Page 171 - Glucose Monitoring Devices

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CHAPTER

Calibration of CGM

systems 9

Giada Acciaroli, PhD, Martina Vettoretti, PhD, Andrea Facchinetti, PhD,
Giovanni Sparacino, PhD
Department of Information Engineering, University of Padova, Padova, Italy
Minimally invasive continuous glucose monitoring (CGM) sensors measure a signal
that reﬂects glucose concentration only indirectly. Indeed, the wired-based sensor
placed through a needle in the subcutaneous tissue measures an electrical current
signal derived from the glucose-oxidase electrochemical reaction [1,2]. The calibra-
tion process consists in the estimation of a mathematical model that converts the
electrical current signal (given in fractions of ampere) into glucose concentration
values (in mg/dL). The parameters of the calibration model are usually estimated
by matching a few self-monitoring of blood glucose (SMBG) samples suitably
collected by the patient as reference measurements.
Most commercialized minimally invasive CGM systems perform the ﬁrst
calibration a few hours (e.g., one or two) after sensor insertion when the sensor
warm-up period has completed, and the subsequent ones every 12e24 h, usually
employing a simple ﬁrst-order time-independent linear model as the calibration
function [3e6]. Given the complex nonlinear and time-dependent relationship
between measured current and glucose concentration, the use of a simple linear
function as an approximation of the more complex behavior is acceptable within
time intervals of limited duration. Thus, frequent recalibrations are required to main-
tain sensor accuracy, as recommended by the manufacturers’ instructions [7e9].
Patients’ discomfort associated with the frequent calibration of the device, and the
need to improve CGM sensors’ accuracy and reliability called for the development
of more sophisticated calibration techniques. In the last decade, several signal
processing, modeling, and machine-learning methods have been proposed by the
academic community to address the calibration issue, which led to improvements
in CGM sensor accuracy and user acceptability. We refer the reader to
Refs. [10,11] for an extended discussion of CGM technologies and current trends
and to Ref. [12] for a comprehensive review of the calibration process.
In this chapter, after a formal description of the calibration problem, we will
illustrate some calibration techniques proposed in the literature for minimally
invasive CGM sensors. We will then present an example of the implementation of
a recently proposed calibration technique based on Bayesian estimation.

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