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...
| Autors principals: | , , , |
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| Format: | Conference item |
| Idioma: | English |
| Publicat: |
IEEE
2024
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| Sumari: | 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. |
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