Page 247 - Glucose Monitoring Devices

Conclusions 251

These equations were included in a glucose homeostasis simulator [24], imple-
mented in Matlab Simulink. An example is presented in Fig. 12.6, along with vali-
dation of the modeling by comparing the sensor error distributions (empirical and
from simulation), as well as the partial autocorrelation functions. No difference
2
between empirical and simulated characteristics was apparent. Moreover, a c test
showed that no significant difference exists between the observed and the simulated
distribution of sensor errors and the distribution of errors of the FreeStyle Navigator
(P >.46).

Conclusions
In this chapter, we presented a sequence of analytical techniques beginning with the
decomposition of CGM sensor errors into errors due to calibration, blood-to-inter-
stitial glucose transport, and additive noise, leading to computer simulation and
building of an in silico sensor based on diffusion and time series modeling. We
should emphasize that the term sensor “error,” although well accepted, may not
be entirely accurate because part of the “error” is due to physiological processes
that result in natural deviations of sensor readings from reference BG levels.
Regardless of terminology, sensor deviations from reference BG levels are gener-
ally explained by the specifics of CGM technologydthe measurement of glucose
takes place in the interstitial compartment, which is associated with, but different
from the blood. However, CGMs are calibrated against BG, and their accuracy is
assessed by comparing IG readings to reference BG. This alone creates two sources
of CGM errors, or deviations: calibration and the BG-to-IG gradient.
During analysis, the errors due to calibration can be separated by recalibrating
the sensor trace using all available reference BG readings, which is the first step
of sensor accuracy analysis. The differences between IG and BG are most evident
during rapid BG excursions: sensor errors tend to be negative (high CGM readings)
when the BG rate of change is negative and positive (low CGM readings) when the
BG rate of change is positive, which is potentially explained by the time lag between
the two compartments. This second component of sensor error due to the BG-to-IG
gradient can be evaluated by a diffusion modelda commonly accepted technique
explaining the possible physiological time lag between blood and IG concentration
[15e17]. After recalibration and accounting for the BG-to-IG gradient, the residual
sensor noise appears to be nonwhite (non-Gaussian) and the consecutive sensor er-
rors are highly interdependent. Thus the presented analysis of residual (after calibra-
tion and diffusion) sensor errors is based on time-series approaches using ARMA
noise to account for the interdependence between consecutive sensor errors. As an
example, we presented the autocorrelation and the distribution of the errors of the
FreeStyle Navigator.
Based on the presented sequence of mathematical models, the computer simu-
lation of sensor errors becomes possible.Suchaninsilico sensor includes both
generic deviations due to physiology and sensor-specific errors due to particular