Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination
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.
ecg organization paroxysmal atrial fibrillation atrial activity sample entropy main atrial wave wavelet transform