Detection and suppression of epileptiform seizures via model-free control and derivatives in a noisy environment

TL;DR

This paper presents a model-free control method using an intelligent proportional-derivative regulator combined with data mining and algebraic differentiation to detect and suppress epileptiform seizures in noisy environments, demonstrated on a neural mass model.

Contribution

It introduces a novel model-free control approach with real-time derivative estimation and data mining for seizure detection, enhancing robustness and avoiding continuous stimulation.

Findings

Effective seizure suppression in noisy conditions

Robustness across different virtual patient models

Accurate detection using maxima-based data mining

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.