Epileptic Seizure Detection based on the Kernel Extreme Learning Machine
文献类型：期刊论文
| 作者 | Liu Q(刘祺)1 ; Zhao XG(赵晓光)1; Hou ZG(侯增广)1; Liu HG(刘洪广)2
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| 刊名 | Technology and health care
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| 出版日期 | 2017-05-31 |
| 期号 | preprint页码:1-11 |
| 关键词 | Epileptic Eeg Multiple Features Elm Kernel Function Cholesky Decomposition |
| 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14830]  |
| 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.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. |
入库方式： OAI收割
来源：自动化研究所
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