Cardiac Pathologies Detection and Classification in 12-lead ECG

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dc.contributor.authorSmíšek, Radovancs
dc.contributor.authorNěmcová, Andreacs
dc.contributor.authorŠaclová, Luciecs
dc.contributor.authorSmital, Lukášcs
dc.contributor.authorVítek, Martincs
dc.contributor.authorKozumplík, Jiřícs
dc.coverage.issue1cs
dc.coverage.volume47cs
dc.date.issued2020-12-30cs
dc.description.abstractBackground: Automatic detection and classification of cardiac abnormalities in ECG is one of the basic and often solved problems. The aim of this paper is to present a proposed algorithm for ECG classification into 19 classes. This algorithm was created within PhysioNet/CinC Challenge 2020, name of our team was HITTING. Methods: Our algorithm detects each pathology separately according to the extracted features and created rules. Signals from the 6 databases were used. Detector of QRS complexes, T-waves and P-waves including detection of their boundaries was designed. Then, the most common morphology of the QRS was found in each record. All these QRS were averaged. Features were extracted from the averaged QRS and from intervals between detected points. Appropriate features and rules were set using classification trees. Results: Our approach achieved a challenge validation score of 0.435, and full test score of 0.354, placing us 11 out of 41 in the official ranking. Conclusion: The advantage of our algorithm is easy interpretation. It is obvious according to which features algorithm decided and what thresholds were set.en
dc.formattextcs
dc.format.extent1-4cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputing in Cardiology. 2020, vol. 47, issue 1, p. 1-4.en
dc.identifier.doi10.22489/CinC.2020.171cs
dc.identifier.issn2325-887Xcs
dc.identifier.orcid0000-0003-0413-3604cs
dc.identifier.orcid0000-0003-1801-7057cs
dc.identifier.orcid0000-0003-1638-4814cs
dc.identifier.orcid0000-0003-1526-4626cs
dc.identifier.orcid0000-0002-8059-1087cs
dc.identifier.orcid0000-0003-3570-6524cs
dc.identifier.other166076cs
dc.identifier.researcheridF-5329-2017cs
dc.identifier.researcheridAAH-1590-2021cs
dc.identifier.researcheridAAL-7695-2021cs
dc.identifier.researcheridH-8505-2014cs
dc.identifier.researcheridD-3351-2014cs
dc.identifier.researcheridKBC-7958-2024cs
dc.identifier.scopus57188873046cs
dc.identifier.scopus6507784572cs
dc.identifier.scopus57188871806cs
dc.identifier.scopus54960986600cs
dc.identifier.scopus35767287500cs
dc.identifier.scopus55931525500cs
dc.identifier.urihttp://hdl.handle.net/11012/196704
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofComputing in Cardiologycs
dc.relation.urihttp://www.cinc.org/archives/2020/pdf/CinC2020-171.pdfcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2325-887X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectECG classificationen
dc.subjectcardiac pathologies classificationen
dc.titleCardiac Pathologies Detection and Classification in 12-lead ECGen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-166076en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:47en
sync.item.modts2025.01.17 18:32:59en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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