Optimal estimation of blood glucose

Continuous glucose monitors based on subcutaneous sensors have the potential to improve regulation of blood glucose in individuals, while reducing the risk of hypoglycemia. In this paper we develop an optimal estimation-based approach to trade-off the probability that a sensed subcutaneous glucose change is due to measurement noise versus an actual blood glucose change. This estimator leads to significant improvement in the estimate of blood glucose, as well as its rate-of-change, compared to a finite-differences approach. This in turn allows a better future prediction of blood glucose, allowing the prediction/warning of impending hypoglycemia. This estimation-based approach can also be used to improve the performance of an automated feedback control system (artificial pancreas) to regulate blood glucose based on subcutaneous measurements.