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

doi:10.3389/fbioe.2020.01006

	Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent
	Li, Qiong 1; Gao, Jianbo 2,3,4; Huang, Qi 5; Wu, Yuan 5; Xu, Bo3
发表期刊	FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
ISSN	2296-4185
	2020-09-08
卷号	8 页码:14
通讯作者	Gao, Jianbo(jbgao.pmb@gmail.com) ; Wu, Yuan(nwuyuan@stu.gxmu.edu.cn)
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.
关键词	EEG epileptiform discharges power spectral density (PSD) scale-dependent Lyapunov exponent (SDLE) random forest classifier support vector machine (SVM)
DOI	10.3389/fbioe.2020.01006
关键词[WOS]	DIRECT DYNAMICAL TEST ; PERMUTATION ENTROPY ; SEIZURE DETECTION ; EEG ; EPILEPSY ; CLASSIFICATION ; NETWORKS ; SYSTEM ; CHAOS
收录类别	SCI
语种	英语
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
项目资助者	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; National Key Research and Development Program of China ; National Science Foundation
WOS研究方向	Biotechnology & Applied Microbiology ; Science & Technology - Other Topics
WOS类目	Biotechnology & Applied Microbiology ; Multidisciplinary Sciences
WOS记录号	WOS:000574273300001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	脑机接口
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42051
专题	复杂系统认知与决策实验室_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
通讯作者单位	中国科学院自动化研究所
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.