In Silico Application of the Epsilon-Greedy Algorithm for Frequency Optimization of Electrical Neurostimulation for Hypersynchronous Disorders

One of the most promising alternatives to suppress epileptic seizures in drug-resistant and neurosurgery-refractory patients is using electro-electronic devices. By applying an appropriate pulsatile electrical stimulation, the process of ictogenesis can be quickly suppressed. However, in designing such stimulation devices, a common problem is defining suitable parameters such as pulse amplitude, duration, and frequency. In this work, we propose a machine learning technique based on the epsilon-greedy algorithm to optimize the pulse frequency which could prevent abnormal neuronal activity without exceeding energy usage for the stimulation. Five different simulations were carried out in order to evaluate the contribution of the energy consumption in determining the minimum frequency. The results show the efficacy of the proposed algorithm to search the minimum pulse frequency necessary to suppress epileptic seizures.