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Closed-Loop Control of Epilepsy Based on Reinforcement Learning.

Ruimin Dan1, Honghui Zhang1, Jianchao Bai1

  • 1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, P. R. China.

International Journal of Neural Systems
|October 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive deep reinforcement learning strategy for epilepsy treatment using deep deterministic policy gradient (DDPG) and model-agnostic meta-learning (MAML). The novel approach significantly reduces seizure frequency and duration while optimizing energy efficiency.

Keywords:
Epilepsycollaborative control strategydeep brain stimulationdeep reinforcement learning

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Epilepsy treatment remains challenging, often relying on open-loop systems with limited adaptability.
  • Current deep brain stimulation (DBS) strategies lack personalized, real-time adjustments for optimal seizure control.

Purpose of the Study:

  • To develop a novel adaptive deep brain stimulation (DBS) control strategy for epilepsy treatment using deep reinforcement learning.
  • To enhance seizure reduction, minimize energy consumption, and enable personalized treatment across diverse patient scenarios.

Main Methods:

  • A random disturbance model of the cortical-thalamus loop was established, transforming the neural modulation problem into a Markov decision process.
  • The Deep Deterministic Policy Gradient (DDPG) algorithm was employed for adaptive dynamic regulation of stimulation parameters.
  • Model-Agnostic Meta-Learning (MAML) was integrated with DDPG to create a collaborative control strategy with transfer learning capabilities.

Main Results:

  • The adaptive DBS control strategy significantly reduced seizure frequency and duration in various epilepsy simulation scenarios.
  • The closed-loop system demonstrated a reduction in energy loss by [Formula: see text] and an increase in non-epileptic states by [Formula: see text] compared to open-loop systems.
  • The MAML-DDPG strategy showed significant advantages across different epilepsy patient scenarios, indicating strong transfer learning capabilities.

Conclusions:

  • The proposed adaptive DBS control strategy based on deep reinforcement learning offers an effective approach for epilepsy treatment.
  • The integration of MAML enhances the strategy's adaptability and personalization, providing crucial technical support for precise epilepsy management.
  • This research paves the way for more intelligent and efficient closed-loop neuromodulation systems.