Ecg Signal Classification Technique Based On Deep Features Using
Differential Evolution Algorithm Extreme Learning Machine (Dea-Elm)

dc.contributor.author	Diker, Aykut
dc.contributor.author	Avci, Engin
dc.date.accessioned	2021-12-15T10:46:32Z
dc.date.available	2021-12-15T10:46:32Z
dc.date.issued	2020
dc.identifier.issn	2147-3129
dc.identifier.issn	2147-3188
dc.identifier.uri	https://app.trdizin.gov.tr/makale/TXpjNU16RXlNZz09/ecg-signal-classification-technique-based-on-deep-features-using-differential-evolution-algorithm-extreme-learning-machine-dea-elm-
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/3214
dc.description.abstract	The movements of electrocardiogram (ECG) signals are very important in the diagnosis of heart disorders.Machine learning methods are widely used to classify ECG signals. The aim of this work is to contribute to theclassification of ECG signals using the D
dc.description.abstract	Elektrokardiyogram (EKG) işaretlerinin hareketleri kalp hastalıklarının teşhisinde çok önemlidir. Makine öğrenme yöntemleri, EKG işaretlerini sınıflandırmak için yaygın olarak kullanılmaktadır. Bu çalışmanın amacı, Diferansiyel Evrim Algoritması Uç Öğre
dc.language.iso	English
dc.source	Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
dc.title	Ecg Signal Classification Technique Based On Deep Features Using Differential Evolution Algorithm Extreme Learning Machine (Dea-Elm)
dc.identifier.issue	3
dc.identifier.startpage	1364
dc.identifier.endpage	1376
dc.identifier.volume	9

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