Detection Algorithm of Phase Singularity Using Phase Variance Analysis for Epicardial Optical Mapping Data.

Area of Science:

• Cardiovascular Physiology
• Computational Biology
• Medical Imaging

Background:

• Spiral reentry is a significant cause of tachycardia.
• Accurate detection and tracking of spiral wave cores are crucial for understanding cardiac arrhythmias.
• Current kernel convolution methods for phase singularity (PS) detection yield numerous false positives, hindering stable tracking.

Purpose of the Study:

• To develop a novel algorithm for precise detection and tracking of phase singularities (PS) in cardiac optical mapping.
• To improve the accuracy and stability of identifying spiral wave cores compared to existing methods.

Main Methods:

• Developed a new algorithm that quantifies phase variance in phase maps.
• Identifies the phase singularity (PS) as the peak in the phase variance distribution.
• Applied the method to cardiac optical mapping images.

Main Results:

• The new algorithm achieved a detection precision of 99.8% for single sustaining spiral wave cores, a significant improvement from the previous 73.1%.
• Demonstrated higher precision in detecting multiple PSs induced by virtual electrode polarization compared to the convolutional method.
• The method effectively identifies PS by locating peaks in the phase variance of optical mapping images.

Conclusions:

• The proposed phase variance algorithm offers a more precise and stable method for detecting spiral wave cores in cardiac optical mapping.
• This advancement provides a better tool for analyzing spiral wave dynamics, crucial for understanding and potentially treating serious heart arrhythmias.