Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Juan Ródenas; Manuel García; Raúl Alcaraz; José Rieta

doi:10.3390/e17096179

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Entropy ◽

10.3390/e17096179 ◽

2015 ◽

Vol 17 (12) ◽

pp. 6179-6199 ◽

Cited By ~ 32

Author(s):

Juan Ródenas ◽

Manuel García ◽

Raúl Alcaraz ◽

José Rieta

Keyword(s):

Atrial Fibrillation ◽

Wavelet Entropy

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/245213 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 11

Author(s):

Raúl Alcaraz ◽

José J. Rieta

Keyword(s):

Atrial Fibrillation ◽

Structural Changes ◽

Supraventricular Arrhythmia ◽

Wavelet Entropy ◽

Non Invasive ◽

Promising Tool ◽

Surface Ecg ◽

Disorder Degree ◽

Sensitivity Specificity ◽

Electrocardiogram Ecg

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.

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