Abstract
Epilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Given the partially successful existing treatments for epileptiform activity suppression, dynamic mathematical models have been proposed with the purpose of better understanding the factors that might trigger an epileptic seizure and how to mitigate it, among which Epileptor stands out, due to its relative simplicity and high prediction ability. Recent studies using such a model have provided evidence that establishing a feedback-based control approach is possible. However, for this strategy to work properly, Epileptor's parameters, which describe the dynamic characteristics of a seizure, must be known beforehand. Therefore, this work proposes a methodology for estimating such parameters based on a successive optimization technique. The results show that it is feasible to approximate their values, and integrating this system identification approach with observers and controllers can be carried out, which might provide an interesting alternative for seizure suppression in practice in the future.
Implantable Stimulator for Epileptic Seizure Suppression With Loading Impedance Adaptability