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

T. M. Medvedeva; A. K. Lüttjohann; M. V. Sysoeva; G. van Luijtelaar; I. V. Sysoev; T. M. Medvedeva; A. K. Lüttjohann; M. V. Sysoeva; G. van Luijtelaar; I. V. Sysoev

doi:10.3934/biophy.2020006

AIMS Biophysics

2020, Volume 7, Issue 2: 65-75. doi: 10.3934/biophy.2020006

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

1.
Saratov Branch of Kotel'nokov Institute of Radioengineering and Electronics of Russian Academy of Sciences, 38 Zelyonaya str., Saratov, Russia
2.
Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 5A Butlerova str., Moscow, Russia
3.
Saratov State University, 83 Astrakhanskaya str., Saratov, Russia
4.
Institute of Physiology I, Westfalische Wilhelms Universität, 27a Robert-Koch-Strabe, Münster, Germany
5.
Yuri Gagarin State Technical University of Saratov, 77 Politekhnicheskaya str., Saratov, Russia
6.
Donders Centre for Cognition, Radboud University, Nijmegen, P.O.Box 9104 6500 HE, Nijmegen, the Netherlands

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.
- absence epilepsy,
- Lyapunov exponent,
- time series analysis,
- EEG,
- spike-wave discharges,
- genetic absence epilepsy model,
- computational models
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

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Abstract

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.

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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