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	Epileptic seizure detection based on the kernel extreme learning machine
	Liu, Qi1; Zhao, Xiaoguang1; Hou, Zengguang1; Liu, Hongguang2
刊名	TECHNOLOGY AND HEALTH CARE
	2017
卷号	25页码:S399-S409
关键词	Epileptic Eeg Multiple Features Elm Kernel Function Cholesky Decomposition
DOI	10.3233/THC-171343
文献子类	Article
英文摘要	This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis. After simple preprocessing, temporal-and wavelet-based features are extracted from epileptic EEG signals. A combined kernel-function-based ELM approach is then proposed for feature classification. To further reduce the computation, Cholesky decomposition is introduced during the process of calculating the output weights. The experimental results show that the proposed method can achieve satisfactory accuracy with less computation time.
WOS关键词	SUPPORT VECTOR MACHINE ; FEATURE-EXTRACTION ; EEG SIGNALS ; ENTROPY ; CLASSIFICATION ; COEFFICIENTS
WOS研究方向	Health Care Sciences & Services ; Engineering
语种	英语
WOS记录号	WOS:000406157200044
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/20703]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China 2.Chinese Peoples Publ Secur Univ, Inst Crime, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Qi,Zhao, Xiaoguang,Hou, Zengguang,et al. Epileptic seizure detection based on the kernel extreme learning machine[J]. TECHNOLOGY AND HEALTH CARE,2017,25:S399-S409.
APA	Liu, Qi,Zhao, Xiaoguang,Hou, Zengguang,&Liu, Hongguang.(2017).Epileptic seizure detection based on the kernel extreme learning machine.TECHNOLOGY AND HEALTH CARE,25,S399-S409.
MLA	Liu, Qi,et al."Epileptic seizure detection based on the kernel extreme learning machine".TECHNOLOGY AND HEALTH CARE 25(2017):S399-S409.

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