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Título : Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO.
Autor : Garcia, Gabriel
Moreira, Gladston Juliano Prates
Gomes, David Menotti
Luz, Eduardo José da Silva
Fecha de publicación : 2017
Citación : GARCIA, G. et al. Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Scientific Reports, v. 7, p. 1-11, 2017. Disponível em: <https://www.nature.com/articles/s41598-017-09837-3>. Acesso em: 16 jan. 2018.
Resumen : Classifying 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.
URI : http://www.repositorio.ufop.br/handle/123456789/9392
metadata.dc.identifier.doi: https://doi.org/10.1038/s41598-017-09837-3
ISSN : 2045-2322
metadata.dc.rights.license: Os trabalhos publicados no periódico Scientific Reports estão sob Licença Creative Commons que permite copiar, distribuir e transmitir o trabalho desde que sejam citados o autor e o licenciante. Fonte: Sherpa/Romeo <http://www.sherpa.ac.uk/romeo/search.php?issn=2045-2322>. Acesso em: 27 fev. 2020.
Aparece en las colecciones: DECOM - Artigos publicados em periódicos

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