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- Title
Epileptic seizure detection based on the kernel extreme learning machine.
- Authors
Qi Liu; Xiaoguang Zhao; Zengguang Hou; Hongguang Liu; Liu, Qi; Zhao, Xiaoguang; Hou, Zengguang; Liu, Hongguang
- Abstract
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.
- Subjects
DIAGNOSIS of epilepsy; KERNEL functions; NEUROLOGICAL disorders; NEURONS; ELECTRODES
- Publication
Technology & Health Care, 2017, Vol 25, pS399
- ISSN
0928-7329
- Publication type
journal article
- DOI
10.3233/THC-171343