Discrimination of endocardial electrogram disorganization using a signal regularity analysis

Area of Science:

• Cardiology
• Biomedical Engineering
• Nonlinear Dynamics

Background:

• Complex fractionated atrial electrograms (CFAEs) represent a complex cardiac signal.
• Understanding the dynamic behavior of CFAEs is crucial for diagnosing and treating atrial arrhythmias.
• Current methods for characterizing CFAE dynamics are limited.

Purpose of the Study:

• To apply nonlinear dynamics measures to characterize the physiological dynamic behavior of CFAEs.
• To assess the disorganization of CFAE signals using quantitative methods.
• To correlate signal complexity with specific nonlinear dynamics measures.

Main Methods:

• Analysis of 113 short-term atrial electrograms (A-EGMs) annotated into four fractionation classes.
• Application of nonlinear dynamics measures: General Correlation Dimension, Approximate Entropy, Detrended Fluctuation Analysis, Lempel-Ziv Complexity, Katz-Sevcik, Variance, and Box Counting Fractal Dimension.
• Statistical evaluation of signal disorganization using the Kruskal Wallis test.

Main Results:

• CFAE disorganization was statistically significant (p < 0.001) for most applied measures, excluding Detrended Fluctuation Analysis and Variance Fractal Dimension.
• Higher signal complexity in A-EGMs corresponded to increased values of General Correlation Dimension (order 1) and Approximate Entropy.
• The study successfully quantified the disorganization present in CFAEs.

Conclusions:

• Nonlinear dynamics measures are effective in characterizing the physiological dynamic behavior and disorganization of CFAEs.
• Increased complexity of A-EGM signals, as indicated by higher General Correlation Dimension and Approximate Entropy, reflects greater disorganization.
• These findings provide a quantitative framework for analyzing CFAEs, potentially aiding in the diagnosis and management of atrial arrhythmias.