ECG signal classification based on SVM

Smíšek, Radovan

ECG signal classification based on SVM

but.event.date	28.04.2016	cs
but.event.title	Student EEICT 2016	cs
dc.contributor.author	Smíšek, Radovan
dc.date.accessioned	2018-07-10T12:48:17Z
dc.date.available	2018-07-10T12:48:17Z
dc.date.issued	2016	cs
dc.description.abstract	Cardiovascular diseases nowadays represent the most common cause of death in Western countries. Long-term ECG recording is modern method, because it allows to detect sporadically occurring pathology. We designed an automatic classifier to detect five pathologies (AAMI standard) by SVM method. The classifier was tested on the entire MIT-BIH Arrhythmia Database with an accuracy of 99.17 %. We also compared the quality of parameters entering the classifier.	en
dc.format	text	cs
dc.format.extent	365-369	cs
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Proceedings of the 22nd Conference STUDENT EEICT 2016. s. 365-369. ISBN 978-80-214-5350-0	cs
dc.identifier.isbn	978-80-214-5350-0
dc.identifier.uri	http://hdl.handle.net/11012/83957
dc.language.iso	en	cs
dc.publisher	Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.relation.ispartof	Proceedings of the 22nd Conference STUDENT EEICT 2016	en
dc.relation.uri	http://www.feec.vutbr.cz/EEICT/	cs
dc.rights	© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.rights.access	openAccess	en
dc.subject	ECG classification	en
dc.subject	support vector machines	en
dc.subject	SVM	en
dc.subject	MIT-BIH database	en
dc.title	ECG signal classification based on SVM	en
dc.type.driver	conferenceObject	en
dc.type.status	Peer-reviewed	en
dc.type.version	publishedVersion	en
eprints.affiliatedInstitution.department	Fakulta elektrotechniky a komunikačních technologií	cs

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