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)

Abstract

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.

Keywords

Neural Networks Epilepsy Seizure Prediction Data Mining Classification EEG processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Srinivasan, V., Eswaran, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions On Information Technology In Biomedicine 11(3), 288–295 (2007)CrossRefGoogle Scholar
  2. 2.
    Ghosh-Dastidar, S., Adeli, H., Dadmehr, N.: Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering 54(7), 1545–1551 (2007)CrossRefGoogle Scholar
  3. 3.
    Subasi, A.: Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Comput. Biol. Med. 37(2), 227–244 (2007)CrossRefGoogle Scholar
  4. 4.
    Mormann, F., Andrzejak, R.G., Elger, C.E., Lehnertz, K.: Seizure prediction: the long and winding road. Brain 130, 314–333 (2007)CrossRefGoogle Scholar
  5. 5.
    Freiburger Zentrum fur Datenanalyse und Mollbildung: The freiburg seizure prediction project abase, https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-dat
  6. 6.
    Leitão, B., Dourado, A., Vieira, M., Sales, F.: Computational system for the prediction of epileptic seizures through multi-sensorial information analysis. Technical report, Department of Informatics Engineering, University of Coimbra (September 2007)Google Scholar
  7. 7.
    Winterhalder, M., Schelter, B., Maiwald, T., Brandt, A., Schad, A., Schulze-Bonhage, A., Timmer, J.: Spatio-temporal patient-individual assessment of synchronization changes for epileptic seizure prediction. Clinical Neurophysiology 117, 2399–2413 (2006)CrossRefGoogle Scholar
  8. 8.
    Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., Pennell, P., Epstein, C., Bakay, R., Dichter, M., Vachtsevanos, G.: Epileptic seizures may begin hours in advance of clinical onset: A report of five patients. Neuron. 30(1), 51–64 (2001)CrossRefGoogle Scholar
  9. 9.
    Merkwirth, C., Parlitz, U., Wedekind, I., Lauterborn, W.: Tstool user manual, version 1.11 (2001), http://www.dpi.physik.uni-goettingen.de/tstool/HTML/index.html
  10. 10.
    Demuth, H., Beale, M., Hagan, M.: Neural network toolbox 6 user’s guide. The MathWorks (2008)Google Scholar
  11. 11.
    Chen, S., Cowan, C., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)CrossRefGoogle Scholar
  12. 12.
    Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Company (1996)Google Scholar
  13. 13.
    Elman, J.L.: Finding structure in time. Cognitive Science 14, 179–211 (1990)CrossRefGoogle Scholar
  14. 14.
    Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., Lang, K.: Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing 37(3), 328–339 (1989)CrossRefGoogle Scholar

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

Personalised recommendations