Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An adaptive plasma glucose controller based on a nonlinear insulin/glucose model

B Candas1, J Radziuk

  • 1Diabetes and Metabolism Research Unit, Ottawa Civic Hospital, Ontario.

IEEE Transactions on Bio-Medical Engineering
|February 1, 1994
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hereditary colorectal cancer registries in Canada: report from the Colorectal Cancer Association of Canada consensus meeting; Montreal, Quebec; October 28, 2011.

Current oncology (Toronto, Ont.)·2013
Same author

Algorithms can be used to identify fragility fracture cases in physician-claims databases.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2011
Same author

Bioavailability and metabolism of oral and percutaneous dehydroepiandrosterone in postmenopausal women.

The Journal of steroid biochemistry and molecular biology·2007
Same author

Diurnal rhythm in endogenous glucose production is a major contributor to fasting hyperglycaemia in type 2 diabetes. Suprachiasmatic deficit or limit cycle behaviour?

Diabetologia·2006
Same author

Mild acute renal failure potentiates metformin accumulation in the diabetic rat kidney without further impairment of renal function.

Diabetes & metabolism·2003
Same author

Quantitation of basal endogenous glucose production in Type II diabetes: importance of the volume of distribution.

Diabetologia·2002

This article presents a new computer-based system designed to manage blood sugar levels more effectively by using a mathematical model that accounts for the complex, non-linear way the body processes insulin. By constantly adjusting its predictions based on real-time data, the system can better handle rapid changes in insulin levels, such as those occurring after injections or infusions. Tested in animal models, the controller successfully kept blood sugar near target levels even when insulin delivery changed quickly. This approach offers a flexible way to monitor glucose, potentially reducing the need for constant, frequent blood testing while maintaining stable control.

Area of Science:

  • Biomedical engineering and adaptive plasma glucose controller systems
  • Mathematical modeling in endocrinology and metabolic physiology

Background:

Traditional methods for managing blood sugar often depend on linear mathematical frameworks that may fail to capture complex physiological dynamics. That uncertainty drove the development of more sophisticated approaches to represent the insulin-glucose regulatory system. Prior research has shown that linear models struggle to predict glucose removal accurately across diverse insulin signal patterns. This gap motivated the exploration of non-linear modeling techniques to improve control precision. No prior work had fully resolved the challenge of adapting to rapid, large-scale fluctuations in insulin action. Researchers have long sought methods that provide reliable performance without requiring constant, high-frequency data sampling. Existing systems frequently face limitations when dealing with the inherent noise present in biological measurements. This study addresses these constraints by integrating a non-linear model into an adaptive control architecture.

Purpose Of The Study:

Keywords:
glycemic managementrecursive estimationmetabolic regulationin vivo testing

Frequently Asked Questions

The researchers propose a two-step procedure where measured glucose levels and exogenous infusion rates estimate past removal rates. These rates are then expressed as a weighted sum of previous insulin inputs and removal values, adjusted on-line via recursive estimation to predict future insulin-dependent glucose clearance.

The authors utilize a discretized non-linear model of the insulin/glucose regulatory system. This component incorporates a prefiltering technique specifically designed to mitigate the effects of corrupting coloured process noise during the recursive parameter estimation phase.

The researchers state that prefiltering is necessary because the estimation process must account for corrupting coloured process noise. This technical requirement ensures that the recursive adjustment of parameters remains accurate despite the presence of interference in the measured biological signals.

Related Experiment Videos

The aim of this study is to design an adaptive plasma glucose controller based on a non-linear insulin/glucose model. This research addresses the limitations of traditional linear approaches in managing blood sugar levels. The authors seek to implement a model that predicts insulin-dependent glucose removal more reliably. By utilizing a non-linear framework, the study intends to provide better control over a wide spectrum of insulin signals. The motivation stems from the need to handle rapid and large variations in insulin action effectively. Researchers also aim to demonstrate that such a controller can function with flexible monitoring intervals. This work explores the integration of recursive estimation to adjust model parameters in real-time. Ultimately, the study evaluates whether this adaptive system can maintain glucose levels near a target set point during diverse insulin delivery conditions.

Main Methods:

Review Approach framing involves evaluating a non-linear model integrated into an adaptive control architecture for glucose regulation. The design utilizes a discretized mathematical representation to facilitate a two-step estimation procedure. Investigators implemented a recursive method to adjust parameters on-line during the operation of the system. The approach incorporates data prefiltering to address the presence of corrupting coloured process noise. Researchers tested the performance of this controller in vivo using three porcine subjects. The experimental protocol involved various intravenous and subcutaneous rapid insulin injections to challenge the system. Additionally, the team utilized staircase infusions to assess the response to changing insulin levels. Sampling intervals were dynamically varied from two minutes during transient phases to seven minutes at steady states.

Main Results:

Key Findings From the Literature indicate that the controller maintains plasma glucose at an average level of 99.9 percent of the target value. The coefficient of variation for this performance metric was measured at 8.7 percent. The system demonstrates a prompt reaction to large and rapid variations in insulin action. Experimental results confirm the efficacy of the controller across both intravenous and subcutaneous delivery routes. The researchers observed that control quality improves as the number of glucose measurements increases. The model successfully predicts the time course of the insulin-dependent fractional rate of glucose removal. Data show that the system remains effective even when sampling frequency is reduced during steady-state conditions. These results support the feasibility of using non-linear adaptive control for managing glucose levels in real-time.

Conclusions:

The authors propose that their non-linear adaptive framework effectively maintains blood sugar near target values across various insulin delivery scenarios. Synthesis and implications suggest that this approach provides a robust alternative to traditional linear control strategies. The researchers report that the system reacts promptly to significant and rapid shifts in insulin activity. Evidence indicates that the controller maintains glucose at an average level of 99.9 percent of the target value. The findings imply that predicting glucose removal rates allows for greater flexibility in monitoring intervals. The study demonstrates that performance remains stable even when sampling frequencies are adjusted based on the physiological state. The authors conclude that their recursive estimation method successfully accounts for process noise during real-time operation. This work highlights the potential for improved glycemic management through adaptive, model-based control strategies.

The system uses measured plasma glucose levels and exogenous glucose infusion rates as the primary data types. These inputs allow the controller to estimate past removal rates and subsequently predict the time course of the insulin-dependent fractional rate of glucose removal.

The controller was tested in vivo using three pigs. The researchers measured performance by maintaining plasma glucose at an average of 99.9 +/- 8.7% of the target value during various intravenous or subcutaneous insulin injections and staircase infusions.

The authors propose that their prediction of glucose removal allows for flexibility in monitoring intervals. They claim this reduces the burden of frequent sampling, as the system maintains control even when intervals vary from two minutes during transients to seven minutes at steady states.