e-journal
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 dynamicmodelisproposed.Thenoveltyconsistsintheadaptationof data normalization parameters in real time to estimate and compensatepatient’ssensitivityvariations.Adaptationisperformedto minimizemeanabsoluterelativedeviationatthecalibrationpoints withatimewindowforgettingstrategy.Fourcalibrationsareused: preprandial and 1.5 h postprandial at two different meals. Two databasesareusedforvalidation:1)a9-hCGMSGold(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 sinceitcounteractssensorsensitivityvariations.Thisimprovement is more evident in one-week simulations.
Index Terms—Artificial pancreas, calibration algorithm (CA), CGMS accuracy, type 1 diabetes.
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