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Automated calibration of somatosensory stimulation using reinforcement learning

BACKGROUND: The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time...

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Autores principales: Borda, Luigi, Gozzi, Noemi, Preatoni, Greta, Valle, Giacomo, Raspopovic, Stanisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523674/
https://www.ncbi.nlm.nih.gov/pubmed/37752607
http://dx.doi.org/10.1186/s12984-023-01246-0
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author Borda, Luigi
Gozzi, Noemi
Preatoni, Greta
Valle, Giacomo
Raspopovic, Stanisa
author_facet Borda, Luigi
Gozzi, Noemi
Preatoni, Greta
Valle, Giacomo
Raspopovic, Stanisa
author_sort Borda, Luigi
collection PubMed
description BACKGROUND: The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. METHODS: We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. RESULTS: Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. CONCLUSIONS: Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts’ employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. Trial registration: ClinicalTrial.gov (Identifier: NCT04217005) SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-023-01246-0.
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spelling pubmed-105236742023-09-28 Automated calibration of somatosensory stimulation using reinforcement learning Borda, Luigi Gozzi, Noemi Preatoni, Greta Valle, Giacomo Raspopovic, Stanisa J Neuroeng Rehabil Research BACKGROUND: The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. METHODS: We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. RESULTS: Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. CONCLUSIONS: Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts’ employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. Trial registration: ClinicalTrial.gov (Identifier: NCT04217005) SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-023-01246-0. BioMed Central 2023-09-26 /pmc/articles/PMC10523674/ /pubmed/37752607 http://dx.doi.org/10.1186/s12984-023-01246-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Borda, Luigi
Gozzi, Noemi
Preatoni, Greta
Valle, Giacomo
Raspopovic, Stanisa
Automated calibration of somatosensory stimulation using reinforcement learning
title Automated calibration of somatosensory stimulation using reinforcement learning
title_full Automated calibration of somatosensory stimulation using reinforcement learning
title_fullStr Automated calibration of somatosensory stimulation using reinforcement learning
title_full_unstemmed Automated calibration of somatosensory stimulation using reinforcement learning
title_short Automated calibration of somatosensory stimulation using reinforcement learning
title_sort automated calibration of somatosensory stimulation using reinforcement learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523674/
https://www.ncbi.nlm.nih.gov/pubmed/37752607
http://dx.doi.org/10.1186/s12984-023-01246-0
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