Detection and suppression of epileptiform seizures via model-free control and derivatives in a noisy environment
<|reference_start|>Detection and suppression of epileptiform seizures via model-free control and derivatives in a noisy environment: 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.<|reference_end|>
@article{join2024detection,
    title={Detection and suppression of epileptiform seizures via model-free
  control and derivatives in a noisy environment},
    author={C'edric Join, D. Blair Jovellar, Emmanuel Delaleau, Michel Fliess},
    journal={arXiv preprint arXiv:2410.01403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01403},
    primaryClass={eess.SY cs.SY math.OC}
}