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dc.contributor.authorGarcia, Gabriel-
dc.contributor.authorMoreira, Gladston Juliano Prates-
dc.contributor.authorGomes, David Menotti-
dc.contributor.authorLuz, Eduardo José da Silva-
dc.identifier.citationGARCIA, G. et al. Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Scientific Reports, v. 7, p. 1-11, 2017. Disponível em: <>. Acesso em: 16 jan. 2018.pt_BR
dc.description.abstractClassifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.pt_BR
dc.titleInter-patient ECG heartbeat classification with temporal VCG optimized by PSO.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License. Fonte: Scientific Reports. <>. Acesso em: 04 ago 2017.pt_BR
Appears in Collections:DECOM - Artigos publicados em periódicos

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