Blood Pressure, Automatic Control of

Yu, Yih‐Choung

doi:10.1002/0471732877.emd039

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…However, SNP remains a useful case study since a mathematical model of the dose-effect relationship exists and has been clinically validated [3]. A variety of approaches to blood pressure control by closed-loop SNP infusion have been proposed over the past three decades (see [4] for a review). While the potential benefits of such a technology remain undisputed, however, it is notable that only one of these designs ever reached commercial production in the early 1990s, and that the resulting device enjoyed such little success that it was discontinued soon after being marketed [5], despite seemingly promising results in premarket clinical trials [6].…”

Section: Introductionmentioning

confidence: 99%

A Robust Multiple-Model Adaptive Control System for Automatic Delivery of a Vasoactive Drug

Malagutti

Dehghani²,

Kennedy³

2012

Biomedical Engineering / 765: Telehealth / 766: Assistive Technologies

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Automatic closed-loop administration of sodium nitroprusside for the regulation of blood pressure in patients experiencing acute hypertension has been the subject of intense research over the last three decades. Yet, to date, manual administration of vasoactive drugs by a human operator remains the standard of care in the clinical setting. This manuscript describes a novel control approach for this application based on Robust Multiple-Model Adaptive Control (RMMAC). The RMMAC architecture features robust controllers designed with µ synthesis and Kalman filtering for system estimation. The new system was coupled with a mathematical model of a patient's response to drug infusion and tested in computational simulations. The results indicate that the RMMAC approach has the potential to deliver robust performance even in challenging operating conditions, with mean arterial pressure remaining within the specified target range over 99% of the time.

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Section: Introductionmentioning

confidence: 99%

A Robust Multiple-Model Adaptive Control System for Automatic Delivery of a Vasoactive Drug

Malagutti

Dehghani²,

Kennedy³

2012

Biomedical Engineering / 765: Telehealth / 766: Assistive Technologies

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Real-time Projected Gradient-based Nonlinear Model Predictive Control with an Application to Anesthesia Control

Hall¹,

Ortmann²,

Picallo³

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Medical drug infusion problems pose a combination of challenges such as nonlinearities from physiological models, model uncertainty due to inter-and intra-patient variability, as well as strict safety specifications. With these challenges in mind, we propose a novel real-time Nonlinear Model Predictive Control (NMPC) scheme based on projected gradient descent iterations. At each iteration, a small number of steps along the gradient of the NMPC cost is taken, generating a suboptimal input which asymptotically converges to the optimal input. We retrieve classical Lyapunov stability guarantees by performing a sufficient number of gradient iterations until fulfilling a stopping criteria. Such a real-time control approach allows for higher sampling rates and faster feedback from the system which is advantageous for the class of highly variable and uncertain drug infusion problems. To demonstrate the controller's potential, we apply it to hypnosis control in anesthesia of two interacting drugs. The controller successfully regulates hypnosis even under disturbances and uncertainty and fulfils benchmark performance criteria.

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An approach to controlled drug infusion via tracking of the time-varying dose-response

Malaguttiy

Dehghaniz

Kennedyy

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Automatic administration of medicinal drugs has the potential of delivering benefits over manual practices in terms of reduced costs and improved patient outcomes. Safe and successful substitution of a human operator with a computer algorithm relies, however, on the robustness of the control methodology, the design of which depends, in turn, on available knowledge about the underlying dose-response model. Real-time estimation of a patient's actual response would ensure that the most suitable control algorithm is adopted, but the potentially time-varying nature of model parameters and the limited number of observation signals may cause the estimation problem to be ill-posed, posing a challenge to adaptive control methods. We propose the use of Bayesian inference through a particle filtering approach as a way to overcome these limitations and improve the robustness of automatic drug administration methods. We report on the results of a simulation study modeling the infusion of vasodepressor drug sodium nitroprusside for the control of mean arterial pressure in acute hypertensive patients. The proposed control architecture was able to meet the required performance objectives under challenging operating conditions.

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Blood Pressure, Automatic Control of

Cited by 3 publications

References 28 publications

A Robust Multiple-Model Adaptive Control System for Automatic Delivery of a Vasoactive Drug

A Robust Multiple-Model Adaptive Control System for Automatic Delivery of a Vasoactive Drug

Real-time Projected Gradient-based Nonlinear Model Predictive Control with an Application to Anesthesia Control

An approach to controlled drug infusion via tracking of the time-varying dose-response

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