NDLI: Bihormonal model predictive control of blood glucose in people with type 1 diabetes

Content Provider	IEEE Xplore Digital Library
Author	Batora, V. Tarnik, M. Murgas, J. Schmidt, S. Norgaard, K. Poulsen, N.K. Madsen, H. Jorgensen, J.B.
Copyright Year	2014
Description	Author affiliation: Dept. of Endocrinology, Hvidovre Hosp., Denmark (Schmidt, S.; Norgaard, K.) \|\| Fac. of Electr. Eng. & Inf. Technol., Slovak Univ. of Technol., Bratislava, Slovakia (Batora, V.; Tarnik, M.; Murgas, J.) \|\| Dept. of Appl. Math. & Comput. Sci., Tech. Univ. of Denmark, Lyngby, Denmark (Poulsen, N.K.; Madsen, H.; Jorgensen, J.B.)
Abstract	In this paper we present a bihormonal control system that controls blood glucose in people with type 1 diabetes (T1D). We use insulin together with glucagon to mitigate the negative effects of hyper- and hypoglycemia. The system consists of a Kalman filter, a micro-bolus insulin and glucagon infusion MPC, a mealtime bolus calculator and a CGM providing feedback to the controller. The controller employs a patient data-based prediction model with ARMAX structure. We test the controller using a bihormonal model with time-varying parameters for 3 subjects and compare its performance to a system with an identical insulin MPC, but a glucagon PD controller. The key contribution of the bihormonal MPC is the efficiency of glucagon use. We consider scenarios where the meals are estimated correctly or overestimated and where the insulin sensitivity increases. Both solutions provide tight glucose control. According to the simulations, the bihormonal MPC requires on average 30% less glucagon than the system with a PD controller.
Starting Page	1693
Ending Page	1698
File Size	392140
Page Count	6
File Format	PDF
ISBN	9781479974092
DOI	10.1109/CCA.2014.6981556
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Insulin Sugar Predictive models Computational modeling PD control Kalman filters
Content Type	Text
Resource Type	Article

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