The Journal of Cognitive Systems

Öz

Epilepsy is a brain activity disorder that manifests itself with epileptic seizures. Although the reasons of epilepsy are not fully known, the diversity and variability of the individual make it difficult to diagnose epilepsy. For this, the diagnosis of epilepsy with computerized systems is one of the most popular research topics in recent years. Although many techniques and methods have been developed for this purpose, Electroencephalogram (EEG) signals are one of the most preferred and most basic ways to diagnose epileptic seizures or epilepsies because of their practicality and easy application. However, interpretation of EEG signals is not easy due to nonlinear and variable signal characteristics. This has led to the preference of nonlinear methods as well as traditional methods in the study of EEG signals. It can be seen from the literature that non-linear methods give very successful results in previous studies. In this study; EEG signals from healthy and epileptic subjects were represented by recurrence parameters obtained by recurrence plot. The features extracted from the recurrence plot are applied to multi-layered artificial neural networks, k-nearest neighbors, and support vector machines classifiers after feature selection process. Accordingly, the highest classification accuracy was achieved at around 97.05% when the multi-layered artificial neural network was used