DocumentCode :
2513465
Title :
Model Research for Epileptic Prediction Based on Improved Chaos Operator of Lyapunov
Author :
Huang, Xiaona ; Wang, Wei ; Sun, Xiangju ; Chen, Yuli ; Li, Lan ; Deng, Yun ; Shen, Yingxia
Author_Institution :
Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China
fYear :
2009
fDate :
11-13 June 2009
Firstpage :
1
Lastpage :
4
Abstract :
Epilepsy is a brain dysfunction disease, which is caused by brain neuron excessive discharge. The damage induced by seizure is irreversible, so it is necessary to propose a new method to prevent epileptic seizure and protect the brain. Therefore, the best way is to predict preictal state accurately and to give preventive treatment. In this paper, the calculation of the largest Lyapunov exponent based on improved Wolf algorithm was used to analyze characteristics of the nonlinear dynamics and extract feature model among rats´ preictal, ictal and normal state, which was applied to forecast the epilepsy. The results showed that the method can be used to predict the preictal state effectively, and it is important for preventive treatment of seizure.
Keywords :
Lyapunov methods; cellular biophysics; chaos; diseases; electroencephalography; feature extraction; medical signal processing; neurophysiology; patient treatment; EEG prediction; Lyapunov exponent; Wolf algorithm; brain dysfunction disease; chaos; epileptic prediction; feature extraction; nonlinear dynamics; preictal state prediction; preventive treatment; Brain modeling; Chaos; Delay effects; Diseases; Electroencephalography; Epilepsy; Nonlinear dynamical systems; Orbital calculations; Predictive models; Rats;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
Type :
conf
DOI :
10.1109/ICBBE.2009.5163064
Filename :
5163064
Link To Document :
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