Non-linear organization analysis of paroxysmal atrial fibrillation.
Atrial fibrillation (AF) is a common supraventricular arrhythmia with episodes that, in the first stages of the disease, may terminate spontaneously. This fact is referred as paroxysmal atrial fibrillation. The analysis of its termination or maintenance could avoid unnecessary therapy and contribute to take the appropriate decisions on its management. The aim of this work is to study if an AF episode terminates spontaneously or not by analyzing the increase of atrial activity (AA) organization prior to AF termination. The organization varies as a consequence of the decrease in the number of reentries into the atrial tissue. The analysis was carried out noninvasively through the use of surface electrocardiogram (ECG) recordings. Sample entropy was selected as non-linear organization index. It was observed that noise and ventricular residues degrade AA organization estimation performance, therefore the use of selective filtering to get the main atrial wave (MAW) was necessary. Using the MAW organization analysis, that is the signal produced by the main reentry wandering the atrial tissue, 46 out of 50 of the terminating and non-terminating analyzed AF episodes were correctly classified (92%). The obtained outcomes allow to conclude that the dominant atrial frequency, and therefore, the main atrial reentry, contains the most relevant information about spontaneous AF termination.
Alcaraz
Raúl
R
Innovation in Bioengeeniering Research Group, University of Castilla-La Mancha, Campus Universitario, 16071, Cuenca, Spain. raul.alcaraz@uclm.es
Rieta
José J
JJ
D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods; D022062:Electrophysiologic Techniques, Cardiac / Q000295:instrumentation; D019277:Entropy; D006325:Heart Atria / Q000473:pathology*; D006329:Heart Conduction System; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors