Electrocardiogram beat classification using deep convolutional neural network techniques

dc.contributor.author	Cömert, Z.
dc.contributor.author	Akbulut, Y.
dc.contributor.author	Akpinar, M.H.
dc.contributor.author	Alçin, Ö.F.
dc.contributor.author	Budak, U.
dc.contributor.author	Aslan, M.
dc.contributor.author	Şengür, A.
dc.date.accessioned	2021-12-16T10:11:52Z
dc.date.available	2021-12-16T10:11:52Z
dc.date.issued	2020
dc.identifier.isbn	9780750332798; 9780750332774
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/12809
dc.description.abstract	The electrocardiogram (ECG) is a useful method which enables the monitoring of various cardiac conditions, such as arrhythmia and heart rate variability (HRV). ECG beats help to determine various heart failures such as cardiac disease and ventricular tach
dc.language.iso	English
dc.publisher	Institute of Physics Publishing
dc.source	Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1
dc.title	Electrocardiogram beat classification using deep convolutional neural network techniques
dc.type	Book Chapter
dc.identifier.endpage	Dec-25
dc.identifier.scopus	2-s2.0-85096256069

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