Key result
Multiple visualization techniques, including confidence maps, level-sets, and topology-based visualizations, were developed to better understand and quantify uncertainty in electrocardiographic imaging source localization.
Why the study?
To help increase the effectiveness of ECGI, new ways were needed to visualize associated measurement and modeling errors.
The study provides new visualization techniques to quantify and understand uncertainty in electrocardiographic imaging source localization.
Answers are grounded in this paper and cite their sources.
May refine ECGI arrhythmia mapping amid localization uncertainty; leaves open prospective clinical validation of error visualizations.
Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualize associated measurement and modeling errors. In this paper, we study source localization uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localization model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualization techniques, including confidence maps, level-sets, and topology-based visualizations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.
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Njeru et al. (2022) studied Arrhythmias (simulated). Uncertainty visualization techniques (confidence maps, level-sets, topology-based visualizations) was evaluated on Source localization uncertainty. Multiple visualization techniques, including confidence maps, level-sets, and topology-based visualizations, were developed to better understand and quantify uncertainty in electrocardiographic imaging source localization.
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