Evaluating fluctuations in human atrial fibrillatory cycle length using monophasic action potentials

Area of Science:

• Electrophysiology
• Cardiac Arrhythmias
• Signal Processing in Medicine

Background:

• Identifying short atrial fibrillation (AF) cycle length (CL) sites is crucial for ablation targets, as cycle lengthening predicts success.
• Optimal methods for measuring AF CL and its stability remain unclear, necessitating advanced signal analysis techniques.
• Spectral dominant frequency (DF) may be prone to double counting, while autocorrelation offers a potentially more accurate estimation method.

Purpose of the Study:

• To investigate fluctuations in intracardiac atrial fibrillation (AF) cycle length (CL).
• To compare the accuracy and stability of autocorrelation versus spectral dominant frequency (DF) for AF CL measurement.
• To determine the optimal duration for stable AF CL assessment.

Main Methods:

• Analysis of 49 AF epochs from 28 patients using monophasic action potentials (MAPs) from the high (HRA) and low (LRA) right atrium.
• Estimation of AF CL using spectral DF and autocorrelation over varying durations (2 seconds, 10 seconds, 2 minutes) in MAPs and filtered bipoles.
• Comparison of measurement accuracy and error rates between spectral DF and autocorrelation methods.

Main Results:

• Autocorrelation demonstrated superior accuracy in estimating both MAP (R=0.92) and bipolar (R=0.83) AF CL compared to spectral DF.
• Spectral DF showed poor correlation with bipolar signals (R=0.31) but good correlation with MAPs (R=0.73), indicating susceptibility to double counting.
• Changes in DF consistently preceded reciprocal changes in organization, while AF CL measurements were stable when analyzed over 10 seconds or longer.

Conclusions:

• Autocorrelation is a more reliable method for estimating AF CL than spectral DF, particularly for bipolar signals.
• AF CL measurements are stable and reliable when analyzed over durations exceeding 10 seconds.
• Observed fluctuations indicate that AF CL changes precede organization changes, offering insights into AF dynamics.