ECG Signals Classification Using Overlapping Variables to Detect Atrial Fibrillation

Authors

  • I. G. Ziccardi Centro de Procesamiento de Señales e Imágenes, Facultad Regional de Buenos Aires, Universidad Tecnológica Nacional, Argentina
  • A. A. Rey Centro de Procesamiento de Señales e Imágenes, Facultad Regional de Buenos Aires, Universidad Tecnológica Nacional, Argentina http://orcid.org/0000-0002-9185-1382
  • W. E. Legnani Centro de Procesamiento de Señales e Imágenes, Facultad Regional de Buenos Aires, Universidad Tecnológica Nacional, Argentina https://orcid.org/0000-0002-6949-0728

DOI:

https://doi.org/10.5540/tcam.2022.023.03.00569

Keywords:

Rényi entropy, statistical complexity, Fisher information, Lyapunov exponent, fractal dimension, atrial fibrillation, decision trees

Abstract

In the present work a method for the detection of the cardiac pathology known as atrial fibrillation is proposed by calculating different information, statistics and other nonlinear measures over ECG signals. The original database contains records corresponding to patients who are diagnosed with this disease as well as healthy subjects. To formulate the dataset the Rényi permutation entropy, Fisher information measure, statistical complexity, Lyapunov exponent and fractal dimension were calculated, in order to determine how to combine this features to optimize the identification of the signals coming from ECG with the above mentioned cardiac pathology. With the aim to improve the results obtained in previous studies, a classification method based upon decision trees algorithms is implemented. Later a Montecarlo simulation of one thousand trials is performed with a seventy percent randomly selected from the dataset dedicated to train the classifier and the remaining thirty percent reserved to test in every trial. The quality of the classification is assessed through the computation of the area under the receiver operation characteristic curve (ROC), the F1-score and other classical performance metrics, such as the balanced accuracy, sensitivity, specificity, positive and negative predicted values. The results show that the incorporation of all these features to the dataset when are employed to train the classifier in the training task produces the best classification, in such a way that the largest quality parameter is achieved.

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Published

2022-09-12

How to Cite

Ziccardi, I. G., Rey, A. A., & Legnani, W. E. (2022). ECG Signals Classification Using Overlapping Variables to Detect Atrial Fibrillation. Trends in Computational and Applied Mathematics, 23(3), 569–581. https://doi.org/10.5540/tcam.2022.023.03.00569

Issue

Section

Original Article