Paper Info
Title
Modeling the Glucose Sensor Error
Abstract
Continuous glucose monitoring (CGM) sensors are portable devices, employed in the treatment of diabetes, able to measure glucose concentration in the interstitium almost continuously for several days. However, CGM sensors are not as accurate as standard blood glucose (BG) meters. Studies comparing CGM versus BG demonstrated that CGM is affected by distortion due to diffusion processes and by time-varying systematic under/overestimations due to calibrations and sensor drifts. In addition, measurement noise is also present in CGM data. A reliable model of the different components of CGM inaccuracy with respect to BG (briefly, “sensor error”) is important in several applications, e.g., design of optimal digital filters for denoising of CGM data, real-time glucose prediction, insulin dosing, and artificial pancreas control algorithms. The aim of this paper is to propose an approach to describe CGM sensor error by exploiting n multiple simultaneous CGM recordings. The model of sensor error description includes a model of blood-to-interstitial glucose diffusion process, a linear time-varying model to account for calibration and sensor drift-in-time, and an autoregressive model to describe the additive measurement noise. Model orders and parameters are identified from the n simultaneous CGM sensor recordings and BG references. While the model is applicable to any CGM sensor, here, it is used on a database of 36 datasets of type 1 diabetic adults in which n = 4 Dexcom SEVEN Plus CGM time series and frequent BG references were available simultaneously. Results demonstrates that multiple simultaneous sensor data and proper modeling allow dissecting the sensor error into its different components, distinguishing those related to physiology from those related to technology.
Year
DOI
Venue
2014
10.1109/TBME.2013.2284023
Biomedical Engineering, IEEE Transactions
Keywords
Field
DocType
autoregressive processes,biochemistry,biodiffusion,biomedical equipment,blood,calibration,chemical sensors,noise measurement,patient treatment,sugar,time series,CGM data denoising,CGM sensor error,Dexcom SEVEN Plus CGM time series,artificial pancreas control algorithms,autoregressive model,blood-to-interstitial glucose diffusion,calibrations,continuous glucose monitoring sensors,diabetes treatment,diabetic adults,glucose concentration measurement,glucose sensor error,insulin dosing,linear time-varying model,multiple simultaneous CGM sensor recordings,noise measurement,optimal digital filter design,physiology,portable devices,real-time glucose prediction,sensor error description,time-varying systematic under-overestimations,Continuous glucose monitoring,diabetes,measurement noise,parameter estimation,sensor calibration
Noise reduction,Artificial pancreas,Autoregressive model,Digital filter,Noise measurement,Computer science,Electronic engineering,Estimation theory,Distortion,Calibration
Journal
Volume
Issue
ISSN
61
3
0018-9294
Citations 
PageRank 
References 
4
0.65
0
Authors
7
Name
Order
Citations
PageRank
Andrea Facchinetti115228.83
Simone Del Favero26715.81
Giovanni Sparacino327652.52
Jessica R Castle4183.54
W Kenneth Ward5668.46
Claudio Cobelli6658113.31
Del Favero, S.7102.24