CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Epileptic Seizure Detection based on the Kernel Extreme Learning Machine
Liu Q(刘祺)1; Zhao XG(赵晓光)1; Hou ZG(侯增广)1; Liu HG(刘洪广)2
Source PublicationTechnology and health care
2017-05-31
IssuepreprintPages:1-11
AbstractThis 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.
KeywordEpileptic Eeg Multiple Features Elm Kernel Function Cholesky Decomposition
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14830
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.中国科学院自动化研究所
2.中国人民公安大学
Recommended Citation
GB/T 7714
Liu Q,Zhao XG,Hou ZG,et al. Epileptic Seizure Detection based on the Kernel Extreme Learning Machine[J]. Technology and health care,2017(preprint):1-11.
APA Liu Q,Zhao XG,Hou ZG,&Liu HG.(2017).Epileptic Seizure Detection based on the Kernel Extreme Learning Machine.Technology and health care(preprint),1-11.
MLA Liu Q,et al."Epileptic Seizure Detection based on the Kernel Extreme Learning Machine".Technology and health care .preprint(2017):1-11.
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