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Characterization of time dynamical evolution of electroencephalographic epileptic records

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  • Rosso, Osvaldo A
  • Mairal, Marı́a Liliana

Abstract

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of the brain dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. The entropy defined from the wavelet functions is a measure of the order/disorder degree present in a time series. In consequence, this entropy evaluates over EEG time series gives information about the underlying dynamical process in the brain, more specifically of the synchrony of the group cells involved in the different neural responses. The total wavelet entropy results independent of the signal energy and becomes a good tool for detecting dynamical changes in the system behavior. In addition the total wavelet entropy has advantages over the Lyapunov exponents, because it is parameter free and independent of the stationarity of the time series. In this work we compared the results of the time evolution of the chaoticity (Lyapunov exponent as a function of time) with the corresponding time evolution of the total wavelet entropy in two different EEG records, one provide by depth electrodes and other by scalp ones.

Suggested Citation

  • Rosso, Osvaldo A & Mairal, Marı́a Liliana, 2002. "Characterization of time dynamical evolution of electroencephalographic epileptic records," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 469-504.
    • Handle: RePEc:eee:phsmap:v:312:y:2002:i:3:p:469-504
    DOI: 10.1016/S0378-4371(02)00914-7
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    Citations

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    Cited by:

    1. Zunino, L. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2007. "Wavelet entropy of stochastic processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 503-512.
    2. Kowalski, A.M. & Martín, M.T. & Plastino, A. & Rosso, O.A., 2011. "Fisher information description of the classical–quantal transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2435-2441.
    3. Argyroudis, George S. & Siokis, Fotios M., 2019. "Spillover effects of Great Recession on Hong-Kong’s Real Estate Market: An analysis based on Causality Plane and Tsallis Curves of Complexity–Entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 576-586.
    4. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2009. "Forbidden patterns, permutation entropy and stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2854-2864.
    5. Redelico, Francisco O. & Traversaro, Francisco & Oyarzabal, Nicolás & Vilaboa, Ivan & Rosso, Osvaldo A., 2017. "Evaluation of the status of rotary machines by time causal Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 321-329.
    6. Ancona, N. & Angelini, L. & De Tommaso, M. & Marinazzo, D. & Nitti, L. & Pellicoro, M. & Stramaglia, S., 2006. "Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 491-498.
    7. Rosso, Osvaldo A. & De Micco, Luciana & Plastino, A. & Larrondo, Hilda A., 2010. "Info-quantifiers’ map-characterization revisited," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4604-4612.
    8. Zunino, L. & Pérez, D.G. & Garavaglia, M. & Rosso, Osvaldo A., 2006. "Characterization of laser propagation through turbulent media by quantifiers based on the wavelet transform: Dynamic study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 79-86.
    9. Andres M. Kowalski & Mariela Portesi & Victoria Vampa & Marcelo Losada & Federico Holik, 2022. "Entropy-Based Informational Study of the COVID-19 Series of Data," Mathematics, MDPI, vol. 10(23), pages 1-16, December.
    10. Siokis, Fotios M., 2018. "Credit market Jitters in the course of the financial crisis: A permutation entropy approach in measuring informational efficiency in financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 266-275.
    11. Baravalle, Roman & Rosso, Osvaldo A. & Montani, Fernando, 2018. "Discriminating imagined and non-imagined tasks in the motor cortex area: Entropy-complexity plane with a wavelet decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 27-39.

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