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Wavelet Jensen–Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures

Author

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  • Pereyra, M.E.
  • Lamberti, P.W.
  • Rosso, O.A.

Abstract

We develop a quantitative method of analysis of EEG records. The method is based on the wavelet analysis of the record and on the capability of the Jensen–Shannon divergence (JSD) to identify dynamical changes in a time series. The JSD is a measure of distance between probability distributions. Therefore for its evaluation it is necessary to define a (time dependent) probability distribution along the record. We define this probability distribution from the wavelet decomposition of the associated time series. The wavelet JSD provides information about dynamical changes in the scales and can be considered a complementary methodology reported earlier [O.A. Rosso, S. Blanco, A. Rabinowicz, Signal Processing 86 (2003) 1275; O.A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, E. Başar, J. Neurosci. Methods 105 (2001) 65; O.A. Rosso, M.T. Martin, A. Figliola, K. Keller, A. Plastino, J. Neurosci. Methods 153 (2006) 163]. In the present study we have demonstrated it by analyzing EEG signal of tonic–clonic epileptic seizures applying the JSD method. The display of the JSD curves enables easy comparison of frequency band component dynamics. This would, in turn, promise easy and successful comparison of the EEG records from various scalp locations of the brain.

Suggested Citation

  • Pereyra, M.E. & Lamberti, P.W. & Rosso, O.A., 2007. "Wavelet Jensen–Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 122-132.
    • Handle: RePEc:eee:phsmap:v:379:y:2007:i:1:p:122-132
    DOI: 10.1016/j.physa.2006.12.051
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    Cited by:

    1. Bozhokin, S.V. & Suslova, I.B., 2015. "Wavelet-based analysis of spectral rearrangements of EEG patterns and of non-stationary correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 151-160.
    2. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.

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