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“Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues
Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy
* Author to whom correspondence should be addressed.
Received: 30 May 2010; in revised form: 25 June 2010 / Accepted: 30 June 2010 / Published: 12 July 2010
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.
Keywords: diabetes; prediction; filtering; calibration; model; time-series
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Sparacino, G.; Facchinetti, A.; Cobelli, C. “Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues. Sensors 2010, 10, 6751-6772.
Sparacino G, Facchinetti A, Cobelli C. “Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues. Sensors. 2010; 10(7):6751-6772.
Sparacino, Giovanni; Facchinetti, Andrea; Cobelli, Claudio. 2010. "“Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues." Sensors 10, no. 7: 6751-6772.