Robust automated cardiac arrhythmia detection in ECG beat signals
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Coadvisor
Graduate program
Undergraduate course
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Publisher
Springer
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Article
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Acesso aberto
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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.
Description
Keywords
ECG heart beats, Electrophysiological signals, Cardiac dysrhythmia classification, Feature extraction, Pattern recognition, Optimum-path forest
Language
English
Citation
Neural Computing & Applications. New York: Springer, v. 29, n. 3, p. 679-693, 2018.
