Comparative study of nonlinear metrics to discriminate atrial fibrillation events from the surface ECG

The atrial activity (AA) of surface ECGs in atrial fibrillation (AF) has been studied to evaluate and compare the ability of several nonlinear metrics in the discrimination of AF events by estimating their regularity. The AA signals were extracted by applying an adaptive QRST can-celation method. Next, the study of surrogate data was performed and revealed the nonlinear behavior of AA. The following nonlinear metrics were studied: Sample Entropy (SampEn), Fuzzy Entropy, Spectral Entropy, Lempel-Ziv Complexity, Hurst Exponents, Generalized Hurst Exponents and AA average power. The Dominant Atrial Frequency (DAF) was added to the study as a reference. To reduce noise and ventricular residues, the Main Atrial Wave (MAW) was obtained applying a selective filtering to the AA signal centered on the DAF. Next, the MAW regularity was estimated using the aforementioned parameters. As conclusion, the study of MAW regularity improved results obtained from direct AA regularity estimation in all the analyzed metrics. Furthermore, some of them achieved better results than SampEn, a widely used metric in the classification of organization-related events in AF. Therefore, they could be considered as promising tools in the quantification of AF events from the surface ECG.