A framework for adapting deep brain stimulation using Parkinsonian state estimates

Mohammed, Ameer, Bayford, Richard ORCID logoORCID: https://orcid.org/0000-0001-8863-6385 and Demosthenous, Andreas (2020) A framework for adapting deep brain stimulation using Parkinsonian state estimates. Frontiers in Neuroscience, 14 , 499. pp. 1-17. ISSN 1662-4548 [Article] (doi:10.3389/fnins.2020.00499)

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The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s.

Item Type: Article
Keywords (uncontrolled): Neuroscience, biomedical signal processing, deep brain stimulation (DBS), feature extraction, fuzzy control, Gaussian mixture models, support vector machine, Parkinson's disease, state estimator
Research Areas: A. > School of Science and Technology > Natural Sciences > Biophysics and Bioengineering group
Item ID: 30297
Notes on copyright: © 2020 Mohammed, Bayford and Demosthenous. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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Depositing User: Jisc Publications Router
Date Deposited: 02 Jun 2020 08:35
Last Modified: 02 Mar 2021 23:42
URI: https://eprints.mdx.ac.uk/id/eprint/30297

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