Abstract
In diabetes research, non-invasive continuous glucose monitoring (NI-CGM) devices represent a new and appealing frontier. In the last years, some multi-sensor devices for NI-CGM have been proposed, which exploit several sensors measuring phenomena of different nature, not only for measuring glucose related signals, but also signals reflecting some possible perturbing processes (temperature, blood perfusion). Estimation of glucose levels is then obtained combining these signals through a mathematical model which requires an initial calibration step exploiting one reference blood glucose (RBG) sample. Even if promising results have been obtained, especially in hospitalized volunteers, at present the temporal accuracy of NI-CGM sensors may suffer because of environmental and physiological interferences. The aim of this work is to develop a general methodology, based on Monte Carlo (MC) simulation, to assess the robustness of the calibration step used by NI-CGM devices against these disturbances. The proposed methodology is illustrated considering two examples: the first concerns the possible detrimental influence of sweat events, while the second deals with calibration scheduling. For implementing both examples, 45 datasets collected by the Solianis Multisensor system are considered. In the first example, the MC methodology suggests that no further calibration adjustments are needed after the occurrence of sweat events, because the “Multisensor+model” system is able to deal with the disturbance. The second case study shows how to identify the best time interval to update the model's calibration for improving the accuracy of the estimated glucose. The methodology proposed in this work is of general applicability and can be helpful in making those incremental steps in NI-CGM devices development needed to further improve their performance.
Highlights
Diabetes consists in a chronic malfunction of the glucose-insulin regulatory system leading to the onset of long and short term health threats caused by uncontrolled excursions of blood glycaemic levels outside the normal “euglycaemic” range (70 ÷ 180 mg/dL) [1]
non-invasive continuous glucose monitoring (NI-continuous glucose monitoring (CGM)) is appealing for obvious reasons related to patient comfort, current accuracy is not yet comparable with that of enzyme-based needle sensors which measure in the subcutis
The Monte Carlo simulation is undertaken in order to assess how the indexes defined in the previous section are influenced by the number of the reference blood glucose (RBG) points used for recalibration, namely, if the calibration rules proposed in the two case studies are really beneficial or if their improvements are only due to the consideration of more RBG points used for calibration
Summary
Diabetes consists in a chronic malfunction of the glucose-insulin regulatory system leading to the onset of long and short term health threats caused by uncontrolled excursions of blood glycaemic levels outside the normal “euglycaemic” range (70 ÷ 180 mg/dL) [1]. Glucose sensors can play a crucial role for improving diabetes treatment. Continuous glucose monitoring (CGM) sensors have been on the market since the early 2000s and are of great interest for several reasons related to the retrospective tuning and optimization of diabetes therapy, as well as for on-line applications such as the so called “artificial pancreas” or hypo/hyper glycemic event prediction [3,4,5,6,7,8,9,10]. Most of the CGM sensors exploit an enzyme based glucose-oxidase needle electrode and are invasive, minimally. To overcome the invasiveness of the needle-based methods, in the last decade several non-invasive continuous glucose monitoring (NI-CGM) technologies have been proposed (see [11] for a recent overview)
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