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

Abstract

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.

An Approach for Recommendations in Self Management of Diabetes based on Expert System

“…This expert system is feasible in diabetes self management and helps diabetes patients to make appropriate health related decisions in complex situations. Some expert systems were designed for diagnosis and treatment of diabetes [6,[16][17][18]. These expert systems did not include both type of diabetes (type 1 and 2) [16], diabetes complications (ex: proteinuria) [6,[16][17][18] blood glucose level in different situations (ex: pre exercise blood glucose) [6,[16][17][18] and diabetes associated diseases (ex: heart disease) [6,[16][17][18] in diabetes control.…”

“…Making appropriate decision in these situations needs knowledge about normal blood glucose levels and related signs and symptoms. An increasing number of software systems including web-based systems, knowledge-based expert systems and fuzzy expert systems are designed aiming at diabetes diagnosis and treatment [6][7][8][9][10][11][12][13][14][15][16][17]. Most of them can be employed in health care settings only [7][8][9][10][11][12].…”

“…At first experimental data were processed and the values were converted into fuzzy values (fuzzification stage).Then the fuzzy verdict mechanism executed rules to make a decision on the possibility of presence of diabetes [15]. Just a few expert systems have been designed incorporating nutrition in self management of diabetes [6,[16][17][18].Campos-Delgado et al in 2006 developed a Mamdani-type fuzzy-based system to present a blood glucose controller for type-1 diabetes patients under multiple daily insulin injections regimen [16].A diabetes self-management expert system, designed in 1992, resulted in significant improvement in knowledge and dietary habits of diabetes patients. It also showed significant reduction in HbA1c levels [17].…”

Hypoglycemia Prevention in Closed-Loop Artificial Pancreas for Patients with Type 1 Diabetes

“…Finally, the fuzzy controller output is obtained via defuzzification combining result back into a specific crisp control output value. Different fuzzy control schemes have been implemented in artificial pancreas studies (see for example Atlas et al (2010);Campos-Delgado et al (2006);Ibbini (2006); Ibbini & Massadeh (2005)). In Atlas et al (2010), a personalized fuzzy logic controller has been validated clinically, and proved to minimize hyperglycemic peaks while preventing hypoglycemia.…”

Robust strategies for glucose control in type 1 diabetes

“…In the last decade, many control algorithms have been proposed and tested in simulations (in silico) such as H ∞ [117,143,130], sliding mode control with glucose prediction after meals [54], neural networks and fuzzy logic [154,28], adaptive control structures [76] and algorithms inspired in the molecular biology of beta cells [113] but those with the best clinical evidence of efficacy are proportional-integral-derivative (PID) [153,103] controllers and model predictive control (MPC) [71]. term is based on the rate of change of blood glucose over time (error trend).…”