Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Robust automated cardiac arrhythmia detection in ECG beat signals

Texto completo
Autor(es):
de Albuquerque, Victor Hugo C. [1] ; Nunes, Thiago M. [2] ; Pereira, Danillo R. [3] ; Luz, Eduardo Jose da S. [4] ; Menotti, David [5] ; Papa, Joao P. [3] ; Tavares, Joao Manuel R. S. [6]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] Univ Fortaleza, Programa Posgrad Informat Aplicada, Lab Bioinformat, Fortaleza, CE - Brazil
[2] Univ Fortaleza, Ctr Ciencias Tecnol, Fortaleza, CE - Brazil
[3] Univ Estadual Paulista, Dept Ciencia Comp, Bauru, SP - Brazil
[4] Univ Fed Ouro Preto, Dept Comp, Ouro Preto, MG - Brazil
[5] Univ Fed Parana, Dept Informat, Curitiba, PR - Brazil
[6] Univ Porto, Fac Engn, Dept Engn Mecan, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Oporto - Portugal
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: NEURAL COMPUTING & APPLICATIONS; v. 29, n. 3, p. 679-693, FEB 2018.
Citações Web of Science: 20
Resumo

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. (AU)

Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
Beneficiário:João Paulo Papa
Linha de fomento: Auxílio à Pesquisa - Regular