Yalcin, NesibeKarakuzu, CihanTezel, Gulay2020-03-262020-03-262013978-4-9902880-7-5https://hdl.handle.net/20.500.12395/2947118th International Symposium on Artificial Life and Robotics (AROB) -- JAN 30-FEB 01, 2013 -- Daejeon, SOUTH KOREAElectroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in classification of epileptic attacks and epilepsy diagnosis. In this paper, epilepsy diagnosis by evaluation of EEG records is presented. Artificial Neural Networks (ANN) is used as a classification technique. Particle Swarm Optimization (PSO) method, which doesn't require gradient calculation, derivative information and any solution of differential equations is preferred for ANN training. This training method is compared with back propagation algorithm, which is one of the traditional methods, and the results are interpreted. In case of using the PSO algorithm, the training and test classification accuracies are %99.67 and %100, respectively. PSO based neural network model (PSONN) has a better classification accuracy than back propagation neural network model (BPNN) for epilepsy diagnosis.eninfo:eu-repo/semantics/closedAccessArtificial neural networksback propagation algorithmEEGepilepsy diagnosisparticle swarm optimizationConference Object460463WOS:000387182200112N/A