Conference item
Model predictive control for closed-loop deep brain stimulation
- Abstract:
- This paper describes a model predictive control (MPC) algorithm for Deep Brain Stimulation (DBS) implants that are used to treat common movement disorders. DBS is currently used in clinical practice in open-loop with constant stimulation, which shortens the effective lifespan of the treatment and can lead to unpleasant side-effects. The goal of closed-loop control is to alleviate symptoms with minimal stimulation. The controller is based on a model of the amplitude of beta-band (13-30 Hz) oscillations of population-level neural activity at the site of the implant, which is a bio-marker related to the presence of symptoms of Parkinson’s Disease. We present a two-stage approach in which a dynamic model for bio-marker activity is identified from data after applying a linearizing transformation, followed by a regulation stage using the identified model together with a model of response to stimulation based on average patient data. A Kalman filter is used to estimate the state of both the stimulation response and the nominal beta activity. The controller is compared to thresholded on/off (bang-bang) and proportional-integral (PI) feedback controllers, which are the most advanced form of control tested in vivo to date. Simulations demonstrate reductions in control input for similar levels of tracking error.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 332.5KB, Terms of use)
-
- Publisher copy:
- 10.1109/CDC56724.2024.10885888
Authors
- Publisher:
- IEEE
- Host title:
- 2024 IEEE 63rd Conference on Decision and Control (CDC)
- Pages:
- 4034-4039
- Publication date:
- 2025-02-26
- Acceptance date:
- 2024-08-27
- Event title:
- 63rd IEEE Conference on Decision and Control (CDC 2024)
- Event location:
- Allianz MiCo, Milan Convention Centre, Italy
- Event website:
- https://cdc2024.ieeecss.org/
- Event start date:
- 2024-12-16
- Event end date:
- 2024-12-19
- DOI:
- EISSN:
-
2576-2370
- ISSN:
-
0743-1546
- EISBN:
- 9798350316339
- ISBN:
- 9798350316346
- Language:
-
English
- Keywords:
- Pubs id:
-
2024488
- Local pid:
-
pubs:2024488
- Deposit date:
-
2024-08-31
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2024
- Rights statement:
- © 2024 IEEE
- Notes:
- This paper was presented at the 63rd IEEE Conference on Decision and Control (CDC 2024), Allianz MiCo, Milan Convention Centre, Italy, 16th-19th December 2024. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://dx.doi.org/10.1109/CDC56724.2024.10885888
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