Probabilistic evolving meal detection and estimation of meal total glucose appearance.
BACKGROUND
Automatic compensation of meals for type 1 diabetes patients will require meal detection from continuous glucose monitor (CGM) readings. This is challenged by the uncertainty and variability inherent to the digestion process and glucose dynamics as well as the lag and noise associated with CGM sensors. Thus any estimation of meal start time, size, and shape is fundamentally uncertain. This uncertainty can be reduced, but not eliminated, by estimating total glucose appearance and using new readings as they become available.


METHODS
In this article, we propose a probabilistic, evolving method to detect the presence and estimate the shape and total glucose appearance of a meal. The method is unique in continually evolving its estimates and simultaneously providing uncertainty measures to monitor their convergence. The algorithm operates in three phases. First, it compares the CGM signal to no-meal predictions made by a simple insulin-glucose model. Second, it fits the residuals to potential, assumed meal shapes. Finally, it compares and combines these fits to detect any meals and estimate the meal total glucose appearance, shape, and total glucose appearance uncertainty.


RESULTS
We validate the performance of this meal detection and total glucose appearance estimation algorithm both separately and in cooperation with a controller on the Food and Drug Administration-approved University of Virginia/Padova Type I Diabetes Simulator. In cooperation with a controller, the algorithm reduced the mean blood glucose from 137 to 132 mg/dl over 1.5 days of control without any increased hypoglycemia.


CONCLUSIONS
This novel, extensible meal detection and total glucose appearance estimation method shows the feasibility, relevance, and performance of evolving estimates with explicit uncertainty measures for use in closed-loop control of type 1 diabetes.
Cameron
Fraser
F
Department of Aeronautics and Astronautics, Stanford University, Stanford, California 94305, USA. fraser@stanford.edu
Niemeyer
Günter
G
Buckingham
Bruce A
BA
D000465:Algorithms; D001786:Blood Glucose / Q000187:drug effects / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000175:diagnosis / Q000188:drug therapy; D019719:Diagnostic Equipment; D004040:Dietary Carbohydrates / Q000008:administration & dosage / Q000378:metabolism*; D005240:Feasibility Studies; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D007328:Insulin / Q000008:administration & dosage; D007332:Insulin Infusion Systems; D008954:Models, Biological; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011336:Probability; D015203:Reproducibility of Results; D013997:Time Factors; D035501:Uncertainty
D001786:Blood Glucose; D004040:Dietary Carbohydrates; D007004:Hypoglycemic Agents; D007328:Insulin