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dc.contributor.authorKölle, Konstanze
dc.contributor.authorFougner, Anders Lyngvi
dc.contributor.authorStavdahl, Øyvind
dc.date.accessioned2017-11-10T14:59:16Z
dc.date.available2017-11-10T14:59:16Z
dc.date.created2017-09-13T14:14:35Z
dc.date.issued2017
dc.identifier.issn1066-033X
dc.identifier.urihttp://hdl.handle.net/11250/2465615
dc.description.abstractMeals are one of the greatest challenges to glucose regulation in diabetes mellitus type 1. Several times each day, food causes heavily elevated blood glucose concentrations that may result in long-term complications. Meal-time insulin boluses are administered to mitigate these hyperglycemic periods. Sporadic omissions of prandial boluses impair the outcome of the insulin therapy by leading to significant variations in blood glucose levels. As continuous glucose monitoring (CGM) becomes more common, an automated detection based on CGM data could support patients by reminding about missed boluses. In fully automated systems, meal detection could temporarily modify controller parameters until the meal is mitigated. In the present study, moving horizon estimation (MHE) and linear discriminant analysis (LDA), abbreviated “MHE+LDA”, are proposed for meal detection. An augmented version of Bergman's minimal model is used for the estimator model. Neither the model parameters nor the MHE tuning are individualized. The method is tested in simulations on the UVa/Padova simulator and its performance is compared to two other methods, namely threshold checking of the current estimated glucose appearance and the GRID algorithm. All meals are detected by MHE+LDA within 35 min while the two comparative methods do not detect the smallest simulated meal. The combination of MHE and LDA outperforms the two other methods also with respect to time of detection. The MHE+LDA method's ability to identify even smaller meals without the need for individual tuning suggests that the method should be further investigated.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleMeal detection based on non-individualized moving horizon estimation and classificationnb_NO
dc.typeJournal articlenb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.journalIEEE Conference on Control Technology and Applicationsnb_NO
dc.identifier.doi10.1109/CCTA.2017.8062516
dc.identifier.cristin1493371
dc.relation.projectNorges forskningsråd: 2248872nb_NO
dc.relation.projectSamarbeidsorganet mellom Helse Midt-Norge og NTNU: 46075401nb_NO
dc.relation.projectSamarbeidsorganet mellom Helse Midt-Norge og NTNU: 46075403nb_NO
dc.description.localcode© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedfalse
cristin.fulltextpreprint
cristin.qualitycode1


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