Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Zavitsanou, Stamatina; Chakrabarty, Ankush; Dassau, Eyal; Doyle, Francis J.

doi:10.3390/pr4040035

Cited by 27 publications

(25 citation statements)

References 137 publications

(150 reference statements)

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“…Therefore, these approaches have found use in manufacturing and medical devices. Additional improvements in the quality of closed-loop drug delivery and adherence within society has been fueled by the invention of wearable sensors [16], minimally invasive actuators, and embedded decision-making platforms [17], along with novel drug delivery mechanisms [18] or input-output pairs [19]. This paper demonstrates how selecting control input (from light-based methods to small-molecule pharmaceuticals) or controller design parameters plays an essential role in resetting the phase of mammalian circadian rhythms.…”

Section: Introductionmentioning

confidence: 99%

Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control

et al. 2019

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The widespread adoption of closed-loop control in systems biology has resulted from improvements in sensors, computing, actuation, and the discovery of alternative sites of targeted drug delivery. Most control algorithms for circadian phase resetting exploit light inputs. However, recently identified small-molecule pharmaceuticals offer advantages in terms of invasiveness and potency of actuation. Herein, we develop a systematic method to control the phase of biological oscillations motivated by the recently identified small molecule circadian pharmaceutical KL001. The model-based control architecture exploits an infinitesimal parametric phase response curve (ipPRC) that is used to predict the effect of control inputs on future phase trajectories of the oscillator. The continuous time optimal control policy is first derived for phase resetting, based on the ipPRC and Pontryagin's maximum principle. Owing to practical challenges in implementing a continuous time optimal control policy, we investigate the effect of implementing the continuous time policy in a sampled time format. Specifically, we provide bounds on the errors incurred by the physiologically tractable sampled time control law. We use these results to select directions of resetting (i.e. phase advance or delay), sampling intervals, and prediction horizons for a nonlinear model predictive control (MPC) algorithm for phase resetting. The potential of this ipPRCinformed pharmaceutical nonlinear MPC is then demonstrated in silico using real-world scenarios of jet lag or rotating shift work.

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Section: Introductionmentioning

confidence: 99%

Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control

et al. 2019

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“…Other applications, depicted in the lower row, include computational algorithms embedded in a medical device or serving as the medical device, i.e., software as a medical device ( 4 ). An example of the former is embedded control algorithms in glucose monitors, which have the potential for advancing modern artificial pancreas systems 10 used in glucose regulation for patients with diabetes ( 5 ). The models for the artificial pancreas have been used to replace in vivo animal studies to initiate clinical studies for these closed-loop devices ( 6 ).…”

Section: Overview Of Computational Modeling For Medical Devicesmentioning

confidence: 99%

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

et al. 2018

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Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

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“…Fuzzy control is found in numerous applications for closed loop therapy (Zarkogianni et al, 2011;Soltesz et al, 2013;Zavitsanou et al, 2016). It has the potential to achieve a level of expertise close to (and possibly better than) human expertise in therapy modulation (Barro and Marin, 2002).…”

Section: Introductionmentioning

confidence: 99%

A Framework for Adapting Deep Brain Stimulation Using Parkinsonian State Estimates

2020

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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.

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Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Cited by 27 publications

References 137 publications

Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control

Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

A Framework for Adapting Deep Brain Stimulation Using Parkinsonian State Estimates

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