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.
Keyphrases
- monte carlo
- end stage renal disease
- high resolution
- randomized controlled trial
- ejection fraction
- left ventricular
- systematic review
- chronic kidney disease
- molecular dynamics
- left atrial
- drinking water
- patient safety
- mass spectrometry
- patient reported
- patient reported outcomes
- mitral valve
- single molecule
- data analysis