Model-based measurement of epileptic tissue excitability

Abstract

In the context of pre-surgical evaluation of epileptic patients, depth-EEG signals constitute a valuable source of information to characterize the spatiotemporal organization of paroxysmal interictal and ictal activities, prior to surgery. However, interpretation of these very complex data remains a formidable task. Indeed, interpretation is currently mostly qualitative and efforts are still to be produced in order to quantitatively assess pathophysiological information conveyed by signals. The proposed EEG model-based approach is a contribution to this effort. It introduces both a physiological parameter set which represents excitation and inhibition levels in recorded neuronal tissue and a methodology to estimate this set of parameters. It includes Sequential Monte Carlo nonlinear filtering to estimate hidden state trajectory from EEG and Particle Swarm Optimization to maximize a likelihood function deduced from Monte Carlo computations. Simulation results illustrate what it can be expected from this methodology.

Role of Multiple-Scale Modeling of Epilepsy in Seizure Forecasting

“…While seizure prediction was not the focus of this work, the inhibitory time constant showed a transition about 40 seconds before the onset of the seizure indicating the potential of this approach for seizure forecasting. Frogerais et al (2007) also demonstrated the feasibility of a similar approach using sequential Monte Carlo nonlinear filtering. Other work by Freestone et al (2011) and Aram et al (2013) demonstrated techniques for determining connectivity parameters for neural field models that can also be used to monitor brain changes.…”

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

“…These computer simulations underscore the hypothesis that this form of epileptic seizures are caused by a gradual change in some parameters that are crucial for the control of the dynamical state of underlying neuronal network activity. Currently, the same model is being investigated further in the sense of attempting to solve an inverse problem, i.e., to identify the set of model's parameters based on observed EEG signals recorded at different stages, interictal, preictal, at seizure onset and during a seizure [24].…”

A brief survey of computational models of normal and epileptic EEG signals: A guideline to model-based seizure prediction

“…The log-likelihood, p ( y :θ), is obtained using different approaches. [ 68 ] For example the model output is analytically a Markov process, so that the likelihood can be approximated by using the optimal filter or Kalman Filter. Optimization of the obtained likelihood is conducted by using the simulated annealing technique.…”