Paper Info
Title
Non-invasive continuous glucose monitoring: improved accuracy of point and trend estimates of the Multisensor system.
Abstract
Non-invasive continuous glucose monitoring (NI-CGM) sensors are still at an early stage of development, but, in the near future, they could become particularly appealing in diabetes management. Solianis Monitoring AG (Zurich, Switzerland) has proposed an approach for NI-CGM based on a multi-sensor concept, embedding primarily dielectric spectroscopy and optical sensors. This concept requires a mathematical model able to estimate glucose levels from the 150 channels directly measured through the Multisensor. A static multivariate linear regression model (with order and parameters common to the entire population of subjects) was proposed for such a scope (Caduff et al., Biosens Bioelectron 26:3794-3800, 2011). The aim of this work is to evaluate the accuracy in the estimation of glucose levels and trends that the NI-CGM Multisensor platform can achieve by exploiting different techniques for model identification, namely, ordinary least squares, subset variable selection, partial least squares and least absolute shrinkage and selection operator (LASSO). Data collected in human beings monitored for a total of 45 study days were used for model identification and model test. Several metrics of standard use in the diabetes scientific community to measure point and clinical accuracy of glucose sensors were used to assess the models. Results indicate that the LASSO technique is superior to the others shrinking many channel weights to zero thus leading to smoother glucose profiles and resulting in a more robust model to possible artifacts in the Multisensor data. Although, as expected, the performance of the NI-CGM system with the LASSO model is not yet comparable with that of enzyme-based needle glucose sensors, glucose trends are satisfactorily estimated. Considering the non-invasive nature of the multi-sensor platform, this result can have an immediate impact in the current clinical practice, e.g., to integrate sparse self-monitoring of blood glucose data with an indication of the glucose trend to aid the diabetic patient in dealing with, or even preventing in the short time scale, the threats of critical events such as hypoglycaemia.
Year
DOI
Venue
2012
10.1007/s11517-012-0932-6
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Diabetes, Glucose sensor, Self-monitoring blood glucose, Multivariate models, Linear regression
Population,Data mining,Feature selection,Ordinary least squares,Lasso (statistics),Partial least squares regression,Bayesian multivariate linear regression,System identification,Mathematics,Linear regression
Journal
Volume
Issue
ISSN
50
10
1741-0444
Citations 
PageRank 
References 
7
0.80
8
Authors
8
Name
Order
Citations
PageRank
Mattia Zanon1111.99
Giovanni Sparacino227652.52
Andrea Facchinetti315228.83
Michela Riz4122.26
Mark Talary5122.19
Roland E. Suri6686.89
Andreas Caduff7122.53
Claudio Cobelli8658113.31