Key result
New visualization techniques, including confidence maps, level-sets, and topology-based visualizations, effectively quantified and illustrated source localization uncertainty in electrocardiographic imaging simulations.
Why the study?
To help increase the effectiveness of electrocardiographic imaging (ECGI) by providing new ways to visualize associated measurement and modeling errors.
Novel visualization techniques, including confidence maps and level-sets, offer a new approach to studying and understanding uncertainty in the electrocardiographic imaging pipeline.
Answers are grounded in this paper and cite their sources.
New ECGI uncertainty visualizations warrant cautious arrhythmia mapping; leaves open clinical adoption pending validation.
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 (Electrocardiographic imaging source localization). Monte Carlo simulations and visualization techniques (confidence maps, level-sets, topology-based visualizations) was evaluated on Source localization uncertainty. New visualization techniques, including confidence maps, level-sets, and topology-based visualizations, effectively quantified and illustrated source localization uncertainty in electrocardiographic imaging simulations.
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