Classifying epilepsy diseases using artificial neural networks and genetic algorithm.
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.
Koçer
Sabri
S
Department of Electronics, Gazi University, Teknikokullar, Ankara, Turkey. skocer@gazi.edu.tr
Canal
M Rahmi
MR
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004569:Electroencephalography; D004827:Epilepsy / Q000145:classification* / Q000175:diagnosis / Q000235:genetics; D005583:Fourier Analysis; D020022:Genetic Predisposition to Disease; D006801:Humans; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity