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Binary classification of cardiac pathologies using deep learning: a PTB-XL dataset approach

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datacite.subject.fosEngenharia e Tecnologia
dc.contributor.authorChaabani, Mohamed
dc.contributor.authorGuerreiro, Nathan
dc.contributor.authorRibeiro, Luiz
dc.contributor.authorLuiz, Luiz E.
dc.contributor.authorSlim, Mohamed
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2026-05-18T15:25:10Z
dc.date.available2026-05-18T15:25:10Z
dc.date.issued2025
dc.description.abstractCardiovascular diseases, such as myocardial infarction, are among the leading causes of death worldwide. Accuracy and time are crucial for diagnosing these conditions and for effective treatment, usually requiring time-consuming manual analysis of clinical-grade electrocardiogram (ECG). This paper presents a novel deep learning-based method for binary classification of cardiac patholo-gies using the PTB-XL dataset. The model integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies be-tween leads. Preprocessing involves converting RGBA ECG images to RGB for-mat and normalizing them to meet the requirements of the inputs of the layers. Initial experiments have shown promising results, achieving an AUC (Area Un-der the Curve) of 86.06%. Further work includes tests to optimize the model's performance based on different key metrics, including accuracy and precision.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.citationChaabani, Mohamed; Guerreiro, Nathan; Ribeiro, Luiz; Luiz, Luiz E.; Slim, Mohamed; Teixeira, João P.aulo (2025). Binary classification of cardiac pathologies using deep learning: a PTB-XL dataset approach. In International Conference on Demographic Transition, Health and Technologies, ICDTHT 2025. Salinas. p. 83-92. ISBN 978-303194900-5DOI: 10.1007/978-3-031-94901-2_7
dc.identifier.doi10.1007/978-3-031-94901-2_7
dc.identifier.isbn978-303194900-5
dc.identifier.urihttp://hdl.handle.net/10198/36715
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.titleBinary classification of cardiac pathologies using deep learning: a PTB-XL dataset approachpor
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.endPage92
oaire.citation.startPage83
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
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