Annals of Biomedical Engineering

, Volume 34, Issue 3, pp 515–529 | Cite as

On Prediction of Epileptic Seizures by Means of Genetic Programming Artificial Features

Article
First Online:
Received:
Accepted:

A general-purpose, systematic algorithm is presented, consisting of a genetic programming module and a k-nearest neighbor classifier to automatically create artificial features—computer-crafted features possibly without a known physical meaning—directly from the reconstructed state-space trajectory of intracranial EEG signals that reveal predictive patterns of epileptic seizures. The algorithm was evaluated with IEEG data from seven patients, with prediction defined over a horizon of 1–5 min before unequivocal electrographic onset. A total of 59 baseline epochs (nonseizures) and 55 preictal epochs (preseizures) were used for validation purposes. Among the results, it is shown that 12 seizures out of 55 were missed while four baseline epochs were misclassified, yielding 79% sensitivity and 93% specificity.

Keywords

Epilepsy Seizure prediction Artificial feature Feature extraction Genetic programming State-space reconstruction 
This is a preview of subscription content, log in to check access.

REFERENCES

  1. 1.
    D'Alessandro, M. The Utility of Intracranial EEG Feature and Channel Synergy for Evaluating the Spatial and Temporal Behavior of Seizure Precursors, PhD Dissertation, Georgia Institute of Technology, Atlanta, 2001.Google Scholar
  2. 2.
    D'Alessandro, M., R. Esteller, G. Vachtsevanos, A. Hinson, J. Echauz, and B. Litt. Epileptic seizures prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients. IEEE Trans. Biomed. Eng. 50(5):603–615, 2003.PubMedCrossRefGoogle Scholar
  3. 3.
    Duda, R., P. Hart, and D. Stork. Pattern Classification, 2nd ed. New York: Wiley, 2001.Google Scholar
  4. 4.
    Echauz, J. Wavelet Neural Networks for EEG Modeling and Classification, PhD Dissertation, Georgia Institute of Technology, August 1995.Google Scholar
  5. 5.
    Elger, C. E., and K. Lehnertz. Seizure prediction by non-linear time series analysis of brain electrical activity. Eur. J. Neurosci. 10:786–789, 1998.PubMedCrossRefGoogle Scholar
  6. 6.
    Firpi, H. Genetically Found, Neurally Computed Artificial Features with Applications to Epileptic Seizure Detection and Prediction, Master's Thesis, University of Puerto Rico-Mayagüez, 2001.Google Scholar
  7. 7.
    Firpi, H. On Prediction and Detection of Epileptic Seizures by Means of Genetic Programming Artificial Features, PhD Dissertation, Michigan State University, 2005.Google Scholar
  8. 8.
    Gilmore, R., and M. Lefranc. The Topology of Chaos: Alice in the Stretch and Squeezeland. New York: Wiley, 2002.Google Scholar
  9. 9.
    Haupt, R. L., and S. E. Haupt. Practical Genetic Algorithms. New York: Wiley, 1998.Google Scholar
  10. 10.
    Iasemidis, L. Epileptic seizure prediction and control. IEEE Trans. Biomed. Eng. 50(5):549–559, 2003.PubMedCrossRefGoogle Scholar
  11. 11.
    Iasemidis, L., D.-S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. M. Pardalos, J. C. Principe, P. R. Carney, A. P. Prasad, B. Veeramani, and K. Tsakalis. Adaptive epileptic seizure prediction system. IEEE Trans. Biomed. Eng. 50(5):616–627, 2003.PubMedCrossRefGoogle Scholar
  12. 12.
    Jansen, B. H. Quantitative analysis of electroencephalograms: Is there chaos in the future? Int. J. Biomed. Comput. 27:95–123, 1991.PubMedCrossRefGoogle Scholar
  13. 13.
    Jia, W., N. Kong, F. Li, X. Gao, S. Gao, G. Zhang, Y. Wang, and F. Yang. An epileptic seizure prediction algorithm based on second-order complexity measure. Physiol. Meas. 26:609–625, 2005.PubMedCrossRefGoogle Scholar
  14. 14.
    Koza, J. R. Genetic Programming: On Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.Google Scholar
  15. 15.
    Lee, S. A., D. D. Spencer, and S. S. Spencer. Intracranial EEG seizure-onset patterns in neocortical epilepsy. Epilepsia 41(3):297–307, 2000.PubMedCrossRefGoogle Scholar
  16. 16.
    Lehnertz, K., and C. E. Elger. Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr Clin Neurophysiol 95:108–117.Google Scholar
  17. 17.
    Lehnertz, K., and B. Litt. The First International Collaborative Workshop on Seizure Prediction: Summary and data description. Clin. Neurophysiol. 116(3):493–505, 2005.PubMedCrossRefGoogle Scholar
  18. 18.
    Litt, B., R. Esteller, J. Echauz, M. D'Alessandro, R. Shor, T. Henry, P. Pennell, C. Epstein, R. Bakay, M. Dichter, and G. Vachtsevanos. Epileptic seizures may begin hours in advance of clinical seizures: A report of five patients. Neuron 29(4):51–64, 2001.CrossRefGoogle Scholar
  19. 19.
    Martinerie, J., C. Adam, M. le van Quyen, M. Baulac, S. Clemenceau, B. Renault, and F. J. Valera.. Epileptic seizures can be anticipated by non-linear analysis. Nat. Med. 4:1176–1176, 1998.CrossRefGoogle Scholar
  20. 20.
    Niederhoefer, C., F. Gollas, A. Chernihovskyi, K. Lehnertz, and R. Tetzlaff. Detection of seizure precursors in the EEG with cellular neural networks. Epilepsia 45(7):245, 2004 (abstract).Google Scholar
  21. 21.
    Orfanidis, S. J. Optimum Signal Processing: An Introduction, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.Google Scholar
  22. 22.
    Petrosian, A., D. Prokhorov, R. Homan, R. Dashieff, and D. Wunch. Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG. Neurocomputing 30:201–218, 2000.CrossRefGoogle Scholar
  23. 23.
    Pritchard, W. S., and D. W. Duke. Measuring chaos in the brain: A tutorial review of nonlinear dynamical EEG analysis. Brain Cogn 27(3):353–397, 1995.PubMedCrossRefGoogle Scholar
  24. 24.
    Sowa, R., F. Mormann, A. Chernihovskyi, C. Niederhoefer, R. Tetzlaff, C. Elger, and K. Lehnertz. Seizure prediction: Measuring EEG phase synchronization with cellular neural networks. Epilepsia 45(7):244, 2004 (abstract).Google Scholar
  25. 25.
    Sprott, J. C. Chaos and Time-Series Analysis. New York: Oxford University Press, 2003.Google Scholar
  26. 26.
    Takens, F. Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence, Warwick 1980 Lecture Notes in Mathematics 898. Berlin: Springer-Verlag, 1981, pp. 336–381.Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA
  3. 3.BioQuantix Corp.AtlantaUSA
  4. 4.Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations