Waveform characterization of atrial fibrillation using phase information.

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Atrial fibrillation (AF) is a complex arrhythmia characterized by irregular atrial activity.
• Accurate characterization of f-wave morphology is crucial for understanding AF mechanisms.
• Existing methods may lack the granularity to capture diverse f-wave patterns.

Purpose of the Study:

• To introduce a novel signal processing method for detailed f-wave morphology characterization in AF.
• To establish a quantitative approach for analyzing f-wave patterns using harmonic phase relationships.
• To demonstrate the method's ability to classify and identify distinct f-wave morphologies.

Main Methods:

• Decomposition of atrial activity into fundamental and harmonic components.
• Estimation of amplitude, frequency, and phase for short signal blocks.
• Utilizing harmonic phase relationships as key features for morphology analysis.
• Clustering of estimated f-waves into typical morphologic patterns.

Main Results:

• The method successfully distinguishes a wide variety of f-wave morphologies.
• Demonstrated efficacy on simulated signals, patient ECGs during organized AF, and drug-induced AF.
• Identified typical morphologic patterns applicable for further AF analysis.

Conclusions:

• The novel method provides a robust framework for characterizing f-wave morphology in AF.
• Harmonic phase relationships offer valuable insights into the underlying atrial activity.
• This approach facilitates the establishment of standardized AF morphology patterns for clinical and research purposes.