Gómez-Orozco, V., De La Pava Panche, I., Álvarez-Meza, A.M. et al. (2 more authors) (2020) A machine learning approach to support deep brain stimulation programming. Revista Facultad de Ingeniería (95). pp. 20-33. ISSN 0120-6230
Abstract
Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced by a particular stimulation setting have been developed. However, the medical specialist still has to search, by trial and error, for a DBS set-up that generates the desired VTA. Therefore, our goal is developing a DBS parameter tuning strategy for current clinical devices that allows defining a target VTA under biophysically viable constraints. We propose a machine learning approach that allows estimating the DBS parameter values for a given VTA, which comprises two main stages: i) A K-nearest neighbors-based deformation to define a target VTA preserving biophysically viable constraints. ii) A parameter estimation stage that consists of a data projection using metric learning to highlight relevant VTA properties, and a regression/classification algorithm to estimate the DBS parameters that generate the target VTA. Our methodology allows setting a biophysically compliant target VTA and accurately predicts the required configuration of stimulation parameters. Also, the performance of our approach is stable for both isotropic and anisotropic tissue conductivities. Furthermore, the computational time of the trained system is acceptable for real-world implementations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. https://creativecommons.org/licenses/by-nc-sa/4.0/ |
Keywords: | Volume of tissue activated; kernel-based learning; anisotropy |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Feb 2020 16:40 |
Last Modified: | 26 Feb 2020 16:40 |
Status: | Published |
Publisher: | Universidad de Antioquia |
Refereed: | Yes |
Identification Number: | 10.17533/udea.redin.20190729 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157519 |