Key result
A new phase variance analysis algorithm improved the precision of detecting a single sustaining spiral wave core from 73.1% to 99.8% compared with the conventional kernel convolution method.
Why the study?
Does a phase variance analysis algorithm improve the precision of phase singularity detection in epicardial optical mapping data compared to the kernel convolution method?
Population
Epicardial optical mapping data (cardiac optical mapping images) used to study spiral reentry
Comparison
New phase singularity detection algorithm using… vs Conventional kernel convolution method
Design
Preclinical
Authors
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Enhances spiral wave detection in animal optical mapping; leaves open translation to clinical arrhythmia studies.
Does a phase variance analysis algorithm improve the precision of phase singularity detection in epicardial optical mapping data compared to the kernel convolution method?
Absolute Event Rate: 99.8% vs 73.1%
A novel phase variance analysis algorithm significantly improves the precision of detecting spiral wave cores in cardiac optical mapping, which may aid in understanding the mechanisms of serious heart arrhythmias.
Tomii et al. (2015) studied Spiral reentry / Heart arrhythmias. Phase variance analysis algorithm vs. Kernel convolution method was evaluated on Precision of detecting a single sustaining spiral wave core. A new phase variance analysis algorithm improved the precision of detecting a single sustaining spiral wave core from 73.1% to 99.8% compared with the conventional kernel convolution method.
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