Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept

Abstract

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.

Figures

Figure 1

The sCGM sensor architecture comprises a commercial CGM sensor (black block) and three software modules for denoising, enhancement, and prediction applied in cascade and working in real time. The denoising module receives in input CGM data and returns in output a smoother CGM profile. The enhancement module receives in input the smoothed CGM data and returns in output more accurate CGM data. Finally, the prediction module receives in input denoised and enhanced CGM data and returns in output the prediction of future glucose value, on which “preventive” hypo- and hyperglycemic alerts can be generated.

Figure 2

Examples of the application of three modules for the sCGM sensor in three representative subjects. A: The denoised output of the sCGM sensor (black line) is compared with raw CGM data (gray line). B: The enhanced output of the sCGM sensor (black line) and raw CGM data (gray line) is compared with reference BG values (gray circles). C: The real-time prediction (black line) obtained from the sCGM output (gray line), the alerts generated by crossing the hypoglycemic threshold (black and gray arrows, respectively) and the temporal gain in forecasting these events thanks to prediction are shown. Note that the time scales on the x-axis of the three panels are different.