Advertisement

Automatic Seizure Detection Based on Support Vector Machines with Genetic Algorithms

  • Jinfeng Fan
  • Chenxi Shao
  • Yang Ouyang
  • Jian Wang
  • Shaobin Li
  • Zicai Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

The electroencephalogram (EEG) machine is the most influential tool in the diagnosis of epilepsy, which is one of the most common neurological disorders. In this paper, a new seizure detection approach, which combined the genetic algorithm (GA) and the support vector machine (SVM), is proposed to improve visual inspection of EEG recordings. Genetic operations are utilized to optimize the performance of SVM classifier, which includes three aspects: feature subset selection, channel subset selection and parameter optimization of SVM. These optimization operations are performed simultaneously during the training process. The epileptic EEG data acquired from hospital are divided into two parts of training set and testing set. The results from the test on EEG data show that the method may more effectively recognize the spike and sharp transients from the EEG recording of epileptic patients than those without using optimal operations.

Keywords

Support Vector Machine Recognition Accuracy Feature Subset Support Vector Machine Model Channel Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Acir, N., Guzelis, C.: Automatic Spike Detection in EEG by a Two-Stage Procedure based on Support vector machines. Computers in Biology and Medicine 34(7), 561–575 (2004)CrossRefGoogle Scholar
  2. 2.
    Weng, W., Khorasani, K.: An Adaptive Structure Neural Networks with Application to EEG Automatic Seizure Detection. Neural Network 9(7), 1223–1240 (1996)CrossRefGoogle Scholar
  3. 3.
    Subasi, A.: Epileptic seizure detection using dynamic wavelet network. Expert System with Applications 29(2), 343–355 (2005)CrossRefGoogle Scholar
  4. 4.
    Gautama, T., Mandic, D.P., Van Hulle, M.M.: Indications of Nonlinear Structures in Brain Electrical Activity. Phys. Rev. E 67, 46204 (2003)CrossRefGoogle Scholar
  5. 5.
    Bandt, C., Pompe, B.: Permutation Entropy — a Natural Complexity Measure for time series. Physical Review Letters 88(17), 174102 (2002)CrossRefGoogle Scholar
  6. 6.
    Natarajan, K., Acharya, U.R., Alias, F., et al.: Nonlinear Analysis of EEG Signals at Different mental States. Biomedical Engineering Online 3(7) (2004)Google Scholar
  7. 7.
    Shao, C.X., Fan, J.F., Feng, H.L., et al.: Fuzzy Analysis of Epileptic EEG Based on Qualitative Simulation. In: Asia Simulation Conference/6th International Conference on System Simulation and Scientific Computing, Beijing, China, vol. 2, pp. 1348–1352 (2005)Google Scholar
  8. 8.
    Cho, S.Y., Kim, B., Park, E., et al.: Automatic Recognition of Alzheimer’s Disease Using Genetic Algorithms and Neural Network. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J., Zomaya, A.Y. (eds.) ICCS 2003. LNCS, vol. 2658, pp. 695–702. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  10. 10.
    Saxena, A., Saad, A.: Evolving an Artificial Neural Network Classifier for Condition Monitoring of Rotating Mechanical systems. Applied Soft Computing (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jinfeng Fan
    • 1
  • Chenxi Shao
    • 1
  • Yang Ouyang
    • 1
  • Jian Wang
    • 1
  • Shaobin Li
    • 1
  • Zicai Wang
    • 2
  1. 1.Department of Computer Science and TechnologyUniversity of Science, and Technology of ChinaHefei, AnhuiP.R. China
  2. 2.Control & Simulation CenterHarbin Institute of TechnologyHarbin, HeilongjiangP.R. China

Personalised recommendations