Characterizing the dynamics of EEG waveforms as the path through parameter space of a mean-field neuronal model: application to epilepsy seizure evolution

In this paper we propose that the dynamic evolution of EEG activity during epileptic seizures may be characterized as a path through parameter space of a mean-field model, reflecting gradual changes in underlying physiological mechanisms. Previous theoretical studies have demonstrated boundaries in parameter space of the model corresponding to transitions in EEG waveforms between apparently normal, spike and wave and subsequently poly-spike and wave activity. In the present manuscript, we develop an evolutionary algorithm that can estimate parameters of an underlying model from clinical data recordings. Our method is novel in that rather than attempting to estimate parameters in the frequency domain, we instead estimate in the time domain, choosing parameters according to the best fit obtained between the model output and features of the observed EEG waveform. We present comparisons of such paths through parameter space from separate seizures from an individual subject, as well as between different subjects. We propose that this method provides a novel approach to classifying seizures and epilepsies in idiopathic generalized epilepsy on the basis of differences in seizure evolution characterized by the path through parameter space. We anticipate that such an explanatory approach to classifying epilepsies and seizures may have potential to provide biomarkers of treatment outcome that might be determinable at point of first diagnosis from routine clinical EEG.