A Dual-Rate Kalman Filter for Continuous Glucose Monitoring

Abstract

A dual-rate Kalman filter is developed for realtime continuous glucose monitoring. Frequent (5 minute) sampling of a noisy, continuous glucose sensor is used for estimation of glucose and its rate-of-change. Infrequent (8 hour intervals) reference glucose meter samples enable the sensor gain and its rate-of-change to be updated. The dual-rate Kalman filter formulation accounts for uncertainty in both the continuous glucose sensor and the reference glucose meter. The method is tested on simulated and experimental data, confirming its superiority to simple one-point calibration.

Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

“…Based on use of a discrete dual-rate Kalman filter, a similar approach was used by Bequette and colleagues 33 and Kuure-Kinsey colleagues. 34 The next section provides a tutorial overview of Kalman filtering.…”

Use of Subcutaneous Interstitial Fluid Glucose to Estimate Blood Glucose: Revisiting Delay and Sensor Offset

“…For example, the Kalman filter by Facchinetti and associates 8 adapts to changes in the sensor calibration as does a version of the Kalman filter proposed by Kuure-Kinsey and coworkers. 31 A third Kalman filter, proposed by Knobbe and Buckingham 6 (not simulated here), includes components that adapt to both changes in sensitivity and changes in time delay. None of the Kalman designs reviewed here, however, attempted to adapt to different or time-varying changes in OS.…”

Hypoglycemia Detection in Critical Care Using Continuous Glucose Monitors: Anin SilicoProof of Concept Analysis

“…Without correction, calibration drift will show up as though the actual BG measurements were higher or lower in a relatively consistent manner as the sensor gain drifts. 40 This drift would cause the alarm to trigger early (possibly falsely) or late. However, such calibration drift is very much a function of the frequency and quality of calibration measurements, which can likely be controlled more readily in a critical care setting.…”

“…More advanced filtering techniques, such as adaptive filtering and Kalman filtering, have been shown to produce very good results. 39,40 Kalman filtering can also be used to correct for calibration drift, 40 which was not studied in this case. However, it is difficult to compare performance here as all studies have used different data with different noise or error distributions from the sensors.…”