Comparative assessment of nonlinear metrics to quantify organization-related events in surface electrocardiograms of atrial fibrillation

Atrial fibrillation (AF) is today the most common sustained arrhythmia, its treatment being not completely satisfactory. Electrical activity organization analysis within the atria could play a key role in the improvement of current AF therapies. The application of a nonlinear regularity index, such as sample entropy (SampEn), to the atrial activity (AA) fundamental waveform has proven to be a successful noninvasive AF organization estimator. However, the use of alternative nonlinear metrics within this context is a pending issue. The present work analyzes the ability of several nonlinear indices to assess regularity of patterns and, thus, organization, in the AA signal and its fundamental waveform, defined as the main atrial wave (MAW). Precisely, Fuzzy Entropy, Spectral Entropy, Lempel-Ziv Complexity and Hurst Exponents were studied, achieving more robust and accurate AF organization estimates than SampEn. Results also provided better AF organization estimates from the MAW than from the AA signal for all the tested nonlinear metrics, which agrees with previous works only focused on SampEn. Furthermore, some of these indices reported a discriminant ability close to 95% in the classification of AF organization-dependent events, thus outperforming the diagnostic accuracy of SampEn and other widely used noninvasive estimators, such as the dominant atrial frequency (DAF). As a conclusion, these nonlinear metrics could be considered as promising estimators of noninvasive AF organization and could be helpful in making appropriate decisions on the patients' management.