NDLI: Improving Accuracy and Precision of Glucose Sensor Profiles: Retrospective Fitting by Constrained Deconvolution

Content Provider	IEEE Xplore Digital Library
Author	Del Favero, S. Facchinetti, A. Sparacino, G. Cobelli, C.
Copyright Year	1964
Abstract	Frequent and accurate reference measurements of blood-glucose (BG) concentration are key for modeling and for computing outcome metrics in clinical trials but difficult, invasive, and costly to collect. Continuous glucose monitoring (CGM) is a minimally-invasive technology that has the requested temporal resolution to substitute BG references for such a scope, but still lacks of precision and accuracy. In this paper, we propose an algorithm that retrospectively reconstructs a reliable continuous-time BG profile for the aforementioned purposes, by simultaneously exploiting the high accuracy of (possibly sparse) BG references and the high temporal resolution of CGM data. The algorithm performs a constrained semiblind deconvolution in two steps: first, it estimates the unknown parameters of a model accounting for plasma-interstitum diffusion and sensor inaccurate calibration; then, it estimates BG performing a regularized deconvolution of CGM data, subject to the additional constraint that the reconstructed BG profile has to lay within the confidence interval of the available BG references. The algorithm was tested on 24 datasets collected in a 20 h clinical trial where CGM records and a median of 13 BG samples per day were available. Mean absolute relative deviation was reduced (from 15.71% to 8.84%) with respect to unprocessed CGM and so did the error in the evaluation of the outcomes metrics (e.g., halved the error in the time-in-hypo assessment). The reconstructed BG profile, in view of its improved accuracy and precision, is suitable for clinical trial assessment, modeling and other offline applications.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	1932261
Starting Page	1044
Ending Page	1053
File Format	PDF
ISSN	00189294
Volume Number	61
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Calibration Sugar Deconvolution Noise Noise measurement Vectors Accuracy outpatient clinical trial evaluation Artificial pancreas constrained semiblind deconvolution continuous glucose monitoring (CGM) diabetes outcome metric computation
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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