Subspace Identification of a Glucose-Insulin model Using Meal Tracer Protocol Measurements

In this study, the problem of identifying a low complexity state space model describing glucose and insulin dynamics from low sample meal tracer experiments is investigated. Triple tracer meal protocol measurements (sampled as low as 15 samples per meal) together with continuous glucose monitoring measurements, measured concurrently at a rate of 5 minutes per sample, are used. A new formulation to estimate the missing input and output measurements at such low sample rates is developed. Nuclear norm minimization is used to exploit low rankness of the stacked input and output matrix, while the {ell1} norm is used to exploit an available sparse basis for the glucose flux profiles. Simulation results, using the UVa Padova simulator, show that the technique outperforms previous methods and also demonstrate the possibility of identifying state space models from triple tracer measurements with good prediction performance under non-ideal conditions

Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters; (2) the vector space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates; and (6) an estimate of the initial conditions. Three glucose fluxes are then estimated, namely; glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization; and other important state variables. The simulation results show that the technique is capable of estimating the glucose fluxes with high accuracy, even for complex meal scenarios. The experimental results indicate that the technique is capable of reproducing the triple tracer measurements for three T1DM undergoing the triple tracer protocol while estimating the missing measurements for a certain model parameter selection