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Title Prediction and Control of Blood Glucose in T1DM Patients
Author Hemmingsen, Christina
Johnsen, Charlotte Aagaard
Supervisor Jørgensen, John Bagterp (Scientific Computing, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Finan, Daniel Aaron (Scientific Computing, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2009
Abstract In this study, the possibility of predicting glucose levels in type 1 diabetes mellitus patients using simple black box models was investigated. More specifically, two types of linear models were developed: The autoregressive exogenous input (ARX) and autoregressive moving average exogenous input (ARMAX). When identified from simulated data from a prominent physiological model of type 1 diabetes, the models performed well, indicating that the dynamics are well captured by the simple linear models. Promising results were also obtained for reducing the quantitative meal information to binary information corresponding to a push-button, 'meal announcement', scenario. The models showed robustness to both errors in the meal information, and variation in insulin sensitivity levels. The latter was realized by manipulating parameters in the physiological model. Using clinical data obtained from type 1 diabetes subjects in ambulatory conditions, the potential use of activity as exogenous inputs was investigated. Both classical linear least squares identification as well as robust identification techniques such as Huber-regression were explored. None of these potential means of improvement gave any convincing results. Overall, the models produced reasonable predictions but (especially the ARMAX models) showed inconsistencies. However, based on the ambulatory nature and limited amount of the available clinical data, little could be concluded from the obtained results and the potential benefits need further exploration. The overall conclusion regarding the system identification results, was that an ARX model is the best candidate of the two for predicting glucose levels. Various model predictive control (MPC) algorithms using an ARX model as the internal model were investigated. The ARX model was identified using data from the physiological model, which also acted as the subject for the MPC. The degree of input information included for system identification and control gave rise to six different MPC cases. As a minimum, only insulin information was used and in addition to this, either binary or quantitative meal information was used. Despite the promising results from system identification, using binary meal information for MPC lead to inconsistent performance. The potential for using meal announcement instead of full meal knowledge was investigated and showed promising results. The cases that resulted in the best BG control, without requiring full meal knowledge for control, used either no meal information or a meal announcement 15 min in advance. Both of these control scenarios are feasible to implement in a closed-loop artificial pancreas device.
Series IMM-M.Sc.-2009-60
Fulltext
Original PDF ep09_60.pdf (3.77 MB)
Admin Creation date: 2009-09-21    Update date: 2009-09-21    Source: dtu    ID: 250261    Original MXD