Measuring the complexity of atrial fibrillation electrograms

Area of Science:

• Electrophysiology
• Computational Cardiology
• Medical Signal Processing

Background:

• Complex fractionated atrial electrograms (CFAE) are key targets for atrial fibrillation (AF) ablation.
• Objective classification algorithms for CFAE are needed to improve ablation strategies.

Purpose of the Study:

• To evaluate Shannon's entropy (ShEn) and the Kolmogorov-Smirnov (K-S) test for classifying CFAE signals.
• To compare ShEn and K-S test performance against fractional intervals (FI).

Main Methods:

• Analysis of 297 electrogram recordings from 13 AF ablation patients.
• Classification of electrograms as CFAE or non-CFAE using Nademanee criteria by consensus of 4 reviewers.
• Assessment of FI, ShEn, and K-S test performance using sensitivity and specificity.

Main Results:

• Fractional intervals (FI) achieved 87% sensitivity and 79% specificity for CFAE detection.
• Shannon's entropy (ShEn) demonstrated 87% sensitivity and 81% specificity.
• The K-S test showed 100% sensitivity and 95% specificity in identifying uninterpretable electrograms.

Conclusions:

• ShEn offers comparable performance to FI in distinguishing CFAE from non-CFAE.
• ShEn provides an objective, automatic method for identifying CFAE sites for AF ablation.
• The K-S test effectively differentiates uninterpretable electrograms.