Purpose: Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies.
Methods: We compared two methods to discriminate atrial electrograms (A-EGMs) with different degree of fractionation. First method (M-1) assessed the average interval between discrete A-EGM spikes detected based on peak-to-peak voltage sensitivity, signal width and refractory interval criteria (algorithm previously implemented in commercially available electroanatomic mapping system). Second method (M-2) employed simple non-parametric description of distribution of peak-to-peak atrial signal differences. Head-to-head comparison of both methods was performed using representative set of 1.5s A-EGMs (n = 113) ranked by an expert into 4 categories: 1 - organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation.
Results: Although input parameters of M-1 method were comprehensively optimized (peak-to-peak sensitivity of 0.02 mV, signal width of 8 ms, and refractory interval of 14 ms) with respect to particular experimental dataset, discriminative power of M-1 method to detect CFAEs was not superior to that provided by the newly introduced M-2 algorithm. Correlations between A-EGM categories and M-1 and M-2 indices of fractionation are shown in the Figure.
Conclusions: Novel method of A-EGMs classification offers operator-independent definition of electrogram complexity. It may easily be incorporated into real-time mapping systems to facilitate the CFAEs identification and guide the AF substrate ablation.
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