Atlas-based methods for efficient characterization of patient-specific ventricular activation patterns.

Area of Science:

• Computational electrocardiology
• Biomedical signal processing
• Cardiac electrophysiology modeling

Background:

• Ventricular activation patterns offer spatial insights into cardiac electrical activity, aiding clinical decisions.
• Current methods for analyzing these patterns can be complex and computationally intensive.

Purpose of the Study:

• To create atlases of major ventricular activation variations from simulated data.
• To link these activation patterns to corresponding vectorcardiograms (VCGs).
• To explore how dimensionality reduction can enhance activation pattern estimation from surface electrograms.

Main Methods:

• Principal Component Analysis (PCA) was used on simulated 3D bi-ventricular activation time distributions from patient-specific geometries.
• Atlases of activation time (AT) and VCGs were derived.
• Sensitivity analysis was performed using a compact Jacobian.
• VCGs were generated by varying AT atlas modes to estimate patient-specific activation maps.

Main Results:

• Atlases provided significant dimensionality reduction, capturing similar variation features.
• The VCG atlas accurately reconstructed patient VCGs using fewer than 10 modes.
• Estimating patient-specific activation maps using the AT atlas resulted in lower errors than more computationally expensive methods.

Conclusions:

• Atlases of activation and VCGs offer a novel approach to analyze high-dimensional cardiac signals.
• These atlases can identify key variations between patients, potentially aiding in novel clinical index discovery.
• The findings may contribute to better risk stratification for arrhythmias and therapeutic outcome prediction.