Multivariate statistical analysis to detect insulin infusion set failure

Multivariate statistical analysis techniques are 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. Bivariate classification (BC), principal component analysis (PCA), and a combined approach were applied to simulated glucose concentrations for 10 patients, based on a nonlinear physiological model of insulin and glucose dynamics. The PCA algorithm had fewer false alarms than BC, while detecting most drifting (ramp) infusion set failures before complete failure occurred.