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dc.contributor.authorKölle, Konstanze
dc.contributor.authorBiester, Torben
dc.contributor.authorChristiansen, Sverre
dc.contributor.authorFougner, Anders Lyngvi
dc.contributor.authorStavdahl, Øyvind
dc.date.accessioned2019-04-15T06:14:36Z
dc.date.available2019-04-15T06:14:36Z
dc.date.created2019-03-28T12:24:46Z
dc.date.issued2019
dc.identifier.citationIEEE journal of biomedical and health informatics. 2019, 23 .nb_NO
dc.identifier.issn2168-2194
dc.identifier.urihttp://hdl.handle.net/11250/2594559
dc.description.abstractAccurate continuous glucose monitoring (CGM) is essential for fully automated glucose control in diabetes mellitus type 1. State-of-the-art glucose control systems automatically regulate the basal insulin infusion. Users still need to manually announce meals to dose the prandial insulin boluses. An automated meal detection could release the user and improve the glucose regulation. In this study, patterns in the postprandial CGM data are exploited for meal detection. Binary classifiers are trained to recognize the postprandial pattern in horizons of the estimated glucose rate of appearance and in CGM data. The appearance rate is determined by moving horizon estimation (MHE) based on a simple model. Linear discriminant analysis (LDA) is used for classification. The proposed method is compared to methods that detect meals when thresholds are violated. Diabetes care data from twelve free-living pediatric patients was downloaded during regular screening. Experts identified meals and their start by retrospective evaluation. The classification was tested by cross-validation. Compared to the threshold-based methods, LDA showed higher sensitivity to meals with a low rate of false alarms. Classifying horizons outperformed the other methods also with respect to time of detection. The onset of meals can be detected by pattern recognition based on estimated model states and consecutive CGM measurements. No individual tuning is necessary. This makes the method easily adopted in the clinical practice.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.subjectMåltidsdeteksjonnb_NO
dc.subjectMeal detectionnb_NO
dc.subjectType 1 diabetesnb_NO
dc.subjectKontinuerlig glukosemålingnb_NO
dc.subjectContinuous glucose measurementnb_NO
dc.subjectMønstergjenkjenningnb_NO
dc.subjectPattern Recognitionnb_NO
dc.titlePattern recognition reveals characteristic postprandial glucose changes: Non-individualized meal detection in diabetes mellitus type 1nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.subject.nsiVDP::Medisinsk teknologi: 620nb_NO
dc.subject.nsiVDP::Medical technology: 620nb_NO
dc.source.pagenumber9nb_NO
dc.source.volume23nb_NO
dc.source.journalIEEE journal of biomedical and health informaticsnb_NO
dc.identifier.doi10.1109/JBHI.2019.2908897
dc.identifier.cristin1688517
dc.relation.projectNorges forskningsråd: 248872nb_NO
dc.relation.projectSamarbeidsorganet mellom Helse Midt-Norge og NTNU: 46075403nb_NO
dc.description.localcode© 2019 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.unitcode194,65,15,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.unitnameInstitutt for klinisk og molekylær medisin
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode1


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