A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
In Silico Application of the Epsilon-Greedy Algorithm for Frequency Optimization of Electrical Neurostimulation for Hypersynchronous Disorders
Tekijät: Da Silva Lima, Gabriel; Cota, Rosa Vinícius; Moreira Bessa, Wallace
Toimittaja: Riascos Salas, Jaime A.; Cota, Vinícius Rosa; Villota Hernán; Betancur Vasquez, Daniel
Konferenssin vakiintunut nimi: Latin American Workshop on Computational Neuroscience
Julkaisuvuosi: 2024
Journal: Communications in Computer and Information Science
Kokoomateoksen nimi: Computational Neuroscience: 4th Latin American Workshop, LAWCN 2023 Envigado, Colombia, November 28–30, 2023, Revised Selected Papers
Vuosikerta: 2108
Aloitussivu: 57
Lopetussivu: 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
Verkko-osoite: https://doi.org/10.1007/978-3-031-63848-0_5
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/457014030
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.
Julkaisussa olevat rahoitustiedot:
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.
