| Full text | |
| Author(s): |
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]
Total Authors: 7
|
| Affiliation: | [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
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | NEURAL COMPUTING & APPLICATIONS; v. 29, n. 3, p. 679-693, FEB 2018. |
| Web of Science Citations: | 20 |
| 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. (AU) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |