Neural network-based computer-aided diagnosis in classification of primary generalized epilepsy by EEG signals

Abstract

Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to classify subgroups of primary generalized epilepsy by using Multilayer Perceptron Neural Networks (MLPNNs). This is the first study classifying primary generalized epilepsy using MLPNNs. MLPNN classified primary generalized epilepsy with the accuracy of 84.4%. This model also classified generalized tonik-klonik, absans, myoclonic and more than one type seizures epilepsy groups correctly with the accuracy of 78.5%, 80%, 50% and 91.6%, respectively. Moreover, new MLPNNs were constructed for determining significant variables affecting the classification accuracy of neural networks. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. These outcomes indicate that this model classified the subgroups of primary generalized epilepsy successfully.