The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases

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dc.contributor.authorSoumiaa, Mhamed-Amine
dc.contributor.authorElhabbari, Sara
dc.contributor.authorMansouri, Mohamed
dc.coverage.issue2cs
dc.coverage.volume28cs
dc.date.accessioned2023-01-13T06:21:07Z
dc.date.available2023-01-13T06:21:07Z
dc.date.issued2022-12-20cs
dc.description.abstractCardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular diseases: Atrial fibrillation (AF), 1st degree atrioventricular block (AV), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature ventricular contraction (PVC), Premature atrial contraction (PAC), ST segment depression (STD), and ST segment elevation (STE). The ECGs are preprocessed, and different features are extracted using Multi-level Discrete Wavelet Transform. The model is trained on a database of more than 6000 electrocardiograms which includes 9 types of 12-lead ECGs: a normal type and the 8 abnormal ones which correspond to the diseases mentioned above.en
dc.formattextcs
dc.format.extent62-66cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2022 vol. 28, č. 2, s. 62-66. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2022.2.062en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3815
dc.identifier.urihttp://hdl.handle.net/11012/208749
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/200cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectElectrocardiogramen
dc.subjectECGen
dc.subjectCardiologyen
dc.subjectDeep learningen
dc.subjectArtificial Neural Networksen
dc.subjectClassificationen
dc.subjectDiagnosisen
dc.subjectAutomationen
dc.subjectDiscrete Wavelet Transformen
dc.subjectSignal Processingen
dc.titleThe Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseasesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 28, No. 2