Name
Title | ||
|---|---|---|
Online Denoising Method to Handle Intraindividual Variability of Signal-to-Noise Ratio in Continuous Glucose Monitoring |
Abstract | ||
|---|---|---|
In the last decade, the availability of new minimally invasive subcutaneous sensors for monitoring glucose level continuously stimulated research on new online strategies for improving the treatment of diabetes, including hyper/hypoglycemic alert generators and artificial pancreas. An important aspect that has to be dealt with in these applications is the random measurement noise that affects continuous glucose monitoring (CGM) signals. One major difficulty is that for a given sensor technology, the signal-to-noise ratio (SNR) can vary from subject to subject (interindividual variability) and also within subject (intraindividual variability). Recently, a denoising approach implemented through a Kalman filter with parameters automatically tuned, once for all, in a burn-in interval was proposed to cope with the interindividual variability of SNR. In this paper, we propose a new denoising method able to cope also with the intraindividual variability of the SNR. The method resorts to a Bayesian smoothing procedure that uses a statistically-based criterion to determine, and continuously update, filter parameters in real time. The performance of the method is assessed on both Monte Carlo simulation and 24 real CGM time series obtained with the Glucoday system (Menarini, Florence, Italy). The method has a general applicability, also outside from the CGM context. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TBME.2011.2161083 | Biomedical Engineering, IEEE Transactions |
Keywords | Field | DocType |
Bayes methods,Kalman filters,Monte Carlo methods,biochemistry,diseases,medical signal processing,signal denoising,smoothing methods,sugar,time series,Bayesian smoothing procedure,CGM time series,Glucoday system,Kalman filter,Monte Carlo simulation,SNR,continuous glucose monitoring signals,filter parameters,intraindividual variability,online denoising method,signal-to-noise ratio,Alert,diabetes,digital filtering,time series | Noise reduction,Time series,Digital filter,Noise measurement,Computer science,Artificial intelligence,Computer vision,Pattern recognition,Signal-to-noise ratio,Speech recognition,Kalman filter,Smoothing,Bayesian probability | Journal |
Volume | Issue | ISSN |
58 | 9 | 0018-9294 |
Citations | PageRank | References |
12 | 1.72 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Facchinetti | 1 | 152 | 28.83 |
| Giovanni Sparacino | 2 | 276 | 52.52 |
| Claudio Cobelli | 3 | 658 | 113.31 |