Closed-Loop Control of Epilepsy Based on Reinforcement Learning.

This study proposes a novel adaptive DBS control strategy for epilepsy treatment based on deep reinforcement learning. By establishing a random disturbance model of the cortical-thalamus loop, the neural modulation problem is successfully transformed into a Markov decision process. Deep Deterministic Policy Gradient (DDPG) algorithm is employed to achieve adaptive dynamic regulation of stimulation parameters, significantly reducing seizure frequency and duration in various epilepsy simulation scenarios. Experimental results demonstrate that the closed-loop control system can further reduce energy loss by [Formula: see text] ([Formula: see text]) compared to conventional open-loop system, while increase the proportion of non-epileptic states by [Formula: see text] ([Formula: see text]). Furthermore, we innovatively integrate Model-Agnostic Meta-Learning (MAML) with DDPG to develop a collaborative control strategy with transfer learning capabilities. This strategy demonstrates significant advantages across different epilepsy patient scenarios, which offers crucial technical support for the precise and adaptive development of epilepsy treatment.