Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9333
Title: Robust automated cardiac arrhythmia detection in ECG beat signals.
Authors: Albuquerque, Victor Hugo Costa de
Nunes, Thiago Monteiro
Pereira, Danillo Roberto
Luz, Eduardo José da Silva
Gomes, David Menotti
Papa, João Paulo
Tavares, João Manuel R. S.
Keywords: Electrophysiological signals
Cardiac dysrhythmia classification
Feature extraction
Pattern recognition
Issue Date: 2016
Citation: ALBUQUERQUE, V. H. C. de et al. Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Computing & Applications , v. 1, p. 1-15, 2016. Disponível em: <https://link.springer.com/article/10.1007/s00521-016-2472-8>. Acesso em: 16 jan. 2018.
Abstract: Nowadays, millions of people are affected by heart diseases worldwide, whereas a considerable amount of them could be aided through an electrocardiogram (ECG) trace analysis, which involves the study of arrhythmia impacts on electrocardiogram patterns. In this work, we carried out the task of automatic arrhythmia detection in ECG patterns by means of supervised machine learning techniques, being the main contribution of this paper to introduce the optimum-path forest (OPF) classifier to this context. We compared six distance metrics, six feature extraction algorithms and three classifiers in two variations of the same dataset, being the performance of the techniques compared in terms of effectiveness and efficiency. Although OPF revealed a higher skill on generalizing data, the support vector machines (SVM)-based classifier presented the highest accuracy. However, OPF shown to be more efficient than SVM in terms of the computational time for both training and test phases.
URI: http://www.repositorio.ufop.br/handle/123456789/9333
metadata.dc.identifier.uri2: https://link.springer.com/article/10.1007/s00521-016-2472-8
ISSN: 14333058
Appears in Collections:DECOM - Artigos publicados em periódicos

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