274 | High-frequency oscillations biomarkers given by maximum entropy information in epileptic signals

Theoretical and Computational Neuroscience

Author: Mauro Granado | Email: granadomauro@gmail.com

Mauro Granado , Nataniel Martinez , Federico Miceli , Fernando Montani

1° Instituto de Física de La Plata (IFLP), CONICET-UNLP, La Plata, Buenos Aires, Argentina
2° Instituto de Investigaciones Físicas De Mar De Plata, CONICET-UNMdP, Mar del Plata, Buenos Aires, Argentina

Intracranial electroencephalography can directly record local field potentials from a large set of neurons in the vicinity of the electrode. To search for possible epileptic biomarkers and to determine the epileptogenic zone leading to seizures, we investigated the dynamics of the basal and preictal signals. For this purpose, we explored the dynamics of the time series recorded for different frequency bands considering high-frequency oscillations up to 240 Hz. We applied a Hilbert transformation to study the amplitude and phase of the signals, and characterized the dynamics of the different frequency bands in the time entropy-complexity plane, HxC, by comparing the dynamical evolution of the basal and preictal time series.
Our results show that as the system evolves temporally in the preictal state, the signal amplitudes of the HFO bands between 220-230 Hz and 230-240 Hz get closer and closer to the maximum entropy and minimum complexity. 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 the frequencies between 220-230 and 230-240 Hz. In this case the maximum entropy is equivalent to the principle of minimum resource consumption of the system. This corresponds to the minimization of the Gibbs free energy since randomness and low complexity seem to be a constraint for the signal dynamics in the preictal state for the frequency bands between