Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/7011
Title: ECG-based heartbeat classification for arrhythmia detection : a survey.
Authors: Luz, Eduardo José da Silva
Schwartz, William Robson
Cámara Chávez, Guillermo
Gomes, David Menotti
Keywords: ECG-based signal processing
Heartbeat classification
Preprocessing
Heartbeat segmentation
Feature extraction
Issue Date: 2016
Citation: LUZ, E. J. da S. et al. ECG-based heartbeat classification for arrhythmia detection: a survey. Computer Methods and Programs in Biomedicine, v. 127, p. 144-164, 2016. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0169260715003314>. Acesso em: 07 ago. 2016.
Abstract: An electrocardiogram (ECG) measures the electric activity of the heart and has been widelyused for detecting heart diseases due to its simplicity and non-invasive nature. By analyzingthe electrical signal of each heartbeat, i.e., the combination of action impulse waveformsproduced by different specialized cardiac tissues found in the heart, it is possible to detectsome of its abnormalities. In the last decades, several works were developed to produceautomatic ECG-based heartbeat classification methods. In this work, we survey the currentstate-of-the-art methods of ECG-based automated abnormalities heartbeat classificationby presenting the ECG signal preprocessing, the heartbeat segmentation techniques, thefeature description methods and the learning algorithms used. In addition, we describesome of the databases used for evaluation of methods indicated by a well-known standarddeveloped by the Association for the Advancement of Medical Instrumentation (AAMI) anddescribed in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitationsand drawbacks of the methods in the literature presenting concluding remarks and futurechallenges, and also we propose an evaluation process workflow to guide authors in futureworks.
URI: http://www.repositorio.ufop.br/handle/123456789/7011
metadata.dc.identifier.doi: https://doi.org/10.1016/j.cmpb.2015.12.008
ISSN: 0169-2607
metadata.dc.rights.license: O periódico Computer Methods and Programs in Biomedicine concede permissão para depósito deste artigo no Repositório Institucional da UFOP. Número da licença: 3926560893208.
Appears in Collections:DECOM - Artigos publicados em periódicos

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