TY - GEN

T1 - Tuning of Controller for Type 1 Diabetes Treatment with Stochastic Differential Equations

A1 - Duun-Henriksen,Anne Katrine

A1 - Boiroux,Dimitri

A1 - Schmidt,Signe

A1 - Skyggebjerg,Ole

A1 - Madsbad,Sten

A1 - Jensen,Peter Ruhdal

A1 - Jørgensen,John Bagterp

A1 - Poulsen,Niels Kjølstad

A1 - Nørgaard,Kirsten

A1 - Madsen,Henrik

AU - Duun-Henriksen,Anne Katrine

AU - Boiroux,Dimitri

AU - Schmidt,Signe

AU - Skyggebjerg,Ole

AU - Madsbad,Sten

AU - Jensen,Peter Ruhdal

AU - Jørgensen,John Bagterp

AU - Poulsen,Niels Kjølstad

AU - Nørgaard,Kirsten

AU - Madsen,Henrik

PY - 2012

Y1 - 2012

N2 - People with type 1 diabetes need several insulin injections every day to keep their blood glucose level in the normal range and thereby avoiding the acute and long term complications of diabetes. One of the recent treatments consists of a pump injecting insulin into the subcutaneous layer combined with a continuous glucose monitor (CGM) frequently observing the glucose level. Automatic control of the insulin pump based on CGM observations would ease the burden of constant diabetes treatment and management. We have developed a controller designed to keep the blood glucose level in the normal range by adjusting the size of insulin infusions from the pump based on model predictive control (MPC). A clinical pilot study to test the performance of the MPC controller overnight was performed. The conclusion was that the controller relied too much on the local trend of the blood glucose level which is a problem due to the noise corrupted observations from the CGM. In this paper we present a method to estimate the optimal Kalman gain in the controller based on stochastic differential equation modeling. With this model type we could estimate the process noise and observation noise separately based on data from the rst clinical pilot study. In doing so we obtained a more robust control algorithm which is less sensitive to <br/>fluctuations in the CGM observations and rely more on the global physiological trend of the blood glucose level. Finally, we present the promising results from the second pilot study testing the improved controller.

AB - People with type 1 diabetes need several insulin injections every day to keep their blood glucose level in the normal range and thereby avoiding the acute and long term complications of diabetes. One of the recent treatments consists of a pump injecting insulin into the subcutaneous layer combined with a continuous glucose monitor (CGM) frequently observing the glucose level. Automatic control of the insulin pump based on CGM observations would ease the burden of constant diabetes treatment and management. We have developed a controller designed to keep the blood glucose level in the normal range by adjusting the size of insulin infusions from the pump based on model predictive control (MPC). A clinical pilot study to test the performance of the MPC controller overnight was performed. The conclusion was that the controller relied too much on the local trend of the blood glucose level which is a problem due to the noise corrupted observations from the CGM. In this paper we present a method to estimate the optimal Kalman gain in the controller based on stochastic differential equation modeling. With this model type we could estimate the process noise and observation noise separately based on data from the rst clinical pilot study. In doing so we obtained a more robust control algorithm which is less sensitive to <br/>fluctuations in the CGM observations and rely more on the global physiological trend of the blood glucose level. Finally, we present the promising results from the second pilot study testing the improved controller.

BT - Proceedings of the 8th IFAC Symposium on Biological and Medical Systems

T2 - Proceedings of the 8th IFAC Symposium on Biological and Medical Systems

ER -