ECG Compression method based on convolutional autoencoder and discrete wavelet transform

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dc.authorid0000-0002-0664-649Xen_US
dc.contributor.authorBekiryazıcı, Tahir
dc.contributor.authorGürkan, Hakan
dc.date.accessioned2021-03-20T20:26:51Z
dc.date.available2021-03-20T20:26:51Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413en_US
dc.description.abstractIn this work, a compression method based on one dimensional convolutional autocoder architecture and wavelet transform is proposed for the compression of ECG signals. The proposed method is tested on the MIT-BIH Arrhythmia Database and its performance is evaluated with respect to compression ratio (CR) and mean-independent percentage mean square difference (MPRD). Experimental results showed that the proposed method achieves an average CR value of 32.27:1 with an averages MPRD of %18.91. © 2020 IEEE.en_US
dc.identifier.doi10.1109/SIU49456.2020.9302056en_US
dc.identifier.isbn9781728172064
dc.identifier.scopus2-s2.0-85100296705en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttp://doi.org/10.1109/SIU49456.2020.9302056
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1288
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBekiryazıcı, Tahir
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectconvolutional auto-encoderen_US
dc.subjectdata compressionen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectECG signalsen_US
dc.titleECG Compression method based on convolutional autoencoder and discrete wavelet transformen_US
dc.title.alternativeEvrisimsel otokodlayici ve ayrik dalgacik donusumu tabanli EKG sikistirma yontemien_US
dc.typeConference Objecten_US

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