Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring

Abstract

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations

This work was supported in part by the Spanish Ministry of Science and Innovation under Project DPI2010-20764-C02 and in part by the European Union under Grant FP7-PEOPLE-2009-IEF, Ref 252085. The work of F. Barcelo-Rico was supported by the Spanish Ministry of Education (FPU AP2008-02967)