title: 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.
authors.affiliation: INSERM, U642, Université de Rennes 1, LTSI, Rennes, F-35000, France. paul.frogerais@univ-rennes1.fr
authors.forename: P
authors.initials: P
authors.lastname: Frogerais
authors.forename: J J
authors.initials: JJ
authors.lastname: Bellanger
authors.forename: F
authors.initials: F
authors.lastname: Wendling
mesh_terms: D000465:Algorithms; D001683:Biological Clocks; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D009435:Synaptic Transmission