Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

References

• Aström, K.J., Hagander, P., & Sternby, J. (1984). Zeros of sampled systems. Automatica, 20, 31–38.
   Web of Science ®Google Scholar
• Bergamasco, M., & Lovera, M. (2010, July). Continuous-time subspace identification in closed-loop. Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems MTNS (pp. 1359–1365). Budapest, Hungary.
   Google Scholar
• Bergman, R., Phillips, L., & Cobelli, C. (1981). Physiologic evaluation of factors controlling glucose tolerance in man: Measurement of insulin sensitivity and beta-cell sensitivity from the response to intravenous glucose. Journal of Clinical Investigation, 68, 1456–1467.
   PubMed Web of Science ®Google Scholar
• Boiroux, D., Duun-Henriksen, A., Schmidt, S., Norgaard, K., Madsbad, S., Skyggebjerg, O., ...Jorgensen, J. (2012, August). Overnight control of blood glucose in people with type 1 diabetes. Proceedings of the 8th IFAC Symposium on Biological and Medical Systems (pp. 73–78). Budapest, Hungary.
   Google Scholar
• Breton, M., & Kovatchev, B. (2008). Analysis, modeling and simulation of the accuracy of continuous glucose sensors. Journal of Diabetes Science and Technology, 2, 853–862.
   PubMedGoogle Scholar
• Brouns, F., Bjork, I., Frayn, K., Gibbs, A., Lang, V., Slama, G., & Wolever, T. (2005). Glycaemic index methodology. Nutrition Research Review, 18, 145–171.
   PubMed Web of Science ®Google Scholar
• Cescon, M., Ståhl, F., Landin-Olsson, M., & Johansson, R. (2009, July). Subspace-based model identification of diabetic blood glucose dynamics. Proceedings of the 15th IFAC Symposium on System Identification (SYSID2009). Saint Malo, France.
   Google Scholar
• Cescon, M., Stemmann, M., & Johansson, R. (2012, October). Impulsive predictive control of T1DM glycemia: An in-silico study. 2012 ASME Dynamic Systems and Control Conference. Fort Lauderdale, FL.
   Google Scholar
• Chiuso, A. (2007). The role of vector auto regressive modeling in predictor based subspace identification. Automatica, 43, 1034–1048.
   Web of Science ®Google Scholar
• Chiuso, A. (2010). On the asymptotic properties of closed-loop CCA-type subspace algorithms: Equivalence results and role of the future horizon. IEEE Transactions on Automatic Control, 55, 624–649.
   Web of Science ®Google Scholar
• Chiuso, A., & Picci, G. (2005). Consistency analysis of certain closed-loop subspace identification methods. Automatica, 41, 377–391.
   Web of Science ®Google Scholar
• Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., & Kovatchev, B. (2009). Diabetes: Models, signals and control. IEEE Reviews in Biomedical Engineering, 2, 54–96.
   PubMedGoogle Scholar
• Cobelli, C., Renard, E., & Kovatchev, B. (2011). Artificial pancreas: Past, present, future. Diabetes, 60, 2672–2682.
   PubMed Web of Science ®Google Scholar
• Dalla Man, C., Rizza, R.R., & 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
• De Nicolao, G., Magni, L., Dalla Man, C., & Cobelli, C. (2011, September). Modeling and control of diabetes: Towards the artificial pancreas. Proceedings of the 18th IFAC World Congress (IFAC2011) (pp. 7092–7101). Milano, Italy.
   Google Scholar
• Dexcom. (2011). Seven plus Retrieved from http://www.dexcom.com/seven-plus
   Google Scholar
• DIAdvisor. (2008). The DIAdvisor™ Retrieved from http://www.diadvisor.eu
   Google Scholar
• Elleri, D., Allen, J., Harris, J., Kumareswaran, K., Nodale, M., Leelarathna, L., ...Hovorka, R. (2013). Absorption patterns of meals containing complex carbohydrates in type 1 diabetes. Diabetologia, 56, 1108–1117.
   PubMed Web of Science ®Google Scholar
• Finan, D., Jorgensen, J., Poulsen, N., & Madsen, H. (2010, July). Robust model identification applied to type 1 diabetes. Proceedings of the 2010 American Control Conference (ACC2010) (pp. 2021–2026). Baltimore, MD.
   Google Scholar
• Finan, D., Palerm, C., Doyle, III, J., & Seborg, D. (2009). Effect of input excitation on the quality of empirical dynamic modes for type 1 diabetes. Process Systems Engineering, 55, 1135–1146.
   Google Scholar
• Golub, G., & Van Loan, C. (1996). Matrix computations. Baltimore, MD: John Hopkins University Press.
   Google Scholar
• Hemocue. (2011). The HemoCue glucose analyzer. Retrieved from http://www.hemocue.com
   Google Scholar
• Horowitz, M., O’Donovan, D., Jones, K., Feinle, C., Rayner, C., & Samsom, M. (2002). Gastric emptying in diabetes: Clinical significance and treatment. Diabetic Medicine, 19, 177–194.
   PubMed Web of Science ®Google Scholar
• Johansson, R. (1994). Identification of continuous-time models. IEEE Transactions on Signal Processing, 4, 887–897.
   Web of Science ®Google Scholar
• Johansson, R., Verhaegen, M., & Chou, C.T. (1999). Stochastic theory of continuous-time state-space identification. IEEE Transactions on Signal Processing, 47, 41–51.
   Web of Science ®Google Scholar
• Kirchsteiger, H., Castillo Estrada, G., Pölzer, S., del Re, L., & Renard, E. (2011, September). Estimating interval process models for type 1 diabetes for robust control design. Proceedings of the 18th IFAC World Congress (IFAC2011) (pp. 11761–11766). Milano, Italy.
   Google Scholar
• Kirchsteiger, H., Pölzer, S., Johansson, R., Renard, E., & del Re, L. (2011, December). Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes. Proceedings of the 50th Conference on Decision and Control and European Control Conference (CDC-ECC2011) (pp. 5176–5181). Orlando, FL.
   Google Scholar
• Klein, R., Klein, B., Moss, S., Davis, M., & DeMets, D. (1988). Glycosylated hemoglobin predicts the incidence and progression of diabetic retinopathy. Journal of the American Medical Association, 260, 2864–2871.
   PubMed Web of Science ®Google Scholar
• Knudsen, T. (2001). Consistency analysis of subspace identification methods based on a linear regression approach. Automatica, 37, 81–89.
   Web of Science ®Google Scholar
• Ljung, L. (1999). System identification: Theory for the user. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Nathan, D. (1993). Long-term complications of diabetes mellitus. New England Journal of Medicine, 328, 1676–1685.
   PubMed Web of Science ®Google Scholar
• Percival, M., Bevier, W., Wang, Y., Dassau, E., Zisser, H., Jovanovic, L., & Doyle, F. (2010). Modeling the effects of subcutaneous insulin administration and carbohydrate consumption on blood glucose. Journal of Diabetes Science and Technology, 4, 1214–1228.
   PubMedGoogle Scholar
• Peternell, K. (1995). Identification of linear dynamic systems by subspace and realization-based algorithms (PhD thesis). Vienna University of Technology.
   Google Scholar
• Poulsen, J., Avogaro, A., Chauchard, F., Cobelli, C., Johansson, R., Nita, L., ...Soendergaard, J. (2010, September). A diabetes management system empowering patients to reach optimised glucose control: From monitor to advisor. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2010) (pp. 5270–5271). Buenos Aires, Argentina.
   Google Scholar
• Schmidt, S., Finan, D., Duun-Henriksen, A., Jorgensen, J., Madsen, H., Bengtsson, H., ...Norgaard, K. (2012). Effects of everyday life events on glucose, insulin, and glucagon dynamics in continuous subcutaneous insulin infusion-treated type 1 diabetes: Collection of clinical data for glucose modeling. Diabetes Technology and Therapeutics, 14, 210–217.
   PubMed Web of Science ®Google Scholar
• Ståhl, F. (2012). Diabetes mellitus glucose prediction by linear and Bayesian ensemble modeling (Licentiate thesis TFRT--3255--SE). Department of Automatic Control, Lund University, Sweden.
   Google Scholar
• Ståhl, F., & Johansson, R. (2009). Diabetes mellitus modeling and short-term prediction based on blood glucose measurements. Mathematical Biosciences, 217, 101–117.
   PubMed Web of Science ®Google Scholar
• The American Diabetes Association. (2010). Standards of medical care in diabetes 2010. Diabetes Care, 33, S11–S61.
   Google Scholar
• The Artificial Pancreas Project. (2011). Juvenile diabetes research foundation (JDRF) Retrieved from http://www.artificialpancreasproject.com/
   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
• The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Research Group. (2005). Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. New England Journal of Medicine, 353, 2643–2653.
   PubMed Web of Science ®Google Scholar
• The DIAdvisor Consortium. (2012). Final publishable summary report. Retrieved from http://cordis.europa.eu/results/
   Google Scholar
• van Heudsen, K., Dassau, E., Zisser, H., Seborg, D., & Doyle, III, J.F. (2012). Control-relevant models for glucose control using a priori patient characteristics. IEEE Transactions on Biomedical Engineering, 59, 1839–1849.
   PubMed Web of Science ®Google Scholar
• Wilinska, M.E., Chassin, L.J., Schaller, H.C., Schaupp, L., Pieber, T.R., & Hovorka, R. (2005). Insulin kinetics in type-1 diabetes: Continuous and bolus delivery of rapid acting insulin. IEEE Transactions on Biomedical Engineering, 52, 3–12.
   PubMed Web of Science ®Google Scholar
• Williams, G., & Pickup, J.C. (1999). Handbook of diabetes, MSD. Oxford: Blackwell Science.
   Google Scholar
• Yellow Spring Instruments. (2013). YSI 2300 STAT Plus^TM glucose analyzer. Retrieved from http://www.ysilifesciences.com/index.php?page=ysi-2300-stat-plus-glucose-lactate-analyzer.
   Google Scholar