Research article Special Issues

Estimating complexity of spike-wave discharges with largest Lyapunov exponent in computational models and experimental data

  • Received: 14 February 2020 Accepted: 29 March 2020 Published: 07 April 2020
  • Here we consider the possibility to characterize the signal complexity of electroencephalograms using calculation of largest Lyapunov exponent explicitly from time series. This would help in detection of seizures, understanding and modeling epileptic activity. Baseline activity and spike-wave discharges (SWDs) were considered as regimes. Three channels relevant for absence epilepsy were studied: the parietal cortex, the ventroposterial medial nucleus of thalamus, and the reticular thalamic nucleus. Experimental data and two types of models were investigated. The result show that SWDs often treated as more or less regular oscillations are characterized by large positive Lyapunov exponent, not very different from the value obtained for baseline activity. The mesoscale network model of epilepsy is mostly able to reproduce this phenomenon, including absolute values. The more simple neuron mass model exhibits Lyapunov exponent during SWDs twice smaller than in baseline.

    Citation: T. M. Medvedeva, A. K. Lüttjohann, M. V. Sysoeva, G. van Luijtelaar, I. V. Sysoev. Estimating complexity of spike-wave discharges with largest Lyapunov exponent in computational models and experimental data[J]. AIMS Biophysics, 2020, 7(2): 65-75. doi: 10.3934/biophy.2020006

    Related Papers:

  • Here we consider the possibility to characterize the signal complexity of electroencephalograms using calculation of largest Lyapunov exponent explicitly from time series. This would help in detection of seizures, understanding and modeling epileptic activity. Baseline activity and spike-wave discharges (SWDs) were considered as regimes. Three channels relevant for absence epilepsy were studied: the parietal cortex, the ventroposterial medial nucleus of thalamus, and the reticular thalamic nucleus. Experimental data and two types of models were investigated. The result show that SWDs often treated as more or less regular oscillations are characterized by large positive Lyapunov exponent, not very different from the value obtained for baseline activity. The mesoscale network model of epilepsy is mostly able to reproduce this phenomenon, including absolute values. The more simple neuron mass model exhibits Lyapunov exponent during SWDs twice smaller than in baseline.



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    Acknowledgments



    This study was funded by Russian Science Foundation, grant number 19-72-10030.

    Conflict of interest



    Authors declare no conflict of interests.

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