title: Signal processing algorithms implementing the "smart sensor" concept to improve continuous glucose monitoring in diabetes.
abstract: Glucose readings provided by current continuous glucose monitoring (CGM) devices still suffer from accuracy and precision issues. In April 2013, we proposed a new conceptual architecture to deal with these problems and render CGM sensors algorithmically smarter, which consists of three modules for denoising, enhancement, and prediction placed in cascade to a commercial CGM sensor. The architecture was assessed on a data set consisting of 24 type 1 diabetes patients collected in four clinical centers of the AP@home Consortium (a European project of 7th Framework Programme funded by the European Committee). This article, as a companion to our prior publication, illustrates the technical details of the algorithms and of the implementation issues.
authors.affiliation: Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation*; D003920:Diabetes Mellitus / Q000097:blood*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation; D012815:Signal Processing, Computer-Assisted