A4 Refereed article in a conference publication
In Silico Application of the Epsilon-Greedy Algorithm for Frequency Optimization of Electrical Neurostimulation for Hypersynchronous Disorders
Authors: Da Silva Lima, Gabriel; Cota, Rosa Vinícius; Moreira Bessa, Wallace
Editors: Riascos Salas, Jaime A.; Cota, Vinícius Rosa; Villota Hernán; Betancur Vasquez, Daniel
Conference name: Latin American Workshop on Computational Neuroscience
Publication year: 2024
Journal: Communications in Computer and Information Science
Book title : Computational Neuroscience: 4th Latin American Workshop, LAWCN 2023 Envigado, Colombia, November 28–30, 2023, Revised Selected Papers
Volume: 2108
First page : 57
Last page: 68
ISBN: 978-3-031-63847-3
eISBN: 978-3-031-63848-0
eISSN: 1865-0937
DOI: https://doi.org/10.1007/978-3-031-63848-0_5(external)
Web address : https://doi.org/10.1007/978-3-031-63848-0_5(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457014030(external)
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.
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Funding information in the publication:
This work was supported by the Marie Skłodowska-Curie Individual Fellowship MoRPHEUS granted to VC, Grant Agreement no. 101032054, funded by the European Union under the framework programme H2020-EU.1.3. - EXCELLENT SCIENCE, and by the Brazilian research agencies CNPq and CAPES.
