Page 178 - Glucose Monitoring Devices

180 CHAPTER 9 Calibration of CGM systems

samples per day. The calibration process should be able to guarantee accurate
glucose estimations in this data-poor scenario.
Several techniques have been proposed to deal with these issues affecting CGM
sensor calibration. The next section presents and discusses the most recent
algorithms proposed in the literature.

State-of-art calibration algorithms and today’s challenges
Simple heuristic to deal with the BG-IG system

One of the major limitations of the calibration linear regression techniques presented
in Section Problem statement is that they all neglect the time lag between the BG
and the raw sensor signal, which can lead to a suboptimal estimation of the param-
eters of the calibration function. Therefore, most of the calibration algorithms devel-
oped by the scientific community included more or less sophisticated approaches to
overcome this limitation and take BG-to-IG dynamics into account. The first simple
approach is to require calibration of the sensor when glucose is relatively stable. This
approach can be applied to any calibration algorithm. The rationale of this heuristic
is that, in such a condition, BG and IG concentrations should be at equilibrium and,
thus, the estimation of the linear regression parameters should not be influenced by
not considering the BG-to-IG dynamics [27]. Following this rationale, Aussedat
et al. [28] developed an automated algorithm that requests sensor calibration only
when a window of stable signal is detected, that is, when the sensor signal has not
changed by more than 1% over a 4-min window, and when the raw current value
for the second calibration point differs from the first by 2 nA. The study proved
that performing calibrations during periods of relative glucose stability minimizes
the difference between BG references and raw sensor measurements due to the
BG-to-IG kinetics.

Kalman filter-based approaches

More sophisticated model-based approaches to account for the BG-to-IG dynamics
have been developed relying on Kalman filter theory. In particular, Knobbe et al.
[29] proposed a five-state extended Kalman filter, which estimates subcutaneous
glucose levels, BG levels, time lag between the sensor measured subcutaneous
glucose and BG, time-rate-of-change of the BG level, and the subcutaneous glucose
sensor scale factor [30]. In this study, BG levels are reconstructed in continuous time
from CGM measurements, employing a state-space Bayesian framework with a
priori knowledge of unknown variables. A direct application of a Kalman filter to
improve CGM sensor accuracy was proposed by Kuure-Kinsey and colleagues
[31], employing a dual-rate Kalman filter and exploiting sparse SMBG measure-
ments to estimate the sensor sensitivity in real time. Although designed for
real-time glucose and its rate of change estimation, the algorithm does not account