| Extracting Epileptic Feature Spikes Using Independent Component Analysis |
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| 作者姓名： | YAN Hong-mei XIA Yang LIU Yan-su LAI Yong-xiu YAO De-zhong ZHOU Dong |
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| 作者单位： | [1]School of Life Science and Technology, University of Electronic Soience and Technology of China Chengdu 610054 China [2]Department of Neurology Medical Center of Huaxi. Sichuan University Chengdu 610064 China, University of Electronic Soience and Technology of China Chengdu 610054 China |
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| 基金项目： | Supported by 973 Project （No. 2003CB71606） and National Natural Science Foundation of China （No.30400105, 90208003） |
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| 摘 要： | In recent years, blind source separation （BSS） by independent component analysis （ICA） has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis （fixed-point IC,4 and natural gradient-flexible ICA） are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients＇ electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic.
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| 关 键 词： | 癫痫 脑电图 独立成分分析 ICA |
| 收稿时间： | 2005-03-31 |
Extracting Epileptic Feature Spikes Using Independent Component Analysis |
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| YAN Hong-mei XIA Yang LIU Yan-su LAI Yong-xiu YAO De-zhong ZHOU Dong.Extracting Epileptic Feature Spikes Using Independent Component Analysis[J].Journal of Electronic Science Technology of China,2005,3(4):369-371. |
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| Authors: | YAN Hong-mei XIA Yang LIU Yan-su LAI Yong-xiu YAO De-zhong ZHOU Dong |
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| Abstract: | In recent years, blind source separation (BSS) by independent component analysis (ICA) has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis (fixed-point ICA and natural gradient-flexible ICA) are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients' electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic. |
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| Keywords: | independent component analysis epilepsy feature spikes electro- encephalogram (EEG) |
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