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Mean Glucose Slope,  Principal Component Analysis Classification to Detect Insulin Infusion Set Failure
Electrical Engineering Department, Universidad de Los Andes.
KTH, School of Electrical Engineering (EES), Automatic Control. (Automatic Control Laboratory)
Chemical and Biological Engineering Department, Rensselaer Polytechnic.
2011 (English)In: 18th IFAC World Congress, Milan, Italy, 2011Conference paper (Refereed)
Abstract [en]

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.
Keyword [en]
Type 1 Diabetes, Failure detection, Multivariate statistical analysis, Bivariate classification
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-46978ScopusID: 2-s2.0-84866753282OAI: diva2:454386
18th IFAC World Congress. Milano (Italy). August 28 - September 2, 2011
QC 20111114Available from: 2011-11-07 Created: 2011-11-07 Last updated: 2011-11-14Bibliographically approved

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