A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation

Abstract

Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify sites of CFAEs is crucial for the development of AF ablation strategies. A novel algorithm for automated description of fractionation of atrial electrograms (A-EGMs) based on the wavelet transform has been proposed. The algorithm was developed and validated using a representative set of 1.5 s A-EGM (n = 113) ranked by three experts into four categories: 1-organized atrial activity; 2-mild; 3-intermediate; 4-high degree of fractionation. A tight relationship between a fractionation index and expert classification of A-EGMs (Spearman correlation rho = 0.87) was documented with a sensitivity of 82% and specificity of 90% for the identification of highly fractionated A-EGMs. This operator-independent description of A-EGM complexity may be easily incorporated into mapping systems to facilitate CFAE identification and to guide AF substrate ablation.

Characterization of Complex Fractionated Atrial Electrograms by Sample Entropy: An International Multi-Center Study

“…Signals in C2 and C3 could indicate sites of substrate changes that could either trigger or sustain AF. This characterization is similar to those described in other works, such as [14,32].…”

“…Each database contains at least four classes of CFAE, each class representing a different fractionation level. Signals in both databases were preselected manually by skilled electrophysiologists (EP) to select high quality CFAE signals with no ventricular far field influence, no artifacts caused by obvious catheter movements and visually-detectable presence of signal noise [14,32]. These classes can also be roughly separated at a higher conceptual level into two main classes, non-fractionated (NF) A-EGM and fractionated (F) A-EGMs.…”

“…The first database was recorded and independently annotated by three different EP that perform radio frequency ablation (RFA) of AF on a regular basis in the Czech Republic [14]. The second one was recorded and annotated by two different EP working at a German unit [32].…”

“…This database was recorded during AF mapping procedures performed on patients that were indicated for RFA of AF [14].…”

“…To overcome this problem, several new approaches and methodologies have been developed so as to measure and quantify the complexity of A-EGM. Kremen et al [14] introduced a novel methodology based on several A-EGM measures extracted from the raw A-EGM signal. Combining the information provided by these measures, it enabled a better and robust enough classification, which was not possible with the use of one single A-EGM measure.…”