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EEG Pattern Recognition to Diagnose Epilepsy Using Wavelet and Chaos Transformations

Author(s): Parisa Baghaie-Anaraki | MohammadReza Yazdchi | AliReza Karimian

Journal: Majlesi Journal of Electrical Engineering
ISSN 2008-1413

Volume: 2;
Issue: 1;
Start page: 51;
Date: 2008;
Original page

Keywords: Choas | Neuron System | Epilepsy | Wavelet | EEG.

By the time-frequency transformations like wavelet and chaos theory to find the feature from sub-bands, it is possible to diagnose the epilepsy although there are some noises and signals. To decompose the EEG into sub-bands such as delta, theta, alpha, beta and gamma, wavelet analysis is used. Chaos theory is used to compute standard deviation, correlation dimension and Lyapunov exponent from the sub-bands, then the neuron system and other classifiers, standard deviations and averages are used to increase the diagnosis accuracy of epilepsy for all three groups of normal, ictal, and inter ictal.Results show a fuzzy subtractive clustering in a specific distance including 8 parameters (persistence 96.8% and standard deviation 0.7) and by Ensemble averaging including 6 parameters (persistence 97.5% and standard deviation 0) is better than other methods and proper for clustering epilepsy disease.This statistics is considerable while visual consideration by specialized neurologists isn’t more than 80 percent.
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