Abstract
The purpose of this study is the online detection of faults and anomalies of a continuous glucose monitor (CGM). We simulated a type 1 diabetes patient using the Medtronic virtual patient model. The model is a system of stochastic differential equations and includes insulin pharmacokinetics, insulin-glucose interaction, and carbohydrate absorption. We simulated and detected two types of CGM faults, i.e., spike and drift. A fault was defined as a CGM value in any of the zones C, D, and E of the Clarke error grid analysis classification. Spike was modelled by a binomial distribution, and drift was modelled by a Gaussian random walk. We used a continuous-discrete extended Kalman filter for the fault detection, based on the statistical tests of the filter innovation and the 90-min prediction residuals of the sensor measurements. The spike detection had a sensitivity of 93% and a specificity of 100%. Also, the drift detection had a sensitivity of 80% and a specificity of 85%. Furthermore, with 100% sensitivity the proposed method was able to detect if the drift overestimates or underestimates the interstitial glucose concentration.
Originalsprog | Engelsk |
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Titel | 2016 European Control Conference, ECC 2016 |
Antal sider | 6 |
Forlag | Institute of Electrical and Electronics Engineers Inc. |
Publikationsdato | 6 jan. 2017 |
Sider | 714-719 |
Artikelnummer | 7810373 |
ISBN (Elektronisk) | 9781509025916 |
DOI | |
Status | Udgivet - 6 jan. 2017 |
Begivenhed | 2016 European Control Conference, ECC 2016 - Aalborg, Danmark Varighed: 29 jun. 2016 → 1 jul. 2016 |
Konference
Konference | 2016 European Control Conference, ECC 2016 |
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Land/Område | Danmark |
By | Aalborg |
Periode | 29/06/2016 → 01/07/2016 |