Extraction and Recognition of Epilepsy Signal Based on Wavelet Packet and Support Vector Machine

被引：0
|
作者：
Yan, Xiufang [1 ]
Jiang, Tao [1 ]
机构：
[1] S China Univ Technol, Sch Biosci & Bioengn, Guangzhou 510006, Guangdong, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND INDUSTRIAL AUTOMATION (ICITIA 2015) | 2015年
关键词：
epilepsy signal detection; electrocorticogram; wavelet packet analysis; support vector machine(1);
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper, a novel pattern recognition model is presented for epilepsy detection, which is the combination of wavelet packet transform (WPT) and support vector machine (SVM). The WPT-based methodology decompose and reorganize each list bands in frequency bands of the electrocorticogram (ECoG), then energy proportion as the formation of feature vectors. The effectiveness of the feature vectors in discriminating between normal brain state and epilepsy seizures is evaluated using SVM. The results show that the epilepsy signals correct detection rate of the model is 93.75%, the missed detection rate was 2.1%, and the false detection rate was 10.4%. The present study can greatly improve the accuracy of epilepsy detection, shortening the detection time and saving manpower.
引用
收藏
页码：310 / 317
页数：8
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