Passivity-Based Control of Electrographic Seizures in a Neural Mass Model of Epilepsy

TL;DR

This paper provides a rigorous passivity-based control analysis of epileptic seizure dynamics using the Epileptor model, offering a new framework for closed-loop neuromodulation in drug-resistant epilepsy.

Contribution

It analytically demonstrates how seizure dynamics can be stabilized and passivated through proper feedback and output redesign, advancing seizure control methods.

Findings

Seizure dynamics are neither passive nor passivatable in their standard form.

Strong passive feedback can stabilize epileptic dynamics.

Proper output redesign can passivate seizure dynamics.

Abstract

Recent advances in neurotechnologies and decades of scientific and clinical research have made closed-loop electrical neuromodulation one of the most promising avenues for the treatment of drug-resistant epilepsy (DRE), a condition that affects over 15 million individuals globally. Yet, with the existing clinical state of the art, only 18% of patients with DRE who undergo closed-loop neuromodulation become seizure-free. In a recent study, we demonstrated that a simple proportional feedback policy based on the framework of passivity-based control (PBC) can significantly outperform the clinical state of the art. However, this study was purely numerical and lacked rigorous mathematical analysis. The present study addresses this gap and provides the first rigorous analysis of PBC for the closed-loop control of epileptic seizures. Using the celebrated Epileptor neural mass model of epilepsy, we analytically demonstrate that (i) seizure dynamics are, in their standard form, neither passive nor passivatable, (ii) epileptic dynamics, despite their lack of passivity, can be stabilized by sufficiently strong passive feedback, and (iii) seizure dynamics can be passivated via proper output redesign. To our knowledge, our results provide the first rigorous passivity-based analysis of epileptic seizure dynamics, as well as a theoretically-grounded framework for sensor placement and feedback design for a new form of closed-loop neuromodulation with the potential to transform seizure management in DRE.