Non-stationary components in Electrograms localize arrhythmogenic substrates in a 3D model of human atria
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Catheter ablation, as a treatment for atrial fibrillation (AF), often yields low success rates in the advanced stages of the arrhythmia. Ablation procedures are guided by atrial mapping using electrogram (EGM) signals, which reflect local electrical activations. The primary goal is to identify arrhythmogenic mechanisms, such as rotors, to serve as ablation targets. Given the chaotic nature of AF propagation, these electrical activations occur at variable rates. This work introduces a novel signal processing approach based on the fractional Fourier transform (FrFT) to characterize the non-stationary content in EGM signals. A 3D biophysical and anatomical model of human atria was used to simulate AF, and unipolar EGMs were calculated. The FrFT-based algorithm was applied to all EGM signals, estimating the optimal FrFT order to capture linear frequency modulations. Electroanatomical maps of these optimal FrFT orders were generated. Results revealed that the AF EGMs exhibit non-stationarity, which can be characterized using the FrFT. Rotors displayed a distinct pattern of non-stationarity, allowing for dynamic tracking, while transient mechanisms were identifiable through variations in the FrFT order, showing different patterns than those of rotors. As a generalization of the classical Fourier analysis, FrFT mapping offers clinically interpretable insights into the rate of change in EGM frequency content over time. This method proves valuable for characterizing AF spatiotemporal dynamics by leveraging the non-stationary information inherent in fibrillatory propagation. © 2025 Elsevier Ltd
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Cardiac computational modeling, Electroanatomical maps, Fractional Fourier transform, Metaheuristics optimization, Non-stationary signals, Electrocardiograms, Fourier analysis, Fourier transforms, Frequency modulation, Atrial fibrillation, Cardiac computational modeling, Computational modelling, Electroanatomical map, Electrograms, Fractional Fourier transforms, Metaheuristic optimization, Nonstationary, Nonstationary signals, Transform order, Ablation