EMD Based Features for Discrimination of Focal and Non-focal EEG Signals

  • Manish Gehlot
  • Yogit Kumar
  • Harshita Meena
  • Varun Bajaj
  • Anil Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)


In this paper, a new method based on empirical mode decomposition (EMD) for discrimination of focal and non-focal electroencephalogram (EEG) signals is proposed. 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 IMFs obtained by EMD of EEG signals are plotted in a 3-D phase space diagram using phase space reconstruction (PSR). The average mean of Euclidean distances (AMED) and average standard deviation of Euclidean distances (ASED) are computed from 3-D phase space diagram. These features has been used as a feature in order to discriminate focal and non-focal EEG signals. The AMED and ASED measurement of IMFs has provided better discrimination performance. The class discriminating ability of these features are quantified using Kruskal-Wallis statistical test.


Focal EEG signal Empirical mode decomposition (EMD) Non-focal EEG signal Phase space diagram 


  1. 1.
    Andrzejak, R.G., Schindler, K., Rummel, C.: Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E. 86, 046206 (2012)Google Scholar
  2. 2.
    Pati, S., Alexopoulos, A.V.: Pharmacoresistant epilepsy: from pathogenesis to current and emerging therapies. Cleveland Clin. J. Med. 77, 457–467 (2010)CrossRefGoogle Scholar
  3. 3.
    Chen, G.: Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst. Appl. 41, 2391–2394 (2014)CrossRefGoogle Scholar
  4. 4.
    Kumar, Y., Dewal, M.L., Anand, R.S.: Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133, 271–279 (2014)CrossRefGoogle Scholar
  5. 5.
    Naghsh-Nilchi, A.R., Aghashahi, M.: Epilepsy seizure detection using eigen-system spectral estimation and multiple layer perceptron neural network. Biomed. Signal Process. Control 5, 147–157 (2010)CrossRefGoogle Scholar
  6. 6.
    Hocepied, G., Legros, B., Bogaert, P.V., Grenez, F., Nonclercq, A.: Early detection of epileptic seizures based on parameter identification of neural mass model. Comput. Biol. Med. 43, 1773–1782 (2013)CrossRefGoogle Scholar
  7. 7.
    Pachori, R.B.: Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process. 2008, 293056 (2008)Google Scholar
  8. 8.
    Oweis, R.J., Abdulhey, E.W.: Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed. Eng. Online 10, 38 (2011)CrossRefGoogle Scholar
  9. 9.
    Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Progr. Biomed. 104(3), 373–381 (2011) Google Scholar
  10. 10.
    Bajaj, V., Pachori, R.B.: Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed. Eng. Lett. 3(1), 17–21 (2013)CrossRefGoogle Scholar
  11. 11.
    Bajaj, V., Pachori, R.B.: Classification of seizure and non-seizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16(6), 1135–1142 (2012)CrossRefGoogle Scholar
  12. 12.
    Bajaj, V., Pachori, R.B.: EEG signal classification using empirical mode decomposition and support vector machine. In: International Conference on Soft Computing for Problem Solving, AISC 131, pp. 623–635 (2011) Google Scholar
  13. 13.
    Bajaj, V., Pachori, R.B.: Application of the sample entropy for discrimination between seizure and seizure-free EEG signals. In: 5th Indian International Conference on Artificial Intelligence, pp. 1232–1247 (2011)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Zhu, G., Li., Y., Wen, P.P., Wang, S., Xi, M.: Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. Am. Inst. Phys. Conf. Proc. 1559, 31–36 (2013) Google Scholar
  16. 16.
    Sharma, R., Pachori, R.B., Gautam, S.: Empirical mode decomposition based classification of focal and non-focal EEG signals. In: International Conference on Medical Biometrics, pp. 135–140 (2014)Google Scholar
  17. 17.
    Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11, 112–114 (2004)CrossRefGoogle Scholar
  18. 18.
    Taken, F.: Detecting strange attractors in turbulence. In: Proceedings of Dynamics System and Turbulence, pp. 366–381 (1980)Google Scholar
  19. 19.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)Google Scholar
  20. 20.
    Bajaj, V., Pachori, R.B.: Human emotion classification from EEG signals using multiwavelet transform. In: IEEE International Conference on Medical Biometrics, pp. 125–130 (2014)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Manish Gehlot
    • 1
  • Yogit Kumar
    • 1
  • Harshita Meena
    • 1
  • Varun Bajaj
    • 1
  • Anil Kumar
    • 1
  1. 1.Discipline of Electronics and Communication EngineeringPDPM Indian Institute of Information Technology, Design and Manufacturing JabalpurJabalpurIndia

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