A machine learning approach to support deep brain stimulation programming


  • Viviana Gómez-Orozco Technological University of Pereira
  • Iván De La Pava Technological University of Pereira https://orcid.org/0000-0003-2593-2501
  • Andrés Álvarez-Meza National University of Colombia
  • Mauricio A. Álvarez University of Sheffield
  • Álvaro Orozco-Gutiérrez Technological University of Pereira https://orcid.org/0000-0002-1167-1446




volume of tissue activated, kernel-based learning, anisotropy


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.

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Author Biographies

Viviana Gómez-Orozco, Technological University of Pereira

Associate Professor of Engineering. Automatic Research Group, Faculty of Engineering.

Iván De La Pava, Technological University of Pereira

Associate Professor of Engineering. Automatic Research Group, Faculty of Engineering. 

Andrés Álvarez-Meza, National University of Colombia

Associate Professor of Engineering. Signal Processing and Recognition Group.

Mauricio A. Álvarez, University of Sheffield

Engineering Associate Research Professor. Department of Computer Science.

Álvaro Orozco-Gutiérrez, Technological University of Pereira

Associate Professor of Engineering. Automatic Research Group, Faculty of Engineering.


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How to Cite

Gómez-Orozco, V., De La Pava, I., Álvarez-Meza, A., Álvarez, M. A., & Orozco-Gutiérrez, Álvaro. (2020). A machine learning approach to support deep brain stimulation programming. Revista Facultad De Ingeniería Universidad De Antioquia, (95), 20–33. https://doi.org/10.17533/udea.redin.20190729

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