title: Comparison of three nonlinear filters for fault detection in continuous glucose monitors.
abstract: The purpose of this study is to compare the performance of three nonlinear filters in online drift detection of continuous glucose monitors. The nonlinear filters are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the particle filter (PF). They are all based on a nonlinear model of the glucose-insulin dynamics in people with type 1 diabetes. Drift is modelled by a Gaussian random walk and is detected based on the statistical tests of the 90-min prediction residuals of the filters. The unscented Kalman filter had the highest average F score of 85.9%, and the smallest average detection delay of 84.1%, with the average detection sensitivity of 82.6%, and average specificity of 91.0%.
authors.forename: Zeinab
authors.initials: Z
authors.lastname: Mahmoudi
authors.forename: Sabrina Lyngbye
authors.initials: SL
authors.lastname: Wendt
authors.forename: Dimitri
authors.initials: D
authors.lastname: Boiroux
authors.forename: Morten
authors.initials: M
authors.lastname: Hagdrup
authors.forename: Kirsten
authors.initials: K
authors.lastname: Norgaard
authors.forename: Niels Kjolstad
authors.initials: NK
authors.lastname: Poulsen
authors.forename: Henrik
authors.initials: H
authors.lastname: Madsen
authors.forename: John Bagterp
authors.initials: JB
authors.lastname: Jorgensen
mesh_terms: D001774:Blood Chemical Analysis / Q000295:instrumentation / Q000379:methods*; D001786:Blood Glucose / Q000032:analysis*; D006801:Humans; D008954:Models, Biological; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted
chemical_list: D001786:Blood Glucose