A method for quantifying recurrent patterns of local wavefront direction during atrial fibrillation.

Area of Science:

• Cardiology
• Computational Biology
• Biomedical Engineering

Background:

• Atrial fibrillation (AF) mechanisms, specifically spiral wave reentry and multiple wavelet breakup, are difficult to distinguish using standard bipolar recordings.
• Existing clinical methods lack the precision to differentiate complex AF dynamics.

Purpose of the Study:

• To develop and validate a novel methodology for differentiating spiral wave reentry from wavelet breakup in AF using bipolar electrogram analysis.
• To assess the efficacy of Recurrence Quantification Analysis (RQA) applied to estimated wavefront direction (Egm-C) for distinguishing AF mechanisms.

Main Methods:

• A 2D computer simulation modeled regions of stable spiral wave reentry and wavelet breakup.
• A grid of unipolar electrodes recorded local intracellular signals to determine actual wavefront direction (WD).
• Electrogram conformation (Egm-C) was calculated from bipolar electrogram morphology to estimate wavefront direction, and RQA was applied to both Egm-C and WD.

Main Results:

• Both RQA of actual WD and Egm-C effectively differentiated spiral wave reentry from wavelet breakup.
• High correlations were observed between RQA of Egm-C and RQA of actual WD (recurrence rate, r=0.96; determinism, r=0.61; line max, r=0.95; entropy, r=0.84; p<0.001).
• Spiral wave reentry regions showed stable, periodic dynamics in recurrence plots, contrasting with the chaotic behavior in wavelet breakup regions.

Conclusions:

• Calculating Egm-C enables RQA on bipolar electrograms during AF.
• This new methodology successfully differentiates regions of spiral wave reentry from multiple wavelet breakup, offering a significant advancement in AF mechanism analysis.