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Congestive heart failure detection in ECG using LSTM and CNN

2025Documento de conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia
dc.contributor.authorRibeiro, Luiz
dc.contributor.authorGuerreiro, Nathan
dc.contributor.authorChaabani, Mohamed Khalil
dc.contributor.authorLuiz, Luiz E.
dc.contributor.authorLazzaretti, André
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2026-05-18T15:48:46Z
dc.date.available2026-05-18T15:48:46Z
dc.date.issued2025
dc.description.abstractCongestive Heart Failure (CHF) is a chronic condition inm which the heart does not pump blood efficiently. This pathology causes fatigue, dyspnea, oedema, nausea, and memory problems, affecting patients’ quality of life. The causes include coronary artery disease, cardiomyopathy, arterial hypertension, and myocarditis. The diagnosis is usually based on the patient’s medical history, physical exams, echocardiogram, electrocardiogram, and other methods. Aiming to improve diagnostic tools, this study proposes an artificial intelligence model based on deep learning to classify pathological ECG signals indicative of CHF. The selected models were LSTM and CNN. The training was conducted using a personalised dataset created from the public databases BIDMC Congestive Heart Failure and PTB Diagnostic ECG from Physionet. ECG data from 28 individuals aged 22 to 71 were selected, including 14 with severe CHF (NYHA class 3 and 4) and 14 control samples without ECG abnormalities. The database architecture was designed so that the input to the neural networks was raw ECG signals without filtering or feature extraction. The results showed an accuracy of 98.21% for the CNN model and 92.26% for the LSTM model.por
dc.description.sponsorshipThis work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).
dc.identifier.citationRibeiro, Luiz; Guerreiro, Nathan; Chaabani, Mohamed Khalil; Luiz, Luiz E.; Lazzaretti, André; Teixeira, João Paulo (2025). Congestive heart failure detection in ECG using LSTM and CNN. In International Conference on Demographic Transition, Health and Technologies, ICDTHT 2025. p. 147-156. ISSN 2198-7246. DOI: 10.1007/978-3-031-94901-2_12
dc.identifier.doi10.1007/978-3-031-94901-2_12
dc.identifier.issn2198-7246
dc.identifier.urihttp://hdl.handle.net/10198/36718
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature Switzerland
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
dc.relation.ispartofSpringer Proceedings in Business and Economics
dc.relation.ispartofHealth Technologies and Demographic Challenges
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleCongestive heart failure detection in ECG using LSTM and CNNpor
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.titleInternational Conference on Demographic Transition, Health and Technologies, ICDTHT 2025
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bcce
person.familyNameTeixeira
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
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