Robust QRS Detection Using Combination of Three Independent Methods

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dc.contributor.authorSmital, Lukášcs
dc.contributor.authorŠaclová, Luciecs
dc.contributor.authorSmíšek, Radovancs
dc.contributor.authorNěmcová, Andreacs
dc.contributor.authorVítek, Martincs
dc.coverage.issue1cs
dc.coverage.volume47cs
dc.date.issued2020-12-28cs
dc.description.abstractQRS detection is a fundamental step in ECG analysis. Although there are many algorithms reporting results close to 100%, this problem is still not resolved. The reported numbers are influenced by the quality of the detector, the quality of annotations and also by the chosen method of testing. In this study, we proposed and properly tested robust QRS detection algorithm based on a combination of three independent principles. For enhancement of QRS complexes there were developed three independent approaches based on continuous wavelet transform, Stockwell transform and phasor transform which are followed by individual adaptive thresholding. Each method produces candidates for QRS complexes which are further processed by cluster analysis resulting in final QRS positions. The proposed detection algorithm was tested on three complete standard ECG databases: MIT-BIH Arrhythmia Database, European ST-T Database and QT Database without any change in algorithm setting. We utilized complete data from mentioned databases including all provided leads and used original (not adjusted) reference positions of QRS complexes. Summarized detection accuracy for all three databases was expressed by sensitivity 99.16% and positive predictive value 98.99%.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.2020.100cs
dc.identifier.issn2325-887Xcs
dc.identifier.orcid0000-0003-1526-4626cs
dc.identifier.orcid0000-0003-1638-4814cs
dc.identifier.orcid0000-0003-0413-3604cs
dc.identifier.orcid0000-0003-1801-7057cs
dc.identifier.orcid0000-0002-8059-1087cs
dc.identifier.other166054cs
dc.identifier.researcheridH-8505-2014cs
dc.identifier.researcheridAAL-7695-2021cs
dc.identifier.researcheridF-5329-2017cs
dc.identifier.researcheridAAH-1590-2021cs
dc.identifier.researcheridD-3351-2014cs
dc.identifier.scopus54960986600cs
dc.identifier.scopus57188871806cs
dc.identifier.scopus57188873046cs
dc.identifier.scopus6507784572cs
dc.identifier.scopus35767287500cs
dc.identifier.urihttp://hdl.handle.net/11012/196701
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofComputing in Cardiologycs
dc.relation.urihttp://www.cinc.org/archives/2020/pdf/CinC2020-100.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.subjectECGen
dc.subjectQRS detectionen
dc.subjectStockwell transformen
dc.subjectcontinuous wavelet transformen
dc.subjectphasor transformen
dc.titleRobust QRS Detection Using Combination of Three Independent Methodsen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-166054en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:46en
sync.item.modts2025.01.17 15:24:06en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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