Detection and suppression of epileptiform seizures via model-free control and derivatives in a noisy environment
Cédric Join, D. Blair Jovellar, Emmanuel Delaleau, Michel Fliess
TL;DR
This work tackles closed-loop suppression of epileptiform seizures using a model-free control framework, avoiding detailed biophysical modeling while enabling effective stimulation in noisy environments. The core approach combines an intelligent proportional-derivative regulator (iPD) from model-free control with a time-domain maxima-based seizure detector and an algebraic differentiator for robust $d/dt$ estimation. The virtual patient, Wendling's neural mass model (derived from the Jansen-Rit framework), is used to test robustness across parameter variations and disturbances via simulations. Results indicate that the proposed control scheme achieves robust seizure suppression with reduced modeling burden, suggesting a practical and tunable neurostimulation strategy for real-time neuronal regulation in noisy clinical settings.
Abstract
Recent advances in control theory yield closed-loop neurostimulations for suppressing epileptiform seizures. These advances are illustrated by computer experiments which are easy to implement and to tune. The feedback synthesis is provided by an intelligent proportional-derivative (iPD) regulator associated to model-free control. This approach has already been successfully exploited in many concrete situations in engineering, since no precise computational modeling is needed. iPDs permit tracking a large variety of signals including high-amplitude epileptic activity. Those unpredictable pathological brain oscillations should be detected in order to avoid continuous stimulation, which might induce detrimental side effects. This is achieved by introducing a data mining method based on the maxima of the recorded signals. The real-time derivative estimation in a particularly noisy epileptiform environment is made possible due to a newly developed algebraic differentiator. The virtual patient is the Wendling model, i.e., a set of ordinary differential equations adapted from the Jansen-Rit neural mass model in order to generate epileptiform activity via appropriate values of excitation- and inhibition-related parameters. Several simulations, which lead to a large variety of possible scenarios, are discussed. They show the robustness of our control synthesis with respect to different virtual patients and external disturbances.