Authors

Lunze, K., Singh, T., Walter, M., Brendel, M. D., and Leonhardt, S.

Source

Biomedical Singla Processing and Control, 8(2).

Abstract

A method for optimal continuous insulin therapy for diabetes patients has been sought since the early 1970s. Although technical and medical advances have been made, a fully automated artificial pancreas to replace the functions of the natural organ is still a research aim. This review compares recent control algorithms for type 1 diabetic patients which automatically connect continuous glucose monitoring and insulin injection, without patient intervention. Black-box model and gray-box model based control strategies are described and their performances are evaluated, with a focus on their feasibility of implementation in a real-life situation. In conclusion, a satisfactory control strategy has not yet been proposed, mainly because most control algorithms rely on continuous blood glucose measurement which is not yet available. Modeling the effect of glucose ingestion as an external disturbance on the time evolution of blood glucose concentration, is now the norm for the control community. In contrast, the effects of physical activity on the metabolic system is not yet fully understood and remain an open issue. Moreover, clinical studies on evaluation of control performance are scarce. Therefore, research on blood glucose control needs to concentrate on advanced patient modeling, control optimization and control performance evaluation under realistic patient-oriented conditions.

@article{Lunze2012,
title = "Blood glucose control algorithms for type 1 diabetic patients: A methodological review",
journal = "Biomedical Signal Processing and Control",
volume = "8",
number = "2",
pages = "107- 119",
year = "2013",
note = "",
issn = "1746-8094",
doi = "10.1016/j.bspc.2012.09.003",
url = "http://www.sciencedirect.com/science/article/pii/S1746809412001061",
author = "Katrin Lunze and Tarunraj Singh and Marian Walter and Mathias D. Brendel and Steffen Leonhardt",
keywords = "Artificial pancreas",
keywords = "Blood glucose control",
keywords = "Model predictive control",
keywords = "Patient model",
keywords = "Insulin therapy devices"
}