Advances in Artificial Pancreas Systems / SpringerBriefs in Bioengineering (PDF)
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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.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.
- Autoren: Ali Cinar , Kamuran Turksoy
- 2018, 1st ed. 2018, 119 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 331972245X
- ISBN-13: 9783319722450
- Erscheinungsdatum: 01.03.2018
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