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

Author

Zheng, Guozheng ; Yu, Liutao ; Feng, Yuwei ; Han, Zhuyi ; Chen, Lisheng ; Zhang, Shouwen ; Wang, Dahui ; Han, Zhangang

Author_Institution

ShangRao Normal Univ., Shangrao, China

fYear

2012

fDate

23-25 March 2012

Firstpage

29

Lastpage

34

Abstract

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.

Keywords

electroencephalography; medical signal processing; support vector machines; CSP; SVM; brain electrical activity record; common spatial pattern method; epilepsy; epileptic seizure onset; intracranial EEG; preseizure signals; scalp EEG; seizure prediction model; support vector machine; Brain modeling; Electroencephalography; Epilepsy; Feature extraction; Predictive models; Scalp; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Technology (ICIST), 2012 International Conference on

Conference_Location

Hubei

Print_ISBN

978-1-4577-0343-0

Type

conf

DOI

10.1109/ICIST.2012.6221603

Filename

6221603