A Study On Key Problems Of Blood Glucose Prediction Model In Artificial Pancreas For Type 1 Diabetes

An artificial pancreas system(APS) is an engineering approach to assisting patients with type 1 diabetes mellitus(T1DM) in maintaining glucose levels within a normal range. The APS consists of three components: a continuous glucose monitoring device to measure glucose concentrations at a regular interval; a control algorithm to compute appropriate rates of insulin infusions; and an insulin infusion device to deliver the computed insulin doses.Due to technical limitations, the existing APS can not satisfy the needs of blood glucose management for patients with T1 DM. The key problem to improve the performance of an APS is the control algorithm. Model predictive control is one of the most commonly used control algorithms. The performance of a model predictive controller for an APS depends highly on the accuracy of a blood glucose prediction model. Therefore, this thesis will focus on the utilization of blood glucose prediction models in APS controllers and the key problems faced by these models.These models can predict future glucose trends based on measured glucose values. According the principle of modeling, the commonly used models for glucose prediction in APS can be divided into two major groups, namely data-driven models and physiological models.Although data-driven models are generally employed as the glucose prediction models in the APS, their prediction accuracy varies due to inaccuracies of glucose sensing and system identification methods. In this thesis, we evaluate the effect of practical issues and identification methods on the time- series glucose prediction models. The simulation studies investigate the difference results among model types, and the clinical datasets evaluate the effect of parameters estimation methods on model identification.The glucose-insulin physiological models consist of a a series of mathematical functions which describe blood glucose metabolism in human body. Most of parameters in physiological models have actual physical meanings, so the influencing factors of physiological process, such as insulin sensitivity, etc., can be quantified in these models. Therefore, these physiological models have unique advantages to be applied to the design and deployment of an APS controller. In this thesis, we will attempt to overcome the obstacles of implementing these physiological models in the APS controller.1) The utilization of the sophisticated physiological models are confined bytheir numerous parameters and complex structure,2) The hardware implementation is another problem for the physiologicalmodels to be applied in the miniaturized and wearable APS.3) The glucose-insulin kinetics are affected by many factors including insulinsensitivity, which is the must-be-considered factor.Cobelli’s glucose-insulin model is the only computer simulation model of glucose-insulin interactions accepted by Food Drug Administration as a substitute to animal trials. In this thesis, Cobelli’s model is simplified by Padé approximant method and implemented on a field programmable gate array(FPGA) based platform as a hardware glucose prediction model. In addition, 24-hour profiles of the insulin sensitivity variation are simulated based on a diurnal pattern of insulin sensitivity.On the one hand, three model order reduction methods, namely Padé approximant, Routh approximant and system identification approximant, are used to obtain a simplified model that are suitable for the design of the APS controller. The results show that the proposed simplified model can describe the insulin-glucose metabolism process rather accurately as well as can be easily implemented. On the other hand, an entire design flow of hardware implementation for simplified model is proposed, and Cobelli’s model is implemented in an FPGA successfully. In addition, a diurnal pattern of insulin sensitivity variation for a whole day is also implemented using lookup tables in order to evaluate the effect of insulin sensitivity.In a word, this thesis discusses the potential problems of data-driven model applied to APS, and presents a method of model simplification and its hardware implementation for a complex physiological model. The successful hardware implementation of Cobelli’s model will promote a wider adoption of this model that can substitute animal trials, provide fast and reliable glucose and insulin estimation, and ultimately assist the further development of an artificial pancreas system.