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 | |
发表期刊 | Technology and health care |
2017-05-31 | |
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 |
第一作者单位 | 中国科学院自动化研究所 |
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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICBEB_Epileptic Seiz(631KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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