Kalman-Based Calibration Algorithm for AgaMatrix Continuous Glucose Monitoring System

Abstract

A continuous glucose monitoring system is composed of a glucose sensor and an estimation (calibration) algorithm. The glucose sensor is composed of an electrode that is inserted under the skin and generates a noisy electrical current in response to interstitial glucose levels. The relationship between the electrical current and the interstitial glucose levels varies between individuals and within the same individual (with sensor wear time). The estimation algorithm infers the glucose levels in the blood from the noisy electrical current signal and uses intermittent capillary glucose measurements to account for intra-and inter-individual variability. In this article, we propose a novel real-time Kalman filter-based estimation algorithm that is composed of three steps: 1) noise filtering step; 2) compartment matching step using sequential Kalman filter; and 3) parameters estimation step using the conventional Kalman filter. The initial estimate of the parameters and covariance matrix are extracted offline using a nonlinear cubature Kalman filter. Our algorithm is compared with four alternative algorithms using 20 sensor data sets. Each data set was generated over a seven-day sensor wear period, during which patients were tested on three in-clinic days (days 1, 4, and 7). The comparison is based on the mean absolute relative difference (MARD) between frequent reference glucose measurements and the sensor glucose levels. MARD for days 1, 4, and 7 were 10.3%, 10.7%, and 11.9% for our algorithm and 12.46%, 15.75%, and 14.17% for the alternative algorithm, respectively. Our algorithm is currently being integrated into a commercial continuous glucose monitoring system (AgaMatrix, Inc., Salem, NH, USA).