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Title: ECG arrhythmia classification based on optimum-path forest.
Authors: Luz, Eduardo José da Silva
Nunes, Thiago Monteiro
Albuquerque, Victor Hugo Costa de
Papa, João Paulo
Gomes, David Menotti
Keywords: Feature extraction
Optimum path forest
Support vector machine
Issue Date: 2013
Citation: LUZ, E. J. da S. et al. ECG arrhythmia classification based on optimum-path forest. Expert Systems with Applications, v. 40, p. 3561-3573, jul. 2013. Disponível em: <>. Acesso em: 22 jan. 2015.
Abstract: An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graphbased pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.
ISSN: 0957-4174
metadata.dc.rights.license: O periódico Expert Systems with Applications concede permissão para depósito do artigo no Repositório Institucional da UFOP. Número da licença: 3552530332283.
Appears in Collections:DECOM - Artigos publicados em periódicos

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