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dc.contributor.authorSetti, Sunilkumar Telagam
dc.contributor.authorSøiland, Elise
dc.contributor.authorStavdahl, Øyvind
dc.contributor.authorFougner, Anders Lyngvi
dc.date.accessioned2020-02-12T14:40:45Z
dc.date.available2020-02-12T14:40:45Z
dc.date.created2020-01-02T14:31:44Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-2603-6
dc.identifier.urihttp://hdl.handle.net/11250/2641382
dc.description.abstractA typical artificial pancreas depends only on the continuous glucose monitoring (CGM) value for insulin dosing. However, both the insulin infusion and the glucose sensing are subject to time delays and slow dynamics. An automated and reliable meal onset information could enhance the control outcome of artificial pancreas by making it possible to infuse insulin earlier and thereby avoid large postprandial glucose excursions. In this study we employ abdominal sounds recorded in two healthy volunteers with a condenser microphone and propose an automated approach for meal onset detection from abdominal sounds. We use the Mel-frequency cepstral coefficients (MFCCs) and wavelet entropy extracted from the abdominal sounds as features. These features are fed to a simple feed forward neural network for discriminating meal from no-meal abdominal sounds. This approach detects meal onset with an average delay of 4.3 minutes in our limited number of subjects. More importantly, it provides lesser response delay than the state-of-the-art CGM based approach, which achieved a response delay ranging from 30- 40 minutes. The preliminary results indicate that the proposed abdominal sound-based approach may provide early meal onset information. This can be exploited in an artificial pancreas through allowable earlier meal insulin boluses, resulting in improved glycemic control.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartofIEEE International Conference on e-Health and Bioengineering (EHB 2019), Proceedings of the
dc.relation.urihttp://hdl.handle.net/11250/2634803
dc.subjectLydanalyse /syntesenb_NO
dc.subjectSound analysis /synthesisnb_NO
dc.subjectMåltidsdeteksjonnb_NO
dc.subjectMeal detectionnb_NO
dc.subjectTarmlydernb_NO
dc.subjectBowel soundsnb_NO
dc.subjectDiabetesnb_NO
dc.titlePilot study of Early Meal Onset Detection from Abdominal Soundsnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.subject.nsiVDP::Medisinsk teknologi: 620nb_NO
dc.subject.nsiVDP::Medical technology: 620nb_NO
dc.identifier.doihttp://dx.doi.org/10.1109/EHB47216.2019.8969901
dc.identifier.cristin1765350
dc.relation.projectNorges forskningsråd: 294828nb_NO
dc.relation.projectNorges forskningsråd: 248872nb_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.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
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cristin.fulltextoriginal


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