Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm

Authors:
Sabri Koçer;M. Rahmi Canal
Affiliations:
Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Teknikokullar, Ankara, Turkey;Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Teknikokullar, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2011

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Abstract

In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.