Minimally-invasive continuous glucose monitoring (CGM) sensors are used in diabetes therapy to monitor interstitial glucose (IG) concentration almost continuously (e.g. every 5 min) and detect/predict dangerous hypo/hyperglycemic episodes. When compared with frequent blood glucose (BG) concentration references, CGM measurements are unavoidably affected by error. Models of the CGM error can be important in several applications, e.g. for testing in simulation the safety and effectiveness of CGM-based artificial pancreas algorithms. In this work, we model the error of the Dexcom G6, a CGM sensor that recently entered the market and does not require in vivo calibrations. The dataset includes CGM and BG data collected in 11 subjects wearing two Dexcom G6 sensors in parallel. The model is derived applying a methodology to dissect and model 3 main CGM error components: BG-to-IG kinetics, calibration error and measurement noise. An aspect of novelty of the method is its capability of handling factory-calibrated CGM sensor data. Results of model identification show that the time-variability of sensor calibration error during the sensor lifetime (10 days) can be well represented by a regression model with time-variant parameters described by 2-order polynomials in time.
