Modeling and Detection of Critical Slowing Down in Epileptic Dynamics

TL;DR

This paper models critical slowing down in epileptic dynamics, providing a new detection algorithm for early warning signs of seizures and proposing a feedback control method to prevent seizures.

Contribution

It introduces a multi-stable slow-fast system model for epilepsy, derives recovery rates, and develops a novel algorithm for early seizure detection and prevention.

Findings

Validated the model through numerical studies

Developed a new algorithm for seizure precursor detection

Proposed a feedback control strategy to prevent seizures

Abstract

Epilepsy is a common neurological disorder characterized by abrupt seizures. Although seizures may appear random, they are often preceded by early warning signs in neural signals, notably, critical slowing down, a phenomenon in which the system's recovery rate from perturbations declines when it approaches a critical point. Detecting these markers could enable preventive therapies. This paper introduces a multi-stable slow-fast system to capture critical slowing down in epileptic dynamics. We construct regions of attraction for stable states, shedding light on how dynamic bifurcations drive pathological oscillations. We derive the recovery rate after perturbations to formalize critical slowing down. A novel algorithm for detecting precursors to ictal transitions is presented, along with a proof-of-concept event-based feedback control strategy to prevent impending pathological oscillations. Numerical studies are conducted to validate our theoretical findings.