A software platform for the comparative analysis of electroanatomic and imaging data including conduction velocity...

Area of Science:

• Cardiac Electrophysiology
• Computational Cardiology
• Medical Imaging Analysis

Background:

• Electroanatomic mapping systems generate vast datasets of electrical activity.
• Understanding the mechanisms sustaining atrial fibrillation requires detailed data analysis.
• Post-operative scrutiny of mapping data can reveal fibrillation drivers.

Purpose of the Study:

• To develop a modular software platform for post-processing electroanatomic mapping data.
• To analyze data using novel algorithms, including conduction velocity (CV) mapping.
• To investigate the mechanistic properties underlying atrial fibrillation.

Main Methods:

• Developed a modular software platform for data analysis.
• Integrated existing and novel algorithms for electroanatomic data.
• Implemented a conduction velocity (CV) mapping algorithm to visualize wavefronts.
• Overlaid imaging data of scar regions for comparative analysis.

Main Results:

• The software platform enables rapid post-operative analysis of electroanatomic mapping data.
• Conduction velocity (CV) mapping effectively highlights wavefront behavior.
• CV mapping results were sensitive to triangulation point distribution and activation times.
• Geometric conditions were defined for optimal triangulation in CV map generation.

Conclusions:

• The developed platform facilitates in-depth analysis of electroanatomic mapping data.
• CV mapping is a valuable tool for understanding atrial fibrillation mechanisms.
• Optimized triangulation is essential for accurate CV mapping and identifying fibrillation drivers.