Skip to main content

Application of Higher Order Spectra to Identify Epileptic EEG

Journal of Medical Systems volume 35, pages 1563–1571 (2011)Cite this article

Abstract

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  1. Cockerell, O. C., Johnson, A. L., Sander, J. W., Hart, Y. M., Goodridge, D. M., and Shorvon, S. D., Mortality from epilepsy: results from a prospective population-based study. Lancet. 344:918–921, 1994.

    Article  Google Scholar 

  2. Callaway, E., and Harris, P. R., Coupling between cortical potentials from different areas. Science. 183:873–875, 1974.

    Article  Google Scholar 

  3. Babloyantz, A., Nicolis, C., and Salazar, J. M., Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys. Lett. 111 A:152–157, 1985.

    Google Scholar 

  4. Mormann, F., Thomas, K., Christoph, R., Andrzejak, R., Kraskov, A., David, P., Elger, C. E., and Lehnertz, K., On the predictability of epileptic seizures. Clin. Neurophysiol. 116:569–587, 2005.

    Article  Google Scholar 

  5. Niederhoefer, C., Gollas, F., Chernihovskyi, A., Lehnertz, K., and Tetzlaff, R., Detection of seizure precursors in the EEG with cellular neural networks. Epilepsia. 45(7):245, 2004.

    Google Scholar 

  6. Kaplan, A. Y., Segmental structure of EEG more likely reveals the dynamic multistability of the brain tissue than the continual plasticity one. Proceedings of ICONIP’ 99, Perth, Australia, 1999, 633–638.

  7. Stam, C. J., Pijn, J. P., Suffczynski, P., and Lopez da Silva, F. H., Dynamics of the human alpha rhythm: evidence for nonline. Clin. Neurophysiol. 110(10):1801–1813, 1999.

    Article  Google Scholar 

  8. Zhuo, S. M., Gan, J. Q., and Sepulveda, F., Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface. Inf. Sci. 178(6):1629–1640, 2008.

    Article  Google Scholar 

  9. Shen, M., Chan, F. H. Y., Sun, L., and Beadle, B. J., Parametric bispectral estimation of EEG signals in different functional states of the brain. IEE Proc. Sci. Meas. Technol. 147(6):374–377, 2000.

    Article  Google Scholar 

  10. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Analysis of epileptic EEG signals using higher order spectra. J. Med. Eng. Technol. 33(1):42–50, 2009.

    Article  Google Scholar 

  11. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study. International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, 3824–3827.

  12. EEG time series Database, http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.

  13. Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, P., and Elger, C. E., Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E. 64:061907, 2001.

    Article  Google Scholar 

  14. Inouye, T., Shinosaki, K., Sakamoto, H., Toi, S., Ukai, S., Iyama, A., Katsuda, Y., and Hirano, M., Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr. Clin. Neurophysiol. 79:204–210, 1991.

    Article  Google Scholar 

  15. Ng, T. T., Chang, S. F., and Sun, Q., Blind detection of photomontage using higher order statistics, IEEE International Symposium on Circuits and Systems (ISCAS), Vancouver, Canada, May 2004.

  16. Bilmes, J. A., A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden Markov models. International Computer Science Institute, 1998.

  17. Vapnik, V., Statistical learning theory. Willey, New York, 1998.

    MATH  Google Scholar 

  18. Burgess, C. J., A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2):1–47, 1998.

    Article  Google Scholar 

  19. Christianini, N., and Taylor, J., Support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, 2000.

    Google Scholar 

  20. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf, B., An introduction to Kernel based learning algorithms. IEEE Trans. Neural Netw. 12:181–201, 2001.

    Article  Google Scholar 

  21. Hsu, C. W., Chang, C. C., and Lin, C. J., A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University 2003.

  22. Ceruti, G. M., and Rubin, S. H., Infodynamics: analogical analysis of states of matter and information. Inf. Sci. 177(4):969–987, 2007.

    Article  Google Scholar 

  23. He, M., Wen-Jian, C., and Shao-Yuan, L., Multiple fuzzy model-based temperature predictive control for HVAC systems. Inf. Sci. 169(1–2):155–174, 2005.

    Article  MATH  Google Scholar 

  24. DeLeo, J. M., Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. Proceedings of the Second International Symposium on Uncertainty Modeling and Analysis, IEEE Computer Society Press, 1993, 318–325.

  25. Downey, T. J., Meyer, D. J., Price, R. K., and Spitznagel, E. L., Using the receiver operating characteristic to assess the performance of neural classifiers. Int. Joint Conf. Neural Networks. 5:3642–3646, 1999.

    Google Scholar 

  26. Myles, P. S., Leslie, K., McNeil, J., Forbes, A., and Chan, M. T. V., Bispectral index monitoring to prevent awareness during anesthesia: the B-Aware randomized controlled trial. Lancet. 363(9423):1757–1763, 2004.

    Article  Google Scholar 

  27. Huang, L., Zhao, J., Singare, S., Wang, J., and Wang, Y., Discrimination of cerebral ischemic states using bispectrum analysis of EEG and artificial neural network. Med. Eng. Phys. 29(1):1–7, 2007.

    Article  Google Scholar 

  28. BCI Competition II—final result http://ida.first.fraunhofer.de/projects/bci/competition_ii/results/index.html (Access time: 30th March 2009).

  29. Ravelli, F., and Antolini, R., Complex dynamics underlying the human electroencephalogram. Biol. Cybern. 67:57–65, 1992.

    Article  MATH  Google Scholar 

  30. Petitmengin, C., Baulac, M., and Navarro, V., Seizure anticipation: are neurophenomenological approaches able to detect preictal symptoms? Epilepsy Behav. 9(2):298–306, 2006.

    Article  Google Scholar 

  31. Lehnertz, K., and Elger, C. E., Can epileptic seizures be predicted? Evidence from nonlinear time series analyses of brain electrical activity. Phys. Rev. Lett. 80:5019–5023, 1988.

    Article  Google Scholar 

  32. Martinerie, J., Adam, C., Le van Quyen, M., Baulac, M., Renault, B., and Varela, F. J., Can epileptic crisis be anticipated? Nat. Med. 4:1173–1176, 1998.

    Article  Google Scholar 

  33. Kannathal, N., Lim, C. M., Acharya, U. R., and Sadasivan, P. K., Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80(3):187–94, 2005.

    Article  Google Scholar 

  34. Lasemidis, L. D., Shiau, D. S., Sackellares, J. C., Pardalos, P. M., and Prasad, A., Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques. IEEE Trans. Biomed. Eng. 51(3):493–506, 2004.

    Article  Google Scholar 

  35. Lasemidis, L. D., Pardalos, P., Sackellares, J. C., and Shiau, D., Quadratic binary programming and dynamical system approach to determine the predictability of epileptic seizures. J. Comb. Optim. 5:9–26, 2001.

    Article  MathSciNet  Google Scholar 

  36. Nigam, V. P., and Graupe, D., A neural-network-based detection of epilepsy. Neurol. Res. 26(6):55–60, 2004.

    Article  Google Scholar 

  37. Srinivasan, V., Eswaran, C., and Sriraam, N., Artificial neural network based epileptic detection using time-domain and frequency domain features. J. Med. Syst. 29(6):647–60, 2005.

    Article  Google Scholar 

  38. Kannathal, N., Acharya, U. R., Lim, C. M., and Sadasivan, P. K., Characterization of EEG—a comparative study. Comp. Meth. Prog. Biomed. 80(1):17–23, 2005.

    Article  Google Scholar 

  39. Polat, K., and Guenes, S., Classification of epileptiform EEG using a hybrid systems based on decision tree classifier and fast fourier transform. Appl. Math. Comput. 32(2):625–31, 2007.

    Google Scholar 

  40. Subasi, A., Signal classification using wavelet feature extraction and a mixture of expert model. Exp. Syst. Appl. 32(4):1084–93, 2007.

    Article  Google Scholar 

  41. Guler, N. F., Ubey, E. D., and Guler, I., Recurrent neural network employing Lyapunov exponents for EEG signals classification. Exp. Syst. Appl. 29(3):506–14, 2005.

    Article  Google Scholar 

  42. Sadati, N., Mohseni, H. R., and Magshoudi, A., Epileptic seizure detection using neural fuzzy networks. In: Proc. Of the IEEE International Conference on Fuzzy Syst., 16–21 Jul 2006, Canada, pp. 596–600.

Download references

Author information

Affiliations

  1. School of Engineering, Division of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    Kuang Chua Chua, U. Rajendra Acharya & C. M. Lim

  2. School of Engineering Systems, Faculty of Built Environment and Engineering, Queensland University of Technology, Gardens Point, 2 George Street, Brisbane, Queensland, 4001, Australia

    Kuang Chua Chua & V. Chandran

Authors
  1. Kuang Chua Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Chandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. U. Rajendra Acharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. M. Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kuang Chua Chua.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Chua, K.C., Chandran, V., Acharya, U.R. et al. Application of Higher Order Spectra to Identify Epileptic EEG. J Med Syst 35, 1563–1571 (2011). https://doi.org/10.1007/s10916-010-9433-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9433-z

Keywords

  • EEG
  • Epilepsy
  • Pre-ictal
  • Entropy
  • Bispectrum
  • Power spectrum
  • GMM
  • ROC