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Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

2023-02Journal article Open access
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dc.contributor.authorPinto, Rui Joãoen
dc.contributor.authorSilva, Pedro Miguel
dc.contributor.authorDuarte, Rui P.
dc.contributor.authorMarinho, Francisco AlexandrePT
dc.contributor.authorPimenta, Luís
dc.contributor.authorGouveia, António Jorge
dc.contributor.authorGonçalves, N.J.A.P.
dc.contributor.authorCoelho, Paulo
dc.contributor.authorZdravevski, Eftim
dc.contributor.authorLameski, Petre
dc.contributor.authorLEITHARDT, VALDERI
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorPires, Ivan Miguel
dc.date.accessioned2023-03-17T11:27:04ZPT
dc.date.available2023-03-17T11:27:04ZPT
dc.date.issued2023-02PT
dc.date.updated2023-03-04T10:59:34ZPT
dc.description.abstractThe prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.pt_PT
dc.description.versionN/Apt_PT
dc.identifier.doi10.1016/j.heliyon.2023.e13601pt_PT
dc.identifier.issn2405-8440PT
dc.identifier.slugcv-prod-3140743PT
dc.identifier.urihttp://hdl.handle.net/10400.26/44204PT
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Center for Endogenous Resource Valorization
dc.titleAlgorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Center for Endogenous Resource Valorization
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05064%2F2020/PT
oaire.citation.startPagee13601pt_PT
oaire.citation.titleHeliyonpt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMonteiro Amaro Duarte
person.familyNamePimenta
person.familyNameGouveia
person.familyNameALVES PARENTE GONÇALVES
person.familyNameMachado Coelho
person.familyNameZdravevski
person.familyNameLameski
person.familyNameREIS QUIETINHO LEITHARDT
person.familyNameGarcia
person.familyNamePires
person.givenNameRui Pedro
person.givenNameLuís
person.givenNameAntónio Jorge Gonçalves de
person.givenNameNORBERTO JORGE
person.givenNamePaulo Manuel
person.givenNameEftim
person.givenNamePetre
person.givenNameVALDERI
person.givenNameNuno
person.givenNameIvan
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person.identifier.scopus-author-id14059938600
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid0614-5834-E7F3 | Valderi Reis Quietinho Leithardt
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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