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

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...

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Bibliographic Details
Published in2012 IEEE International Conference on Information Science and Technology pp. 29 - 34
Main Authors Guozheng Zheng, Liutao Yu, Yuwei Feng, Zhuyi Han, Lisheng Chen, Shouwen Zhang, Dahui Wang, Zhangang Han
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2012
Subjects
Brain modeling
Electroencephalography
Epilepsy
Feature extraction
Predictive models
Scalp
Support vector machines
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ISBN9781457703430
1457703432
ISSN2164-4357
DOI10.1109/ICIST.2012.6221603

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Summary: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.
ISBN:9781457703430
1457703432
ISSN:2164-4357
DOI:10.1109/ICIST.2012.6221603