Abstract
The availability of continuous glucose monitoring (CGM) sensors allows development of new strategies for the treatment of diabetes. In particular, from an on-line perspective, CGM sensors can become “smart” by providing them with algorithms able to generate alerts when glucose concentration is predicted to exceed the normal range thresholds. To do so, at least four important aspects have to be considered and dealt with on-line. First, the CGM data must be accurately calibrated. Then, CGM data need to be filtered in order to enhance their signal-to-noise ratio (SNR). Thirdly, predictions of future glucose concentration should be generated with suitable modeling methodologies. Finally, generation of alerts should be done by minimizing the risk of detecting false and missing true events. For these four challenges, several techniques, with various degrees of sophistication, have been proposed in the literature and are critically reviewed in this paper.
Highlights
The knowledge of glucose concentration in blood is a key aspect in the quantitative understanding of the glucose-insulin system and in the diagnosis and treatment of diabetes
Long term studies resorted to self-monitoring blood glucose (SMBG) data, i.e., three-to-four samples per day obtained by the patient himself by using fingerstick glucose meters
The retrospective analysis of SMBG time-series was used by physicians, together with the information taken from the ―patient’s diary‖ and some glycaemic indexes, to assess glucose control and the effectiveness of a particular therapy [4,5,6,7]
Summary
The knowledge of glucose concentration in blood is a key aspect in the quantitative understanding of the glucose-insulin system and in the diagnosis and treatment of diabetes. CGM signals are a key component of the so-called artificial pancreas, a device conceived for Type 1 diabetic patients aimed at maintaining glucose concentration within safe ranges by infusing insulin subcutaneously via a pump under the control of a closed-loop algorithm (see Hovorka and Cobelli et al for two recent reviews [18,19]). Another important on-line application of CGM sensors is the generation of alerts when glucose concentration is predicted to exceed the normal range thresholds [20]. We note that we will review only general aspects, i.e., signal processing, while possible sensor-dependant sources of error, e.g., related to specific sensor physics, chemistry and electronics, are not addressed here
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