Kalman Smoothing for Objective and Automatic Preprocessing of Glucose Data

A method for preprocessing a time series of glucose measurements based on Kalman smoothing is presented. Given a glucose data time series that may be irregularly sampled, the method outputs an interpolated time series of glucose estimates with mean and variance. The method can provide homogenization of glucose data collected from different devices by using separate measurement noise parameters for differing glucose measurement equipment. We establish a link between the ISO 15197 standard and the measurement noise variance used by the Kalman smoother for self-monitoring of blood glucose (SMBG) measurements. The method provides phaseless smoothing, and it can automatically correct errors in the original datasets like small fallouts and erroneous readings when surrounding data allow. The estimated variance can be used for deciding at which times the data are trustworthy. The method can be used as a preprocessing step in many kinds of glucose data processing and analysis tasks, such as computing the mean absolute relative deviation between measurement systems or estimating the plasma-to-interstitial fluid glucose dynamics of continuous glucose monitor or flash glucose monitor (FGM) signals. The method is demonstrated on SMBG and FGM glucose data from a clinical study. A MATLAB implementation of the method is publicly available.