The Application of Neural Networks in Classification of Epilepsy Using EEG Signals

  • Cenk Sahin
  • Seyfettin Noyan Ogulata
  • Kezban Aslan
  • Hacer Bozdemir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)


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 evaluate epileptic patients and classify epilepsy groups by using Multi-Layer Perceptron Neural Networks (MLPNNs). 418 patients with epilepsy diagnoses according to International League against Epilepsy (ILAE, 1981) were included in this study. The correct classification of this data was performed by two expert neurologists before they were executed by MLPNNs. The MLPNNs were trained by the parameters obtained from the EEG signals and clinic properties of the patients. We classified the epilepsy into two groups such as partial and primary generalized epilepsy and we achieved an 89.2% correct prediction rate by using MLPNN model. The parameters of the loss of consciousness in the course of seizure, the duration and ritmicity of abnormal activities found in EEG constituted the most significant variables in the classification of epilepsy by using MLPNN. These results indicate that the classification performance of MLPNN model for epilepsy groups is satisfactory and we think that this model may be used in clinical studies as a decision support tool to confirm the classification of epilepsy groups after they are developed.


Epilepsy EEG Multilayer Perceptron Neural Network (MLPNN) Levenberg-Marquardt 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Cenk Sahin
    • 1
  • Seyfettin Noyan Ogulata
    • 1
  • Kezban Aslan
    • 2
  • Hacer Bozdemir
    • 2
  1. 1.Department of Industrial Engineering, Faculty of Engineering and Architecture, Cukurova University, 01330, AdanaTurkey
  2. 2.Department of Neurology, Faculty of Medicine, Cukurova University, 01330, AdanaTurkey

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