Comparison of Three Nonlinear Filters for Fault Detection in Continuous Glucose Monitors

Zeinab Mahmoudi, Sabrina Lyngbye Wendt, Dimitri Boiroux, Morten Hagdrup, Kirsten Nørgaard, Niels Kjølstad Poulsen, Henrik Madsen, John Bagterp Jørgensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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%.
Original languageEnglish
Title of host publicationProceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’2016)
PublisherIEEE
Publication date2016
Pages3507-3510
ISBN (Print)978-1-4577-0220-4
Publication statusPublished - 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16) - Orlando, FL, United States
Duration: 16 Aug 201620 Aug 2016
Conference number: 38
http://embc.embs.org/2016/

Conference

Conference38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16)
Number38
CountryUnited States
CityOrlando, FL
Period16/08/201620/08/2016
Internet address

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