Quantifying the frequency modulation in electrograms during simulated atrial fibrillation in 2D domains

Area of Science:

• Cardiovascular Research
• Signal Processing
• Medical Physics

Background:

• Atrial fibrillation (AF) is a common arrhythmia causing significant mortality and morbidity.
• Current catheter ablation strategies for AF, particularly persistent AF, face challenges due to limitations in analyzing complex electrogram (EGM) signals.
• Existing methods often overlook the time-frequency varying nature of EGM components crucial for understanding AF substrates.

Purpose of the Study:

• To develop and validate a novel algorithm based on the fractional Fourier transform (FrFT) for analyzing non-stationary EGM signals during simulated atrial fibrillation.
• To enhance the characterization of arrhythmogenic substrates by capturing time-varying frequency components in EGM signals.
• To improve the efficacy of catheter ablation by providing better insights into AF mechanisms.

Main Methods:

• Development of a pre-processing step to enhance EGM waveform features.
• Implementation of a windowing process for dynamic EGM assessment.
• A fractional Fourier transform (FrFT) order optimization stage to identify compact signal representations and frequency modulation rates.
• Application of the FrFT algorithm to simulated AF episodes in 2D atrial tissue models.

Main Results:

• The FrFT-based algorithm successfully characterized non-stationary components in simulated AF EGM signals.
• Optimized FrFT orders were used to generate spatial maps correlated with AF propagation dynamics.
• Extreme values in the optimum orders map effectively localized fibrillatory mechanisms responsible for AF.

Conclusions:

• The FrFT-based algorithm offers a powerful tool for analyzing complex, time-varying EGM signals in atrial fibrillation.
• This approach enhances the understanding of localized arrhythmogenic substrates, potentially leading to more precise and effective catheter ablation.
• The study demonstrates the utility of signal processing techniques like FrFT in advancing cardiovascular research and clinical interventions for AF.