Fuzzy-Based Controller for Glucose Regulation in Type-1 Diabetic Patients by Subcutaneous Route

This paper presents an advisory/control algorithm for a type-1 diabetes mellitus (TIDM) patient under an intensive insulin treatment based on a multiple daily injections regimen (MDIR). The advisory/control algorithm incorporates expert knowledge about the treatment of this disease by using Mamdani-type fuzzy logic controllers to regulate the blood glucose level (BGL). The overall control strategy is based on a two-loop feedback strategy to overcome the variability in the glucose-insulin dynamics from patient to patient. An inner-loop provides the amount of both rapid/short and intermediate/long acting insulin (RSAI and ILAI) formulations that are programmed in a three-shots daily basis before meals. The combined preparation is then injected by the patient through a subcutaneous route. Meanwhile, an outer-loop adjusts the maximum amounts of insulin provided to the patient in a time-scale of days. The outer-loop controller aims to work as a supervisor of the inner-loop controller. Extensive closed-loop simulations are illustrated, using a detailed compartmental model of the insulin-glucose dynamics in a TIDM patient with meal intake

[1]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[2]  R. Bellazzi,et al.  The subcutaneous route to insulin-dependent diabetes therapy. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[3]  R. Meier,et al.  Fuzzy logic control of human blood pressure during anesthesia , 1992, IEEE Control Systems.

[4]  A. Schiffrin,et al.  Computer-assisted Insulin Dosage Adjustment , 1985, Diabetes Care.

[5]  S. Andreassen,et al.  Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks , 1999, IEEE Transactions on Biomedical Engineering.

[6]  E. Ruiz-Velazquez,et al.  Knowledge-based controllers for blood glucose regulation in Type I diabetic patients by subcutaneous route , 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.

[7]  Gade Pandu Rangaiah,et al.  Robust PID controller for blood glucose regulation in type I diabetics , 2004 .

[8]  L. Duckstein,et al.  Fuzzy classification of patient state with application to electrodiagnosis of peripheral polyneuropathy , 1995, IEEE Transactions on Biomedical Engineering.

[9]  Norman Fleischer,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. , 1993 .

[10]  Giuseppe De Nicolao,et al.  Adaptive controllers for intelligent monitoring , 1995, Artif. Intell. Medicine.

[11]  Ricardo Femat,et al.  Self-tuning insulin adjustment algorithm for type 1 diabetic patients based on multi-doses regime , 2005 .

[12]  P. Brunetti,et al.  Long-term intensive treatment of type 1 diabetes with the short-acting insulin analog lispro in variable combination with NPH insulin at mealtime. , 1999, Diabetes care.

[13]  Francis J. Doyle,et al.  Robust H∞ glucose control in diabetes using a physiological model , 2000 .

[14]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[15]  John Thomas Sorensen,et al.  A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes , 1985 .

[16]  J. Skyler,et al.  Algorithms for Adjustment of Insulin Dosage by Patients Who Monitor Blood Glucose , 1981, Diabetes Care.

[17]  H. Lebovitz,et al.  Therapy for Diabetes Mellitus and Related Disorders , 1994 .

[18]  N. Cano,et al.  Bench-to-bedside review: Glucose production from the kidney , 2002, Critical care.

[19]  Marcelo Simoes Introduction to Fuzzy Control , 2003 .

[20]  T. Deutsch,et al.  Compartmental models for glycaemic prediction and decision-support in clinical diabetes care: promise and reality. , 1998, Computer methods and programs in biomedicine.

[21]  Ricardo Femat,et al.  Blood glucose control for type I diabetes mellitus: A robust tracking H∞ problem , 2004 .

[22]  E.R. Carson,et al.  A spectrum of approaches for controlling diabetes , 1992, IEEE Control Systems.

[23]  Standards of Medical Care for Patients With Diabetes Mellitus , 1998, Diabetes Care.

[24]  J. Radziuk,et al.  An adaptive plasma glucose controller based on a nonlinear insulin/glucose model , 1994, IEEE Transactions on Biomedical Engineering.

[25]  D Rodbard,et al.  Computer Simulation of Plasma Insulin and Glucose Dynamics After Subcutaneous Insulin Injection , 1989, Diabetes Care.

[26]  Volker Tresp,et al.  Neural-network models for the blood glucose metabolism of a diabetic , 1999, IEEE Trans. Neural Networks.

[27]  Moshe Phillip,et al.  Comparison of continuous subcutaneous insulin infusion and multiple daily injection regimens in children with type 1 diabetes: a randomized open crossover trial. , 2003, Pediatrics.

[28]  A. M. Zbinden,et al.  Fuzzy Logic Control of Blood Pressure during Anesthesia , 2022 .

[29]  T. Shimauchi,et al.  Microcomputer-aided insulin dose determination in intensified conventional insulin therapy , 1988, IEEE Transactions on Biomedical Engineering.

[30]  T. Hara,et al.  Improved diabetes control by using ‘close adjustment algorithms’ , 2004, Pediatrics international : official journal of the Japan Pediatric Society.

[31]  Michael Reinfrank,et al.  An introduction to fuzzy control (2nd ed.) , 1996 .

[32]  R. B. Tattersall,et al.  Home blood glucose monitoring , 1979, Diabetologia.

[33]  Malgorzata E. Wilinska,et al.  Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin , 2005, IEEE Transactions on Biomedical Engineering.

[34]  P. Raskin,et al.  Improved Glycemic Control in Intensively Treated Type 1 Diabetic Patients Using Blood Glucose Meters With Storage Capability and Computer-Assisted Analyses , 1998, Diabetes Care.

[35]  D. Goldstein,et al.  Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial. , 2002, Diabetes care.

[36]  Jean-Christophe Buisson,et al.  Balancing meals using fuzzy arithmetic and heuristic search algorithms , 2003, IEEE Trans. Fuzzy Syst..

[37]  E R Carson,et al.  Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes. , 1998, Computer methods and programs in biomedicine.

[38]  E D Lehmann,et al.  A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. , 1992, Journal of biomedical engineering.

[39]  W. Haddad,et al.  Drug dosing control in clinical pharmacology , 2005, IEEE Control Systems.

[40]  D. Lehmann,et al.  The freeware AIDA interactive educational diabetes simulator , 2001 .