Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

References

• Balandat, M. (2010). Constrained robust optimal trajectory tracking: Model predictive control approaches (Diploma thesis). Technische Universität Darmstadt, Darmstadt, Germany.
   Google Scholar
• Boiroux, D., Schmidt, S., Frøssing, L., Nørgaard, K., Sten, M., Ole, S., … Jørgensen, J.B. (2012). Control of blood glucose for people with type 1 diabetes: An in-vivo study. Proceedings of the 17th Nordic Process Control Workshop (pp. 133–140). Kgs. Lyngby, Denmark.
   Google Scholar
• Cengiz, E., & Tamborlane, W.V. (2009). A tale of two compartments: Interstitial versus blood glucose monitoring. Diabetes Technology & Therapeutics, 11(S1), S11–S16.
   PubMedGoogle Scholar
• Dalla Man, C., Guerra, S., Sparacino, G., Renard, E., & Cobelli, C. (2010). A reduced type 1 diabetes model for model predictive control. Poster presented at the 3rd International Conference on Advanced Technologies and Treatments for Diabetes (ATTD), Basel, Switzerland.
   Google Scholar
• Dalla Man, C., Rizza, R.A., & Cobelli, C. (2007). Meal simulation model of the glucose-insulin system. IEEE Transactions on Biomedical Engineering, 54, 1740–1749.
   PubMed Web of Science ®Google Scholar
• Doyle, F., Jovanovic, L., & Seborg, D. (2007). Glucose control strategies for treating type 1 diabetes mellitus. Journal of Process Control, 17, 572–576.
   Google Scholar
• Fabietti, P.F., Canonico, V., Federici, M.O., Benedetti, M.M., & Sarti, E. (2006). Control oriented model of insulin and glucose dynamics in type 1 diabetics. Medical and Biological Engineering and Computing, 44, 69–78.
   PubMed Web of Science ®Google Scholar
• Fabietti, P.F., Canonico, V., Orsini-Federiei, M., Sarti, E., & Massi-Benedetti, M. (2007). Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes. Journal of Diabetes Technology and Therapeutics, 9, 327–338.
   PubMed Web of Science ®Google Scholar
• Finan, D.A., Palerm, C.C., Doyle III., F.J., Seborg, D.E., Zisser, H., Bevier, W.C., & Jovanovic, L. (2009). Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes. American Institute of Chemical Engineers Journal, 55(5), 1135–1146.
   Web of Science ®Google Scholar
• Gani, A., Gribok, A.V., Rajaraman, S., Ward, W.K., & Reifman, J. (2009). Predicting subcutaneous glucose concentration in humans: Data-driven glucose modeling. IEEE Transactions on Biomedical Engineering, 56(2), 246–254.
   PubMed Web of Science ®Google Scholar
• Garcia Jaramillo, M.A. (2011). Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients ( PhD thesis). University of Girona, Department of Electrical Engineering, Electronics and Automation, Girona, Spain.
   Google Scholar
• Garnier, H., & Wang, L. (Eds.). (2008). Identification of continuous time models from sampled data. London: Springer.
   Google Scholar
• Harvey, R., Youqing, W., Grosman, B., Percival, M., Bevier, W., Finan, D., … Dassau, E. (2010). Quest for the artificial pancreas: Combining technology with treatment. IEEE Engineering in Medicine and Biology Magazine, 29, 53–62.
   PubMed Web of Science ®Google Scholar
• Hovorka, R. (2005). Management of diabetes using adaptive control. International Journal of Adaptive Control and Signal Processing, 19, 309–325.
   Web of Science ®Google Scholar
• Johansson, R. (1993). System modeling and identification. Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Johansson, R. (1994). Identification of continuous-time models. IEEE Transactions on Signal Processing, 42, 887–879.
   Web of Science ®Google Scholar
• Johansson, R. (2010, July 5–9). Continuous-time model identification and state estimation using non-uniformly sampled data. Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS2010) (pp. 347–354). Budapest, Hungary.
   Google Scholar
• Kirchsteiger, H., Castillo Estrada, G., Pölzer, S., Renard, E., & Del Re, L. (2011, August 28 to September 2). Estimating interval process models for type 1 diabetes for robust control design. Proceedings of the 18th IFAC World Congress(pp. 11761–11766). Milano, Italy.
   Google Scholar
• Kirchsteiger, H., Pölzer, S., Johansson, R., Renard, E., & del Re, L. (2011). Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes. Proceedings of the 49th IEEE Conference on Decision and Control (CDC) (pp. 5176–5181). Orlando, FL.
   Google Scholar
• Kovatchev, B., Cobelli, C., Renard, E., Anderson, S., Breton, M., Patek, S., … Farret, A. (2010). Multinational study of subcutaneous model-predictive closed-loop control in type 1 diabetes mellitus: Summary of the results. Journal of Diabetes Science and Technology, 4(6), 1374–1381.
   PubMedGoogle Scholar
• Kovatchev, B., Heinemann, L., Anderson, S., & Clarke, W. (2008). Comparison of the numerical and clinical accuracy of four continuous glucose monitors. Diabetes Care, 31(6), 1160–1164.
   PubMed Web of Science ®Google Scholar
• Kuhn, E., & Lavielle, M. (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 49(4), 1020–1038.
   Web of Science ®Google Scholar
• Mayne, D.Q., Seron, M.M., & Raković, S.V. (2005). Robust model predictive control of constrained linear systems with bounded uncertainties. Automatica, 41, 219–224.
   Web of Science ®Google Scholar
• Milanese, M., Norton, J., Piet-Lahanier, H., & Walter, E. (Eds.). (1996). Bounding approaches to system identification. Berlin: Springer.
   Google Scholar
• Naumova, V., Pereverzyev, S.V., & Sampath, S. (2012). A meta-learning approach to the regulatrized learning – case study: Blood glucose prediction. Neural Networks, 33, 181–193.
   PubMed Web of Science ®Google Scholar
• Niedzwiecki, M. (2002). Identification of time-varying processes. Chichester: Wiley.
   Google Scholar
• Norton, J. (1987). Identification and application of bounded-parameter models. Automatica, 23(4), 497–507.
   Web of Science ®Google Scholar
• Obermaier, K., Schmelzeisen-Redeker, G., Schoemaker, M., Klötzer, H.M., Kirchsteiger, H., Eikmeier, H., & del Re, L. (2013). Performance evaluations of continuous glucose monitoring systems: Precision absolute relative deviation is part of the assessment. Journal of Diabetes Science and Technology, 7(4), 824–832.
   PubMedGoogle Scholar
• Parker, R., Gatzke, E., & Doye III, F. (2000). Advanced model predictive control (MPC) for type I diabetic patient blood glucose control. Proceedings of the American Control Conference (ACC) (Vol. 5, pp. 3483–3487). Chicago, IL.
   Google Scholar
• Söderström, T., & Stoica, P. (1989). System identification. Hemel Hempstead: Prentice Hall International.
   Google Scholar
• Stork, A.D.M., Kemperman, H., Erkelens, D.W., & Veneman, T.F. (2005). Comparison of the accuracy of the hemocue glucose analyzer with the Yellow Springs Instrument glucose oxidase analyzer, particularly in hypoglycemia. European Journal of Endocrinology, 153(2), 275–281.
   PubMed Web of Science ®Google Scholar
• Sung, S.W., & Lee, I.B. (2001). Prediction error identification method for continuous-time processes with time delay. Industrial and Engineering Chemistry Research, 40(24), 5743–5751.
   Web of Science ®Google Scholar
• The Diabetes Control and Complications Trial Research Group. (1993), The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New England Journal of Medicine, 329, 977–986.
   PubMed Web of Science ®Google Scholar
• van Heusden, K., Dassau, E., Zisser, H.C., Seborg, D.E., & Doyle III, F.J. (2012). Control-relevant models for glucose control using a priori patient characteristics. IEEE Transactions on Biomedical Engineering, 59(7), 1839–1849.
   PubMed Web of Science ®Google Scholar