ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

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dc.contributor.authorMaršánová, Luciecs
dc.contributor.authorRonzhina, Marinacs
dc.contributor.authorSmíšek, Radovancs
dc.contributor.authorVítek, Martincs
dc.contributor.authorNěmcová, Andreacs
dc.contributor.authorSmital, Lukášcs
dc.contributor.authorNováková, Mariecs
dc.coverage.issue7cs
dc.date.accessioned2020-08-04T11:58:07Z
dc.date.available2020-08-04T11:58:07Z
dc.date.issued2017-09-11cs
dc.description.abstractAccurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).en
dc.formattextcs
dc.format.extent1-11cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationScientific Reports. 2017, issue 7, p. 1-11.en
dc.identifier.doi10.1038/s41598-017-10942-6cs
dc.identifier.issn2045-2322cs
dc.identifier.other139580cs
dc.identifier.urihttp://hdl.handle.net/11012/70118
dc.language.isoencs
dc.publisherNaturecs
dc.relation.ispartofScientific Reportscs
dc.relation.urihttp://link.springer.com/article/10.1038/s41598-017-10942-6cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2045-2322/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectAutomatic heartbeat classificationen
dc.subjectmyocardial ischemiaen
dc.subjectventricular premature beatsen
dc.subjectECGen
dc.subjectmorphological featuresen
dc.subjectspectral featuresen
dc.subjectdiscriminant functionen
dc.subjectnaive Bayes classifieren
dc.subjectsupport vector machineen
dc.subjectk-nearest neighborsen
dc.subjectrabbit isolated hearten
dc.titleECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental studyen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-139580en
sync.item.dbtypeVAVen
sync.item.insts2020.09.02 13:54:51en
sync.item.modts2020.09.02 13:39:32en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
thesis.grantorVysoké učení technické v Brně. . Masarykova Univerzita v Brněcs
thesis.grantorVysoké učení technické v Brně. . Ústav přístrojové techniky AV ČRcs
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