Model predictive control for closed-loop deep brain stimulation
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 ca...
| Główni autorzy: | , , , |
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| Format: | Conference item |
| Język: | English |
| Wydane: |
IEEE
2024
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| _version_ | 1826314451156992000 |
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| author | Steffen, S Cannon, M Tan, H Debarros, J |
| author_facet | Steffen, S Cannon, M Tan, H Debarros, J |
| author_sort | Steffen, S |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-09-25T04:32:40Z |
| format | Conference item |
| id | oxford-uuid:e3db8412-2e13-422c-a3fb-92b4e6996fa0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:32:40Z |
| publishDate | 2024 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:e3db8412-2e13-422c-a3fb-92b4e6996fa02024-09-02T12:34:20ZModel predictive control for closed-loop deep brain stimulationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:e3db8412-2e13-422c-a3fb-92b4e6996fa0EnglishSymplectic ElementsIEEE2024Steffen, SCannon, MTan, HDebarros, JThis 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. |
| spellingShingle | Steffen, S Cannon, M Tan, H Debarros, J Model predictive control for closed-loop deep brain stimulation |
| title | Model predictive control for closed-loop deep brain stimulation |
| title_full | Model predictive control for closed-loop deep brain stimulation |
| title_fullStr | Model predictive control for closed-loop deep brain stimulation |
| title_full_unstemmed | Model predictive control for closed-loop deep brain stimulation |
| title_short | Model predictive control for closed-loop deep brain stimulation |
| title_sort | model predictive control for closed loop deep brain stimulation |
| work_keys_str_mv | AT steffens modelpredictivecontrolforclosedloopdeepbrainstimulation AT cannonm modelpredictivecontrolforclosedloopdeepbrainstimulation AT tanh modelpredictivecontrolforclosedloopdeepbrainstimulation AT debarrosj modelpredictivecontrolforclosedloopdeepbrainstimulation |