Logotipo do repositório
 

Publicação:
Robust automated cardiac arrhythmia detection in ECG beat signals

Carregando...
Imagem de Miniatura

Orientador

Coorientador

Pós-graduação

Curso de graduação

Título da Revista

ISSN da Revista

Título de Volume

Editor

Springer

Tipo

Artigo

Direito de acesso

Acesso abertoAcesso Aberto

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.

Descrição

Palavras-chave

ECG heart beats, Electrophysiological signals, Cardiac dysrhythmia classification, Feature extraction, Pattern recognition, Optimum-path forest

Idioma

Inglês

Como citar

Neural Computing & Applications. New York: Springer, v. 29, n. 3, p. 679-693, 2018.

Itens relacionados

Unidades

Departamentos

Cursos de graduação

Programas de pós-graduação