EEG Features as Biomarkers for Discrimination of Preictal States

  • Alkiviadis Tsimpiris
  • Dimitris KugiumtzisEmail author
Part of the Springer Optimization and Its Applications book series (SOIA, volume 65)


The aim of this study is the selection of the most relevant features of electroencephalograms (EEG) for classification and clustering of preictal states. First, a sum of 312 time series features were computed on consecutive segments of preictal EEG (simple statistical measures, linear and nonlinear measures), where some of them regard different method specific parameters. The efficiency of three methods for feature selection was assessed, i.e., the Forward Sequential Selection (FSS), Support Vector Machines with Recursive Feature Elimination (SVM-RFE) and a MI filter. The classification was applied first to 1000 realizations of simulated data from the Mackey–Glass system at different high dimensional chaotic regimes, and next to 12 scalp early and late preictal EEG recordings of different epileptic patients (about 3 h and half an hour before the seizure onset, respectively). The optimal feature subsets selected by the three feature selection strategies for the same classification problems were found very often to have common features. Based on these feature subsets, classification with k-means partitioning as well as SVM was assessed on test sets of EEG from the same recordings. Feature subsets for each channel and episode or only episode did not classify on the test set as well as a global feature subset of a sufficiently large number of the most frequent features over all channels and episodes. We concluded that a global feature subset of 16 most frequent features can play the role of a biomarker and distinguish early and late preictal states.


Feature Selection Feature Subset Detrended Fluctuation Analysis Feature Selection Algorithm Recursive Feature Elimination 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We thank Pål G. Larsson from the National Center of Epilepsy of Norway for providing the EEG data.


  1. 1.
    Berry, M.J., Linoff, G.: Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons, Inc., New York, NY (1997)Google Scholar
  2. 2.
    Brown, B.: A new perspective for information theoretic feature selection. In: 12th International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 5. Journal of Machine Learning Research (2009)Google Scholar
  3. 3.
    Brun, M., Sima, C., Hua, J., Lowey, J., Carrol, B., Suh, E., Dougherty, R.E.: Model-based evaluation of clustering validation measures. Pattern Recognition 40, 807 – 824 (2007)zbMATHCrossRefGoogle Scholar
  4. 4.
    Bruzzo, A.A., Gesierich, B., Santi, M., Tassinari, C.A., Birbaumer, N., Rubboli, G.: Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. a preliminary study. Neurological Sciences 29(1), 3 – 9 (2008)Google Scholar
  5. 5.
    de Carvalho, F.A.T., de Souza, R.M.C.R., Chavent, M., Lechevallier, Y.: Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters 27, 167 – 179 (2006)CrossRefGoogle Scholar
  6. 6.
    Claassen, J.: How I treat patients with EEG patterns on the ictalinterictal continuum in the neuro ICU. Neurocritical Care 11(3), 437 – 444 (2009)CrossRefGoogle Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20(3), 273 – 297 (1995)zbMATHGoogle Scholar
  8. 8.
    D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., Litt, B.: Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Transactions on Biomedical Engineering 50(5), 603 – 615 (2007)CrossRefGoogle Scholar
  9. 9.
    Direito, B., Dourado, A., Sales, F., Vieira, M.: An application for electroencephalogram mining for epileptic seizure prediction. Lecture Notes in Computer Science 5077, 87 – 101 (2008)CrossRefGoogle Scholar
  10. 10.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, second edn. Wiley-Interscience (2001)Google Scholar
  11. 11.
    Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Physica D 9, 189 – 208 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Greene, B.R., Faul, S., Marnane, W.P., Lightbody, G., Korotchikova, I., Boylan, G.B.: A comparison of quantitative EEG features for neonatal seizure detection. Clinical Neurophysiology 119(6), 1248 – 1261 (2008)CrossRefGoogle Scholar
  13. 13.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)zbMATHCrossRefGoogle Scholar
  14. 14.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Springer-Verlag, New York, NY (2001)zbMATHGoogle Scholar
  15. 15.
    Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D 31, 277 – 283 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Hu, J., Gao, J., Principe, J.: Analysis of biomedical signals by the Lempel-Ziv complexity: the effect of finite data size. IEEE Transactions on Biomedical Engineering 53(12), 2606 – 2609 (2006)CrossRefGoogle Scholar
  17. 17.
    Iasemidis, L., Pardalos, P., Shiau, D.S., Chaovalitwongse, W., Narayanan, K., Kumar, S., Carney, P., Sackellares, J.: Prediction of human epileptic seizures based on optimization and phase changes of brain electrical activity. Journal of Optimization Methods and Software 18(1), 81 – 104 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Jain, A., Zongker, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 153 – 158 (1997)CrossRefGoogle Scholar
  19. 19.
    Jain, K.K.: The Handbook of Biomarkers, first edn. Springer, NY,Dordrecht Heidelberg London (2010)CrossRefGoogle Scholar
  20. 20.
    Jouny, C.C., Franaszczuk, P.J., Bergey, G.K.: Characterization of epileptic seizure dynamics using Gabor atom density. Clinical Neurophysiology 114(3), 426–437 (2003)CrossRefGoogle Scholar
  21. 21.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)zbMATHGoogle Scholar
  22. 22.
    Kugiumtzis, D.: State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length. Physica D 95, 13 – 28 (1996)zbMATHCrossRefGoogle Scholar
  23. 23.
    Kugiumtzis, D., Papana, A., Tsimpiris, A., Vlachos, I., Larsson, P.G.: Time series feature evaluation in discriminating preictal EEG states. Lecture Notes in Computer Science 4345, 298–310 (2006)CrossRefGoogle Scholar
  24. 24.
    Kugiumtzis, D., Vlachos, I., Papana, A., Larsson, P.G.: Assessment of measures of scalar time series analysis in discriminating preictal states. International Journal of Bioelectromagnetism 9(3), 134–145 (2007)Google Scholar
  25. 25.
    Liao, T.W.: Clustering of time series data—a survey. Pattern Recognition 38(11), 1857–1874 (2005)zbMATHCrossRefGoogle Scholar
  26. 26.
    Liu, H., Liu, L., Zhang, H.: Feature selection using mutual information: An experimental study. In: T.B. Ho, Z.H. Zhou (eds.) PRICAI 2008: Trends in Artificial Intelligence, Lecture Notes in Computer Science, vol. 5351, pp. 235–246. Springer Berlin / Heidelberg (2008)Google Scholar
  27. 27.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman and Hall, CRC Press (2008)Google Scholar
  28. 28.
    Mackey, M., Glass, L.: Oscillation and chaos in physiological control systems. Science 197, 287 (1977)CrossRefGoogle Scholar
  29. 29.
    Marwan, N., Romano, C.M., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Physics Reports 438(5-6), 237–329 (2007)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Meisler, M., E., J., O’Brien, E., Sharkey, M.: Sodium channel gene family: epilepsy mutations, gene interactions and modifier effects. The Journal of Physiology 588(11), 1841 – 1848 (2010)Google Scholar
  31. 31.
    Oyegbile, O., Bhattacharya, A., Seidenberg, M., Hermann, P.: Quantitative MRI biomarkers of cognitive morbidity in temporal lobe epilepsy. Epilepsia 47(1), 143 – 152 (2006)CrossRefGoogle Scholar
  32. 32.
    Raymer, M., Punch, W., Goodman E.D.and Kuhn, L., Jain, A.: Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation 4, 164 – 171 (2000)Google Scholar
  33. 33.
    Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507 – 2517 (2007)CrossRefGoogle Scholar
  34. 34.
    Schelter, B., Winterhalder, M., Feldwisch, H., Drentrup, G., Wohlmuth, J., Nawrath, J., Brandt, A., Schulze-Bonhage, A., Timmer, J.: Seizure prediction: The impact of long prediction horizons. Epilepsy Research 73, 213 – 217 (2007)CrossRefGoogle Scholar
  35. 35.
    Tsallis, C.: Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World. Springer, New York (2009)zbMATHGoogle Scholar
  36. 36.
    Tsimpiris, A., Kugiumgis, D.: Feature selection for classification of oscillating time series. Expert Systems, doi:10.1111/j.1468-0394.2011.00605.x (2011)Google Scholar
  37. 37.
    Xu, G., Wang, J., Q, Z., Zhu, J.: An epileptic seizure prediction algorithm from scalp EEG based on morphological filter and Kolmogorov complexity. Lecture Notes in Computer Science 4561, 736 – 746 (2007)Google Scholar
  38. 38.
    Yum, M.K., Jung, K.Y., Kang, H.C., Kim, H.D., Shon, Y.M., Kang, J.K., Lee, I.K., Park, K.J., Kwon, O.Y.: Effect of a ketogenic diet on EEG: Analysis of sample entropy. Seizure-European Journal Of Epilepsy 17(6), 561–566 (2008)CrossRefGoogle Scholar
  39. 39.
    Zaffalon, M., Hutter, M.: Robust feature selection by mutual information distributions. In: Proceedings of the 18th International Conference on Uncertainty in Artificial Intelligence (UAI-2002), pp. 577 – 584. Morgan Kaufmann (2002)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Mathematical, Physical and Computational Sciences of EngineeringAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations