Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes

Zeinab Mahmoudi, 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 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.
Original languageEnglish
Title of host publicationProceedings of the 15th annual European Control Conference (ECC '16)
PublisherIEEE
Publication date2016
Pages714-719
ISBN (Print)978-1-5090-2590-9
Publication statusPublished - 2016
Event15th European Control Conference (ECC16) - Aalborg, Denmark
Duration: 29 Jun 20161 Jul 2016
Conference number: 16
http://www.ecc16.eu/call.shtml

Conference

Conference15th European Control Conference (ECC16)
Number16
CountryDenmark
CityAalborg
Period29/06/201601/07/2016
Internet address

Cite this

Mahmoudi, Z., Boiroux, D., Hagdrup, M., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016). Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes. In Proceedings of the 15th annual European Control Conference (ECC '16) (pp. 714-719). IEEE.
Mahmoudi, Zeinab ; Boiroux, Dimitri ; Hagdrup, Morten ; Nørgaard, Kirsten ; Poulsen, Niels Kjølstad ; Madsen, Henrik ; Jørgensen, John Bagterp. / Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes. Proceedings of the 15th annual European Control Conference (ECC '16). IEEE, 2016. pp. 714-719
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title = "Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes",
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.",
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Mahmoudi, Z, Boiroux, D, Hagdrup, M, Nørgaard, K, Poulsen, NK, Madsen, H & Jørgensen, JB 2016, Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes. in Proceedings of the 15th annual European Control Conference (ECC '16). IEEE, pp. 714-719, 15th European Control Conference (ECC16), Aalborg, Denmark, 29/06/2016.

Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes. / Mahmoudi, Zeinab; Boiroux, Dimitri; Hagdrup, Morten; Nørgaard, Kirsten; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp.

Proceedings of the 15th annual European Control Conference (ECC '16). IEEE, 2016. p. 714-719.

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

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AU - Hagdrup, Morten

AU - Nørgaard, Kirsten

AU - Poulsen, Niels Kjølstad

AU - Madsen, Henrik

AU - Jørgensen, John Bagterp

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N2 - 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.

AB - 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.

M3 - Article in proceedings

SN - 978-1-5090-2590-9

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BT - Proceedings of the 15th annual European Control Conference (ECC '16)

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Mahmoudi Z, Boiroux D, Hagdrup M, Nørgaard K, Poulsen NK, Madsen H et al. Application of the Continuous-Discrete Extended Kalman Filter for Fault Detection in Continuous Glucose Monitors for Type 1 Diabetes. In Proceedings of the 15th annual European Control Conference (ECC '16). IEEE. 2016. p. 714-719