Estimation of future glucose concentrations with subject-specific recursive linear models

Abstract

Background: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data.

Methods: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-/intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA).

Results: Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more.

Conclusions: Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic/hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.

FIG. 1.

Prediction of blood glucose with time-invariant models for a representative (a) healthy subject, (b) glucose-intolerant subject, and (c) subject with type 2 diabetes for Group A with a PH of two time steps.

FIG. 2.

Glucose prediction results with the recursive algorithm for a representative (a) healthy subject and (b) subject with type 2 diabetes for Group B.

FIG. 3.

Variation in model parameters for a representative (a) healthy subject and (b) subject with type 2 diabetes for Group B. Predicted glucose concentrations are for a PH of six time steps. Representative subjects are the same as in Figure 2.