On predicting the spontaneous termination of atrial fibrillation episodes using linear and nonlinear parameters of ECG signal and RR series

In this study, surface ECG signals recoded during atrial fibrillation (AF) episodes have been investigated to detect signs of spontaneous termination and to derive an automatic classifier of terminating (T) and nonterminating (N) AF events. The ECG signals consisted in Holier recordings coming from the 2004 Computers in Cardiology Challenge database, A set of features have been extracted from the ECG signals and the related RR interval series including both linear and nonlinear indexes. In the training dataset, we observed a prolonged dominant atrial cycle length (DACL) passing from N to T accompanied by an increased of residual ECG (rECG) power. Concerning the RR interval variability a reduction of mean RR interval and Regularity (R) and an increase of approximated entropy (ApEn) have been documented. These features were used to train a feed-forward neural network which was employed for the automatic classification of the challenge test set. Score of the classifier was encouraging: 26/30 episodes were correctly classified.