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

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Advances in Brain, Vision, and Artificial Intelligence (BVAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4729))

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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 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.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering and Architecture, Cukurova University, 01330, Adana, Turkey

    Cenk Sahin & Seyfettin Noyan Ogulata

  2. Department of Neurology, Faculty of Medicine, Cukurova University, 01330, Adana, Turkey

    Kezban Aslan & Hacer Bozdemir

Authors
  1. Cenk Sahin
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  2. Seyfettin Noyan Ogulata
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  3. Kezban Aslan
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  4. Hacer Bozdemir
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Francesco Mele Giuliana Ramella Silvia Santillo Francesco Ventriglia

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© 2007 Springer-Verlag Berlin Heidelberg

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Sahin, C., Ogulata, S.N., Aslan, K., Bozdemir, H. (2007). The Application of Neural Networks in Classification of Epilepsy Using EEG Signals. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_48

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  • DOI: https://doi.org/10.1007/978-3-540-75555-5_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75554-8

  • Online ISBN: 978-3-540-75555-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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