Authors: Eduardo J. Da S. Luz; Luiz H.C. Merschmann; David Menotti; Gladston J.P. Moreira
Addresses: Department of Computing, Federal University of Ouro Preto, Ouro Preto, MG, Brazil ' Department of Computing, Federal University of Ouro Preto, Ouro Preto, MG, Brazil ' Department of Informatics, Federal University of Paraná, Curitiba, PR, Brazil ' Department of Computing, Federal University of Ouro Preto, Ouro Preto, MG, Brazil
Abstract: Several types of arrhythmias that can be rare and harmless, but may result in serious cardiac issues, and several ECG analysis methods have been proposed in the literature to automatically classify the various classes of arrhythmias. Following the Association for the Advancement of Medical Instrumentation (AAMI) standard, 15 classes of heartbeats can be hierarchically grouped into five superclasses. In this work, we propose to employ the hierarchical classification paradigm to five ECG analysis methods in the literature, and compare their performance with flat classification paradigm. In our experiments, we use the MIT-BIH Arrhythmia Database and analyse the use of the hierarchical classification following AAMI standard and a well-known and established evaluation protocol using five superclasses. The experimental results showed that the hierarchical classification provided the highest gross accuracy for most of the methods used in this work and provided an improvement in classification performance of N and SVEB superclasses.
Keywords: biomedical engineering; ECG signals; electrocardiograms; heartbeats; arrhythmias; hierarchical classification; heartbeat classification; bioinformatics.
International Journal of Bioinformatics Research and Applications, 2017 Vol.13 No.2, pp.146 - 160
Accepted: 25 Jul 2016
Published online: 21 Mar 2017 *