Key result
The ensemble average transform identified synthetic driver frequencies embedded in complex fractionated atrial electrograms more accurately than Fourier analysis in the presence of noise (p ≤ 0.005).
p-value: p=≤0.005
The ensemble average transform provides a more robust and efficient method than Fourier analysis for identifying dominant frequencies and morphologic features of complex fractionated atrial electrograms during atrial fibrillation.
May improve driver identification in noisy CFAE signals; leaves open impact on AF ablation outcomes.
BACKGROUND: Representation of independent biophysical sources using Fourier analysis can be inefficient because the basis is sinusoidal and general. When complex fractionated atrial electrograms (CFAE) are acquired during atrial fibrillation (AF), the electrogram morphology depends on the mix of distinct nonsinusoidal generators. Identification of these generators using efficient methods of representation and comparison would be useful for targeting catheter ablation sites to prevent arrhythmia reinduction. METHOD: A data-driven basis and transform is described which utilizes the ensemble average of signal segments to identify and distinguish CFAE morphologic components and frequencies. Calculation of the dominant frequency (DF) of actual CFAE, and identification of simulated independent generator frequencies and morphologies embedded in CFAE, is done using a total of 216 recordings from 10 paroxysmal and 10 persistent AF patients. The transform is tested versus Fourier analysis to detect spectral components in the presence of phase noise and interference. Correspondence is shown between ensemble basis vectors of highest power and corresponding synthetic drivers embedded in CFAE. RESULTS: The ensemble basis is orthogonal, and efficient for representation of CFAE components as compared with Fourier analysis (p ≤ 0.002). When three synthetic drivers with additive phase noise and interference were decomposed, the top three peaks in the ensemble power spectrum corresponded to the driver frequencies more closely as compared with top Fourier power spectrum peaks (p ≤ 0.005). The synthesized drivers with phase noise and interference were extractable from their corresponding ensemble basis with a mean error of less than 10%. CONCLUSIONS: The new transform is able to efficiently identify CFAE features using DF calculation and by discerning morphologic differences. Unlike the Fourier transform method, it does not distort CFAE signals prior to analysis, and is relatively robust to jitter in periodic events. Thus the ensemble method can provide a useful alternative for quantitative characterization of CFAE during clinical study.
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Ciaccio et al. (2011) studied Atrial fibrillation (n=20). Ensemble average transform vs. Fourier transform was evaluated on Correspondence of top spectral peaks to synthetic driver frequencies in the presence of phase noise and interference (p=≤0.005). The ensemble average transform identified synthetic driver frequencies embedded in complex fractionated atrial electrograms more accurately than Fourier analysis in the presence of noise (p ≤ 0.005).
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