Estimation of blood glucose level based on PPG signals measured by smart devices

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but.event.date25.04.2023cs
but.event.titleSTUDENT EEICT 2023cs
dc.contributor.authorVargová, Enikö
dc.contributor.authorNěmcová, Andrea
dc.date.accessioned2023-07-17T05:57:34Z
dc.date.available2023-07-17T05:57:34Z
dc.date.issued2023cs
dc.description.abstractThis paper deals with the possibilities of non-invasivedetermination of blood glucose from photoplethysmographic signals.Monitoring blood sugar is the most important part of managingdiabetes. Diabetes is one of the world’s major chronic diseases.Untreated diabetes is often a cause of death.Two datasets have been created by recording thephotoplethysmographic signals of 16 people using two smart devices(a smart wristband and a smartphone), along with their bloodglucose levels measured in an invasive way. Thephotoplethysmographic signals were preprocessed, and suitablefeatures were extracted from them. The aim of the work is to proposemethods for glycemic classification and prediction.Various machine-learning models were created. The best modelfor classifying blood glucose into two groups (low blood glucose andhigh blood glucose) is random forest, which achieves an F1 score of84% and 80% on two different test sets obtained from two smartdevices. The best blood glucose level prediction model is also basedon random forest and achieves an MAE of 1.02 mmol/l and 1.17mmol/l on both testing datasets.en
dc.formattextcs
dc.format.extent137-140cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers. s. 137-140. ISBN 978-80-214-6154-3cs
dc.identifier.doi10.13164/eeict.2023.137
dc.identifier.isbn978-80-214-6154-3
dc.identifier.issn2788-1334
dc.identifier.urihttp://hdl.handle.net/11012/210675
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 29st Conference STUDENT EEICT 2023: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectPPGen
dc.subjectglycemiaen
dc.subjectdiabetesen
dc.subjectsmartphoneen
dc.subjectsmartdevicesen
dc.subjectclassificationen
dc.subjectpredictionen
dc.titleEstimation of blood glucose level based on PPG signals measured by smart devicesen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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