Real-Time Improvement of Continuous Glucose-Monitoring Accuracy
The smart sensor concept
- Andrea Facchinetti, PHD1,
- Giovanni Sparacino, PHD1,
- Stefania Guerra, PHD1,
- Yoeri M. Luijf, MD2,
- J.Hans DeVries, MD, PHD2,
- Julia K. Mader, MD3,
- Martin Ellmerer, PHD3,
- Carsten Benesch, PHD4,
- Lutz Heinemann, PHD4,
- Daniela Bruttomesso, MD, PHD5,
- Angelo Avogaro, MD, PHD5,
- Claudio Cobelli, PHD1⇓,
- on behalf of the AP@home Consortium
- 1Department of Information Engineering, University of Padova, Padova, Italy
- 2Department of Internal Medicine, Academic Medical Centre, Amsterdam, the Netherlands
- 3Department of Internal Medicine, Medical University of Graz, Graz, Austria
- 4Profil Institute for Metabolic Research GmbH, Neuss, Germany
- 5Department of Clinical and Experimental Medicine, University of Padova, Padova, Italy
- Corresponding author: Claudio Cobelli, .
OBJECTIVE Reliability of continuous glucose-monitoring (CGM) sensors is key in several applications. In this work we demonstrate that real-time algorithms can render CGM sensors smarter by reducing their uncertainty and inaccuracy and improving their ability to alert for hypo- and hyperglycemic events.
RESEARCH DESIGN AND METHODS The smart CGM (sCGM) sensor concept consists of a commercial CGM sensor whose output enters three software modules, able to work in real time, for denoising, enhancement, and prediction. These three software modules were recently presented in the CGM literature and here we apply them to the Dexcom SEVEN Plus continuous glucose monitor. We assessed the performance of the sCGM on data collected in two trials, each containing 12 patients with type 1 diabetes.
RESULTS The denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time.
CONCLUSIONS We have introduced and implemented the sCGM sensor concept. Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications. This can be of great clinical impact for hypo- and hyperglycemic alert generation as well in artificial pancreas devices.
- Received April 18, 2012.
- Accepted September 5, 2012.
- © 2012 by the American Diabetes Association.
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