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
2017-05-31
发表期刊Technology and health care
期号preprint页码:1-11
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.
关键词Epileptic Eeg Multiple Features Elm Kernel Function Cholesky Decomposition
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14830
专题复杂系统管理与控制国家重点实验室_先进机器人
作者单位1.中国科学院自动化研究所
2.中国人民公安大学
推荐引用方式
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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