Enhanced Accuracy of Continuous Glucose Monitoring by Online Extended Kalman Filtering

Abstract

Most continuous glucose monitoring (CGM) devices measure a current, proportional to the interstitial glucose (IG) concentration, which is converted into a glucose level by a standard device calibration step that exploits some blood glucose (BG) references. However, data show that deterioration of sensor gain may occur, which can affect CGM output by a systematic and possibly large (e.g., up to 15/20 mg/dL) error. Enhanced calibration algorithms for improving the accuracy of CGM are thus of critical importance, especially in real-time applications. In this work we present an enhanced Bayesian calibration method that can be implemented online by using the Extended Kalman Filter. The method takes into account the existence of BG-to-IG kinetics by incorporating a population convolution model and exploits only four BG reference samples per day. The new method is successfully applied on 10 simulated virtual patients. Its performance in improving the accuracy of CGM profiles is significantly better than that of other current calibration procedures. Furthermore, the new method is shown to be robust to changes in its parameters. Improvement in the accuracy of CGM is also shown on a representative subject. Realistic simulations show that the new enhanced calibration method significantly improves the accuracy of CGM signals, suggesting potential benefits by its inclusion in real-time applications of CGM devices.