Skip to main content
SpringerLink
Log in

EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine

  • Conference paper
Book cover Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 131))

Abstract

In this paper, we present a new method based on empirical mode decomposition (EMD) for classification of seizure and seizure-free EEG signals. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The method proposes the use of the area parameter and mean frequency estimation of IMFs in the classification of the seizure and seizure-free EEG signals. These parameters have been used as an input in least squares support vector machine (LS-SVM), which provides classification of seizure EEG signals from seizure-free EEG signals. The classification accuracy for classification of seizure and seizure-free EEG signals obtained by using proposed method is 98.33% for second IMF with radial basis function kernel of LS-SVM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Iasemidis, L.D., Shiau, D.S., et al.: Adaptive epileptic seizure prediction system. IEEE Trans. Biomed. Eng. 50, 616–627 (2003)

    Article  Google Scholar 

  2. Gotman, J.: Automatic detection of seizures and spikes. J. Clin. Neurophysiol. 16, 130–140 (1999)

    Article  Google Scholar 

  3. Hese, P.V., Martens, J.P., Boon, P., Dedeurwaerdere, S., Lemahieu, I., Walle, R.V.: Detection of spike and wave discharges in the cortical EEG of genetic absence epilepsy rats from Strasbourg. Phys. Med. Biol. 48, 1685–1700 (2003)

    Article  Google Scholar 

  4. Srinivasan, V., Eswaran, C., Sriraam, N.: Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 29, 647–660 (2005)

    Article  Google Scholar 

  5. Boashash, B., Mesbah, M., Colditz, P.: Time frequency detection of EEG abnormalities. In: Boashash, B. (ed.) Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, ch. 15, article 15.5, pp. 663–670. Elsevier (2003)

    Google Scholar 

  6. Pachori, R.B., Sircar, P.: EEG signal analysis using FB expansion and second-order linear TVAR process. Signal Process 88, 415–420 (2008)

    Article  Google Scholar 

  7. Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87 (2003)

    Article  Google Scholar 

  8. Khan, Y.U., Gotman, J.: Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114, 898–908 (2003)

    Article  Google Scholar 

  9. Tzallas, A.T., Tsipouras, M.G., Fotisdis, D.I.: Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput. Intell. Neurosci., article ID 80510 (2007)

    Google Scholar 

  10. Güler, N.F., Übeyli, E.D., Güler, İ.: Recurrent neural networks employing Lyapunov exponents for EEG signal classification. Expert Syst. Appl. 29, 506–514 (2005)

    Article  Google Scholar 

  11. Lehnertz, K., Elger, C.E.: Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr. Clin. Neurophysiol. 95, 108–117 (1995)

    Article  Google Scholar 

  12. Accardo, A., Affinito, M., Carrozzi, M., Bouquet, F.: Use of the fractal dimension for the analysis of electroencephalographic time series. Biol. Cybern. 77, 339–350 (1997)

    Article  Google Scholar 

  13. Kannathal, N., Choob, M.L., et al.: Entropies for detection of epilepsy in EEG. Computer Methods Progr. Biomed. 80, 187–194 (2005)

    Article  Google Scholar 

  14. Casdagli, M.C., Iasemidis, L.D., et al.: Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol. 102, 98–105 (1997)

    Article  Google Scholar 

  15. Song, Y., Lió, P.: A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J. Biomed. Sci. Eng. 3, 556–567 (2010)

    Article  Google Scholar 

  16. Ghosh-Dastidar, S., Adeli, H., Dadmehr, N.: Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 54, 1545–1551 (2007)

    Article  Google Scholar 

  17. Ghosh-Dastidar, S., Adeli, H., Dadmehr, N.: Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 55, 512–518 (2008)

    Article  Google Scholar 

  18. Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36, 2027–2036 (2009)

    Article  Google Scholar 

  19. Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximation entropy and artificial neural networks. J. Neurosci. Methods 193, 156–163 (2010)

    Article  Google Scholar 

  20. Pachori, R.B.: Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process., article ID 293056 (2008)

    Google Scholar 

  21. Oweis, R.J., Abdulhey, E.W.: Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed. Eng. Online 10, 38 (2011)

    Article  Google Scholar 

  22. Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition, Computer Methods Progr. Biomed. (2011) (in press), doi:10.1016/j.cmpb.2011.03.009

    Article  Google Scholar 

  23. Huang, N.E., et al.: The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lon. A 454, 903–995 (1998)

    Article  MathSciNet  Google Scholar 

  24. Andrzejak, R.G., Lehnertz, K., et al.: 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, article ID 061907 (2001)

    Google Scholar 

  25. Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11, 112–114 (2004)

    Article  Google Scholar 

  26. Cohen, M.E., Hudson, D.L., Deedwania, P.: Applying continuous chaotic modeling to cardic signal analysis. IEEE Eng. Med. Biol. Mag. 15, 97–102 (1996)

    Article  Google Scholar 

  27. Schroeder, J.: Signal processing via Fourier-Bessel series expansion. Digit. Signal Process. 3, 112–124 (1993)

    Article  MathSciNet  Google Scholar 

  28. Pachori, R.B., Hewson, D.: Assessment of effects of sensory perturbations using Fourier-Bessel expansion method for postual stability analysis. J. Intell. Syst. 20(2), 167–186 (2011)

    Google Scholar 

  29. Pachori, R.B., Sircar, P.: Analysis of multicomponent AM-FM signals using FB-DESA method. Digit. Signal Process. 20, 42–62 (2010)

    Article  Google Scholar 

  30. Pachori, R.B., Sircar, P.: A new technique to reduce cross terms in Wigner distribution. Digit. Signal Process. 17, 466–474 (2007)

    Article  Google Scholar 

  31. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  Google Scholar 

  32. Sukens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)

    Article  Google Scholar 

  33. Khandoker, A.H., Lai, D.T.H., Begg, R.K., Palaniswami, M.: Wavelet-based feature extraction for support vector machines for screening balance impairments in the eldery. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 587–597 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, Indian Institute of Technology Indore, 452017, Indore, India

    Varun Bajaj & Ram Bilas Pachori

Authors
  1. Varun Bajaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ram Bilas Pachori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Varun Bajaj .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Kusum Deep

  2. Department of Mathematics and, Computer Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

  3. , Department of Paper Technology, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Millie Pant

  4. Information Technology and Management, ABV-Indian Institute of, 109, E- Block, Gwalior, 474010, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer India Pvt. Ltd.

About this paper

Cite this paper

Bajaj, V., Pachori, R.B. (2012). EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_57

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-0491-6_57

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0490-9

  • Online ISBN: 978-81-322-0491-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation