High-frequency oscillations in the ripple bands and amplitude information coding: Toward a biomarker of maximum entropy in the preictal signals

Granado, Mauro; Collavini, Santiago; Baravalle, Roman; Martinez, Nataniel; Montemurro, Marcelo A.; Rosso, Osvaldo A. and Montani, Fernando (2022). High-frequency oscillations in the ripple bands and amplitude information coding: Toward a biomarker of maximum entropy in the preictal signals. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(9), article no. 093151.

DOI: https://doi.org/10.1063/5.0101220


Intracranial electroencephalography (iEEG) can directly record local field potentials (LFPs) from a large set of neurons in the vicinity of the electrode. To search for possible epileptic biomarkers and to determine the epileptogenic zone that gives rise to seizures, we investigated the dynamics of basal and preictal signals. For this purpose, we explored the dynamics of the recorded time series for different frequency bands considering high-frequency oscillations (HFO) up to 240 Hz. We apply a Hilbert transform to study the amplitude and phase of the signals. The dynamics of the different frequency bands in the time causal entropy-complexity plane, H×C, is characterized by comparing the dynamical evolution of the basal and preictal time series. As the preictal states evolve closer to the time in which the epileptic seizure starts, the, H×C, dynamics changes for the higher frequency bands. The complexity evolves to very low values and the entropy becomes nearer to its maximal value. These quasi-stable states converge to equiprobable states when the entropy is maximal, and the complexity is zero. We could, therefore, speculate that in this case, it corresponds to the minimization of Gibbs free energy. In this case, the maximum entropy is equivalent to the principle of minimum consumption of resources in the system. We can interpret this as the nature of the system evolving temporally in the preictal state in such a way that the consumption of resources by the system is minimal for the amplitude in frequencies between 220–230 and 230–240 Hz. iEEG permits us to describe deep brain electrical activity. In this work, we investigate the dynamics of preictal and basal signals in patients with refractory epilepsy using entropy and complexity quantifiers. Our results show that minutes before the epileptic seizure, the system evolves from a highly dissipative chaotic state of the basal period to a state where the entropy reaches a maximum and the complexity is significantly curtailed, corresponding to the preictal period.

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