scalp EEG often contains a lot of noise and motion artifacts. The challenge is even greater if one wishes to develop explainable rather than black-box based approaches. To take on this challenge, we propose to use a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE). We analyzed 640 multi-channel EEG segments, each 4slong. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We found that features from SDLE were very effective in distinguishing epileptiform discharges from normal EEG. Using Random Forest Classifier (RF) and Support Vector Machines (SVM), the proposed approach with different features from SDLE robustly achieves an accuracy exceeding 99% in distinguishing epileptiform discharges from normal control ones. A single parameter, which is the ratio of the spectral energy of EEG signals and the SDLE and quantifies the regularity or predictability of the EEG signals, is introduced to better understand the high accuracy in the classification. It is found that this regularity is considerably greater for epileptiform discharges than for normal controls. Robustly having high accuracy in distinguishing epileptiform discharges from normal controls irrespective of which classification scheme being used, the proposed approach has the potential to be used widely in a clinical setting.
WOS关键词DIRECT DYNAMICAL TEST ; PERMUTATION ENTROPY ; SEIZURE DETECTION ; EEG ; EPILEPSY ; CLASSIFICATION ; NETWORKS ; SYSTEM ; CHAOS
资助项目National Natural Science Foundation of China[71661002] ; National Natural Science Foundation of China[41671532] ; Fundamental Research Funds for the Central Universities ; National Key Research and Development Program of China[2019AAA0103402] ; National Science Foundation
WOS研究方向Biotechnology & Applied Microbiology ; Science & Technology - Other Topics
语种英语
WOS记录号WOS:000574273300001
出版者FRONTIERS MEDIA SA
资助机构National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; National Key Research and Development Program of China ; National Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/42050]  
专题数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者Gao, Jianbo; Wu, Yuan
作者单位1.Guangxi Univ, Sch Comp Elect & Informat, Nanning, Peoples R China
2.Beijing Normal Univ, Fac Geog Sci, Ctr Geodata & Anal, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
4.Guangxi Univ, Int Coll, Nanning, Peoples R China
5.Guangxi Med Univ, Affiliated Hosp 1, Nanning, Peoples R China
推荐引用方式
GB/T 7714		 Li, Qiong,Gao, Jianbo,Huang, Qi,et al. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent[J]. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY,2020,8:14.
APA Li, Qiong,Gao, Jianbo,Huang, Qi,Wu, Yuan,&Xu, Bo.(2020).Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent.FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY,8,14.
MLA Li, Qiong,et al."Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent".FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 8(2020):14.

入库方式： OAI收割

来源：自动化研究所

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