Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 245213, 9 pages
Research Article

Application of Wavelet Entropy to Predict Atrial Fibrillation Progression from the Surface ECG

1Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Escuela Politécnica, Campus Universitario, 16071 Cuenca, Spain
2Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, 46730 Gandía, Spain

Received 28 May 2012; Revised 4 August 2012; Accepted 20 August 2012

Academic Editor: Thierry Busso

Copyright © 2012 Raúl Alcaraz and José J. Rieta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.