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EEG Rhythm Extraction Based on Relevance Analysis and Customized Wavelet Transform

  • L. Duque-Muñoz
  • R. D. Pinzon-Morales
  • G. Castellanos-Dominguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)

Abstract

The waveform of physiological signals carries useful information about the brain states. Automated computational algorithms are used in clinical medicine for extracting this information that cannot be read directly by visual inspection. Nonetheless, difficulties arise in the extraction because the intrinsic rhythms of the waveforms vary with the changes in the state of the brain. That is the case for electroencephalogram (EEG) signals from Epileptic seizure events. Here, we address the extraction of information from EEG signals by using a novel methodology that quantitatively measures the intrinsic rhythms of EEG waveforms related to healthy or Epileptic seizure events. In this method, the customized wavelet is used to estimate the EEG rhythms and then the relevance analysis with Fuzzy entropy and Stochastic measure are used to discriminate between seizure free and seizure states. The classification stage is based on classification performance using a support vector machine classifier. The pertinence of the proposed methodology during the Epileptic seizure identification is discussed, and future directions are presented.

Keywords

Customized wavelet Epileptic seizure detection Relevant analysis Brain state classification 

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References

  1. 1.
    Cantero, J.L., Atienza, M., Salas, R.M.: Human alpha oscillations in wakefulness, drowsiness period, and REM sleep: different electroencephalographic phenomena within the alpha band. Neurophysiologie Clinique Clinical Neurophysiology 32, 54–71 (2002)CrossRefGoogle Scholar
  2. 2.
    West, M., Prado, R., Krystal, A.D.: Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series. Journal of the American Statistical Association 94, 375–387 (1999)CrossRefGoogle Scholar
  3. 3.
    De Clercq, W., Vanrumste, B., Papy, J.-M., Van Paesschen, W., Van Huffel, S.: Modeling common dynamics in multichannel signals with applications to artifact and background removal in EEG recordings. Trans. on Biomed. Eng. 52, 2006–2015 (2005)CrossRefGoogle Scholar
  4. 4.
    Gandhi, T., Ketan-Panigrahi, B., Anand, S.: A comparative study of wavelet families for EEG signal classification. Neurocomputing 74, 3051–3057 (2011)CrossRefGoogle Scholar
  5. 5.
    Pinzon-Morales, R.D., Orozco-Gutierrez, A.A., Castellanos-Dominguez, G.: Novel signal-dependent filter bank method for identification of multiple basal ganglia nuclei in Parkinsonian patients. J. Neural Eng. 8, 3 (2011)CrossRefGoogle Scholar
  6. 6.
    Gouze, A., Antonini, M., Barlaud, M., Macq, B.: Design of signal-adapted multidimensional lifting scheme for lossy coding. IEEE Trans. on Image Processing 13(12), 1589–1603 (2004)CrossRefGoogle Scholar
  7. 7.
    Kiymik, M.K., Güler, I., Dizibüyük, A., Akin, M.: Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Computers in Biology and Medicine 35, 603–616 (2005)CrossRefGoogle Scholar
  8. 8.
    Duque-Muñoz, L., Espinosa-Oviedo, J., Castellanos-Dominguez, G.: Identification and monitoring of brain activity based on stochastic relevance analysis of short-time EEG rhythms. Biomd Eng Online 13 (2014)Google Scholar
  9. 9.
    Jaganathan, P., Kuppuchamy, R.: A threshold fuzzy entropy based feature selection for medical database classification. Computers in Biology and Medicine 43, 2222–2229 (2013)CrossRefGoogle Scholar
  10. 10.
    Li, Y., Wei, H.-L., Billings, S.A., Sarrigiannis, P.G.: Time-varying model identification for time-frequency feature extraction from EEG data. Journal of Neuroscience Methods 196, 151–158 (2011)CrossRefGoogle Scholar
  11. 11.
    Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32, 1084–1093 (2007)CrossRefGoogle Scholar
  12. 12.
    Veluvolu, K., Wang, Y.: Adaptive estimation of EEG-rhythms for optimal band identification in BCI. J. Neurosci. Methods 203, 163–172 (2012)CrossRefGoogle Scholar
  13. 13.
    Pinzon-Morales, R.D., Hirata, Y.: Customization of Wavelet Function for Pupil Fluctuation Analysis to Evaluate Levels of Sleepiness. J. Communication and Computer 10, 585–592 (2013)Google Scholar
  14. 14.
    Gandhi, T., Panigrahi, B.K., Anand, S.: A comparative study of wavelet families for EEG signal classification. J. Neurocomputing 17(74), 3051–3057 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • L. Duque-Muñoz
    • 1
  • R. D. Pinzon-Morales
    • 2
  • G. Castellanos-Dominguez
    • 3
  1. 1.Research group SISTEMICUniversidad de AntioquiaMedellinColombia
  2. 2.The Neural Cybernetics laboratoryChubu UniversityKasugaiJaban
  3. 3.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaBogotáColombia

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