Bidomain Sample Entropy to Predict Termination of Atrial Arrhythmias

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Therefore, the ability to predict if an AF episode terminates spontaneously or not is a challenging clinical problem. This work presents a robust AF prediction methodology carried out by estimating by applied sample entropy (SampEn) the atrial activity (AA) organization increase prior to AF termination. This regularity variation appears as a consequence of the decrease in the number of reentries into the atrial tissue. AA was obtained from surface ECG recordings using an average QRST template cancellation technique. Wavelet transform (WT) was used in a bidomain way (time and frequency) in order to improve organization estimation. Thereafter, a more robust and reliable classification process for terminating and non-terminating AF episodes was developed making use of two different wavefet decomposition strategies. Finally, the atrial activity organization both in time and wavelet domains (bidomain) was estimated. Trougth the application of this strategy 96% of the terminating and non-terminating analyzed AF episodes were correctly classified.