Local Activation Identification in Persistent Atrial Fibrillation Intracardiac EGM Signals for Automatic...

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, particularly in the elderly, increasing stroke risk.
• Catheter ablation (CA) is the most effective long-term treatment for persistent AF.
• Spatio-temporal dispersion (STD) is a novel CA approach targeting arrhythmia-sustaining active zones.

Purpose of the Study:

• To address the challenge of accurate peak detection in multipolar electrograms (EGM) for automatic STD pattern classification.
• To develop an explainable method for STD classification, mimicking interventional cardiologists' real-time visual analysis.
• To evaluate a new peak detection technique against existing methods for STD classification.

Main Methods:

• Development and comparison of a novel peak detection algorithm for intracardiac EGM signals.
• Evaluation of nine different peak detection techniques, including the proposed method, on real STD data.
• Analysis of peak identification accuracy within a mathematical pipeline for STD classification.

Main Results:

• Classical signal processing methods often fail to accurately detect peaks in challenging intracardiac EGM signals.
• The proposed peak detection method is a fundamental component for overcoming the STD classification problem.
• Improved classification accuracy for STD patterns compared to previous works was achieved.

Conclusions:

• Accurate peak detection is essential for the mathematical pipeline enabling STD classification.
• The novel peak detection approach enhances the accuracy of classifying STD patterns in AF.
• This work contributes to more effective catheter ablation strategies for persistent AF.