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Seizure prediction model based on method of common spatial patterns and support vector machine

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机构： [1]ShangRao Normal University, Jiangxi, China. [2]Department of Systems Science, Beijing Normal University, Beijing100875, China. [3]Beijing Institute of Functional Neurosurgery, Xuanwu Hospital , Captial Medical University, Beijing, China.

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Records of brain electrical activity from intracranial and scalp EEG of seven patients with different types of epilepsy are analyzed to predict the epileptic seizure onset. A method based on the CSP and SVM is introduced. This is an efficient method to predict epileptic seizures: from 52 pre-seizure signals, the seizure onsets in 23 of those are predicted. Through this method, we propose a seizure prediction model which gets an accuracy rate represented by predictions / seizures of 5/20-5/5 and a pseudo-prediction rate of 1.6-10.9 per hour. © 2012 IEEE.

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第一作者机构： [1]ShangRao Normal University, Jiangxi, China.

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通讯机构： [2]Department of Systems Science, Beijing Normal University, Beijing100875, China.

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