Recurrence quantification analysis of complex-fractionated electrograms differentiates active and passive sites...

Area of Science:

• Cardiology
• Computational Biology
• Biomedical Engineering

Background:

• Distinguishing active atrial fibrillation (AF) drivers from passive electrograms is challenging.
• Complex-fractionated atrial electrograms (CFAEs) are difficult to interpret due to overlapping signals.
• Current methods struggle to differentiate CFAEs originating from AF drivers versus bystander sites.

Purpose of the Study:

• To develop a method to differentiate electrograms from active AF drivers versus passive mechanisms.
• To test the hypothesis that active CFAE sites exhibit more recurrent wavefront directionality.
• To utilize nonlinear recurrence quantification analysis (RQA) to distinguish active from passive CFAEs.

Main Methods:

• Computer models of active (rotors) and passive (wavebreak, slow conduction) CFAE mechanisms were created.
• CFAEs were converted into discrete time-series representing electrogram conformation (Egm-C).
• RQA was applied to simulated and human AF CFAE signals to compare recurrence rates.

Main Results:

• Simulated active CFAE sites showed significantly higher Egm-C recurrence (31.8%) compared to passive sites (0.3%) (P<.0001).
• Human AF CFAE signals also demonstrated higher recurrence in active sites (13.6%) versus passive sites (0.1%) (P<.002).
• Recurrence differences were significant despite similar mean cycle lengths between active and passive CFAE sites.

Conclusions:

• Active CFAEs crucial for AF maintenance exhibit higher Egm-C recurrence.
• RQA effectively differentiates active CFAE sites from passive bystander sites.
• This method offers a potential tool for guiding AF ablation strategies.