title: Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination.
abstract: The aim of this work is to predict non-invasively if an AF episode terminates spontaneously or not by analyzing the increase of atrial activity organization prior to paroxysmal atrial fibrillation (PAF) termination. Sample entropy was selected as non-linear organization index. Synthetic PAF signals were used to evaluate the notable impact of noise in AA organization estimation. Three strategies to reduce noise, ventricular residues and enhance the atrial activity main features were proposed. The best prediction results were obtained through main atrial wave (MAW) organization estimation. The MAW can be considered as the fundamental waveform associated to the AA. The 92% of the terminating and non-terminating analyzed PAF episodes were correctly classified. Thereby, it can be concluded that the MAW non-linear analysis from the surface ECG is a reliable and useful tool to predict spontaneous PAF termination.
authors.affiliation: E.U. Politécnica de Cuenca, Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Campus Universitario, 16071 Cuenca, Spain. raul.alcaraz@uclm.es
authors.forename: R
authors.initials: R
authors.lastname: Alcaraz
authors.forename: J J
authors.initials: JJ
authors.lastname: Rieta
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology*; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods*; D019277:Entropy; D006325:Heart Atria / Q000503:physiopathology; D006329:Heart Conduction System / Q000503:physiopathology; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D012984:Software; D013997:Time Factors