Robust automated cardiac arrhythmia detection in ECG beat signals

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018-02

Autores

Victor Hugo C. de Albuquerque (Autor) Outra; Thiago M. Nunes (Autor) Outra; Danillo R. Pereira (Autor) Outra; Eduardo José da S. Luz (Autor) Outra; David Menotti (Autor) Outra; João P. Papa (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Neural Computing and Applications

Vol. 29 Nº 3

Páginas: 679-693

ISSN: 0941-0643

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 17 Citações

ISI Web of Science

Scopus - 42 Citações

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-00K-N5W

DOI: 10.1007/s00521-016-2472-8

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15