Multiple Instance Learning Framework Used For Ecg Premature Contraction Localization

but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorNovotna, Petra
dc.date.accessioned2021-07-21T07:06:59Z
dc.date.available2021-07-21T07:06:59Z
dc.date.issued2021cs
dc.description.abstractWe propose the model combining convolutional neural network with multiple instancelearning in order to localize the premature atrial contraction and premature ventricular contraction.The model is based on ResNet architecture modified for 1D signal processing. Model was trainedon China Physiological Signal Challenge 2018 database extended by manually labeled ground truthpositions of premature complexes. The presented method did not reach satisfying results in PAClocalization (with dice = 0.127 for avg-pooling implementation). On the other hand, results of localizationof PVCs were comparable with other published studies (with dice = 0.952 for avg-poolingimplementation).en
dc.formattextcs
dc.format.extent311-315cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 311-315. ISBN 978-80-214-5942-7cs
dc.identifier.isbn978-80-214-5942-7
dc.identifier.urihttp://hdl.handle.net/11012/200770
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 27st Conference STUDENT EEICT 2021: General papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectEEICTen
dc.subjectECGen
dc.subjectPACen
dc.subjectPVCen
dc.subjectCNNen
dc.subjectMILen
dc.subjectarrhytmiaen
dc.subjectlocalizationen
dc.titleMultiple Instance Learning Framework Used For Ecg Premature Contraction Localizationen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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