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

Computational and Mathematical Methods in Medicine. 2012;2012 DOI 10.1155/2012/245213

 

Journal Homepage

Journal Title: Computational and Mathematical Methods in Medicine

ISSN: 1748-670X (Print); 1748-6718 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Raúl Alcaraz (Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Escuela Politécnica, Campus Universitario, 16071 Cuenca, Spain)
José J. Rieta (Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, 46730 Gandía, Spain)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 24 weeks

 

Abstract | Full Text

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 (f) 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.