Paper Info
Title
Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device.
Abstract
Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.
Year
DOI
Venue
2019
10.3390/s19173677
SENSORS
Keywords
Field
DocType
diabetes,continuous glucose monitoring,non-invasive,multisensor
Reference values,Continuous glucose monitoring,Pattern recognition,Linear model,Regression analysis,Diabetes management,Electronic engineering,Artificial intelligence,Engineering,Multiple linear regression model
Journal
Volume
Issue
ISSN
19
17.0
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Giada Acciaroli121.82
Mattia Zanon200.34
Andrea Facchinetti315228.83
Andreas Caduff4122.53
Giovanni Sparacino527652.52