Epileptic Seizure Classification Using Neural Networks with 14 Features

  • Rui P. Costa
  • Pedro Oliveira
  • Guilherme Rodrigues
  • Bruno Leitão
  • António Dourado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5178)


Epilepsy is one of the most frequent neurological disorders. The main method used in epilepsy diagnosis is electroencephalogram (EEG) signal analysis. However this method requires a time-consuming analysis when made manually by an expert due to the length of EEG recordings. This paper proposes an automatic classification system for epilepsy based on neural networks and EEG signals. The neural networks use 14 features (extracted from EEG) in order to classify the brain state into one of four possible epileptic behaviors: inter-ictal, pre-ictal, ictal and pos-ictal. Experiments were made in a (i) single patient (ii) different patients and (ii) multiple patients, using two datasets. The classification accuracies of 6 types of neural networks architectures are compared. We concluded that with the 14 features and using the data of a single patient results in a classification accuracy of 99%, while using a network trained for multiple patients an accuracy of 98% is achieved.


Neural Networks Epilepsy Seizure Prediction Data Mining Classification EEG processing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rui P. Costa
    • 1
  • Pedro Oliveira
    • 1
  • Guilherme Rodrigues
    • 1
  • Bruno Leitão
    • 1
  • António Dourado
    • 1
  1. 1.Center for Informatics and SystemsUniversity of CoimbraCoimbraPortugal

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