Epileptic seizure detection based on the kernel extreme learning machine
文献类型：期刊论文
| 作者 | Liu, Qi1 ; Zhao, Xiaoguang1; Hou, Zengguang1; Liu, Hongguang2
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| 刊名 | TECHNOLOGY AND HEALTH CARE
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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