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This brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those solely reliant on glucose and insulin concentrations because they can accommodate other relevant influences like physical activity, stress and sleep.
Such factors also contribute to the accuracy of the adaptive control systems present in the artificial pancreas which is the focus of the brief, as their presence is indicated before they have an apparent effect on the glucose concentration and so can be more easily compensated. The adaptive controller is based on generalized predictive control techniques and also includes rules for changing controller parameters or structure based on the values of physiological variables. Simulation studies and clinical studies are reported to illustrate the performance of the techniques presented.
List of contents
Introduction.- Physiology and Factors Affecting Blood Glucose Concentration.- Components of an Artificial Pancreas.- Modeling Glucose Concentration Dynamics.- Hypoglycemia Alarm Systems.- Hyperglycemia Alarm Systems.- Various Control Philosophies and Algorithms.- Multivariable Control of Glucose Concentration.- Dual Hormone Techniques for AP Systems.- Integrated Hypo-/Hyperglycemia Alarm and Control Systems.- Future Developments.
About the author
Ali Cinar is Professor of Chemical Engineering at the Illinois Institute of Technology. His research concentrates on three areas: modeling, simulation and control of biomedical systems, complex adaptive agent-based systems, and supervision of process operations. His research activities focus on the development of theory, methods, and tools to use in these application areas. He is a Fellow of the AIChE and the author of books on batch fermentation and chemical process performance evaluation.
Kamuran Turksoy is a postdoctoral researcher in biomedical engineering at IIT. His research focuses on the development of hypoglycemia early alarm systems, multivariable adaptive control systems and process monitoring, performance assessment, and fault diagnosis techniques for risk mitigation in artificial pancreas systems. He has developed software to implement the methods and algorithms developed, and tested them in simulation and clinical studies.
Summary
This brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those solely reliant on glucose and insulin concentrations because they can accommodate other relevant influences like physical activity, stress and sleep.
Such factors also contribute to the accuracy of the adaptive control systems present in the artificial pancreas which is the focus of the brief, as their presence is indicated before they have an apparent effect on the glucose concentration and so can be more easily compensated. The adaptive controller is based on generalized predictive control techniques and also includes rules for changing controller parameters or structure based on the values of physiological variables. Simulation studies and clinical studies are reported to illustrate the performance of the techniques presented.
