Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Abstract

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

Keywords: calibration; continuous glucose monitoring; diabetes; glucose sensors.

Figure 1

Representative three days of blood glucose (BG) monitoring obtained with self-monitoring BG (SMBG), diamonds, and with continuous glucose monitoring (CGM), continuous line. Horizontal dashed lines indicate the euglycemic range. Data taken from a previously published study [15].

Figure 2

Examples in which the continuous glucose monitoring (CGM) sensor output (continuous line) (a) overestimates and (b) underestimates the reference blood glucose (BG) (points). Data taken from a previously published study [15].

Figure 3

(a) Two-compartment model describing the blood glucose to interstitial glucose (BG-to-IG) kinetics. $R_a$ is the rate of appearance; $k_{01},k_{02},k_{12},k_{21}$ are rate constants. The time constant of the BG-to-IG system is $\tau =\frac{1}{k_{02}+k_{12}}$. (b) Representative blood glucose (BG) (dashed line) and interstitial glucose (IG) (continuous line) concentration profiles simulated as described in the text assuming τ = 11 min.

Figure 4

Representative raw CGM sensor signal (continuous line, units not specified by the manufacturer) that exhibits a nonphysiological drift (dashed line) due to the time-variability of sensor sensitivity. Data were previously published in [15].