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Sensors 2016, 16(12), 2093; doi:10.3390/s16122093

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Department of Information Engineering, University of Padova, Padova 35131, Italy
Academic Editor: W. Rudolf Seitz
Received: 14 October 2016 / Revised: 17 November 2016 / Accepted: 7 December 2016 / Published: 9 December 2016
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
View Full-Text   |   Download PDF [888 KB, uploaded 9 December 2016]   |  

Abstract

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones. View Full-Text
Keywords: signal processing; diabetes; calibration; model identification; filtering; prediction signal processing; diabetes; calibration; model identification; filtering; prediction
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Facchinetti, A. Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges. Sensors 2016, 16, 2093.

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