EEG Pattern Recognition to Diagnose Epilepsy Using Wavelet and Chaos Transformations

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 ۸ parameters (persistence ۹۶.۸% and standard deviation ۰.۷) and by Ensemble averaging including ۶ parameters (persistence ۹۷.۵% and standard deviation ۰) 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 ۸۰ percent.