Mean Glucose Slope, Principal Component Analysis Classification to Detect Insulin Infusion Set Failure
2011 (English)In: 18th IFAC World Congress, Milan, Italy, 2011Conference paper (Refereed)
The bivariate classification technique using the mean glucose slope (MGS) and the first component of the principal component analysis (PCA), is applied to insulin infusion set failure detection (IISF), a challenging problem faced by individuals with type 1 diabetes that are on continuous insulin infusion pump therapy. The objective of this study was to determine if the proposed approach could be used to distinguish between normal patient data and data from patients under IISF online, in a reasonably short period of time. The proposed approach was applied to simulated glucose concentrations for 10 patients, based on a nonlinear physiological model of insulin and glucose dynamics. Although it presents few false alarms, it was capable of detecting most drifting (ramp) infusion set failures before complete failure occurred.
Place, publisher, year, edition, pages
Milan, Italy, 2011.
Type 1 Diabetes, Failure detection, Multivariate statistical analysis, Bivariate classification
IdentifiersURN: urn:nbn:se:kth:diva-46978ScopusID: 2-s2.0-84866753282OAI: oai:DiVA.org:kth-46978DiVA: diva2:454386
18th IFAC World Congress. Milano (Italy). August 28 - September 2, 2011
QC 201111142011-11-072011-11-072011-11-14Bibliographically approved