title: Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation.
abstract: Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. In the first stages of the disease, AF may terminate spontaneously and it is referred as paroxysmal atrial fibrillation (PAF). In this respect, the prediction of PAF termination or maintenance could avoid unnecessary therapy and contribute to take the appropriate decisions on its management. The aim of this work is to predict non-invasively the spontaneous termination of PAF episodes by analyzing the variation of atrial activity (AA) organization. The organization increases as a consequence of the decrease in the number of reentries wandering the atrial tissue before termination. The analysis has been carried out by applying sample entropy, which is a non-linear organization estimator, to surface electrocardiogram (ECG) recordings. Synthetic signals were used in order to evaluate the notable impact of noise in AA organization estimation. Therefore, to reduce noise, ventricular residues and enhance the fundamental features of AA, the main atrial wave (MAW) was extracted making use of selective filtering. Through MAW organization estimation applied to real ECGs, 95% (19 out of 20) of the learning PAF recordings and 90% (27 out of 30) of the test episodes were correctly predicted. As a consequence, the MAW organization analysis from surface ECGs can be considered as a promising tool to predict spontaneous PAF termination.
authors.affiliation: Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Campus Universitario, 16071 Cuenca, Spain. raul.alcaraz@uclm.es
authors.forename: Raúl
authors.initials: R
authors.lastname: Alcaraz
authors.forename: José J
authors.initials: JJ
authors.lastname: Rieta
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000473:pathology* / Q000503:physiopathology*; D004562:Electrocardiography; D006325:Heart Atria / Q000503:physiopathology; D006801:Humans; D017711:Nonlinear Dynamics