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Erschienen in: Neural Computing and Applications 3/2018

19.07.2016 | Original Article

Robust automated cardiac arrhythmia detection in ECG beat signals

verfasst von: Victor Hugo C. de Albuquerque, Thiago M. Nunes, Danillo R. Pereira, Eduardo José da S. Luz, David Menotti, João P. Papa, João Manuel R. S. Tavares

Erschienen in: Neural Computing and Applications | Ausgabe 3/2018

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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.

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Fußnoten
1
The edges are weighted by the distance between their corresponding samples/nodes.
 
2
For such purpose, we used the LibOPF library [24].
 
3
The recognition rates were computed using the standard formula, i.e., the ratio of the number of correct classifications by the number of database samples and H the harmonic mean between sensitivity and specificity.
 
4
SVM parameters were optimized through cross-validation procedure.
 
5
SVM implementation used was based on LIBSVM [4].
 
6
We have executed all techniques 10 times for statistical purposes.
 
7
LIBSVM implements the one-against-one method for multi-class tasks.
 
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Metadaten
Titel
Robust automated cardiac arrhythmia detection in ECG beat signals
verfasst von
Victor Hugo C. de Albuquerque
Thiago M. Nunes
Danillo R. Pereira
Eduardo José da S. Luz
David Menotti
João P. Papa
João Manuel R. S. Tavares
Publikationsdatum
19.07.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2472-8

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