ArticleComputers in biology and medicine2021
The electrocardiographic forward problem: A benchmark study.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging.Medical & biological engineering & computing · 2025Pooled it
- An integrated platform for 2-D and 3-D optical and electrical mapping of arrhythmias in Langendorff-perfused rabbit hearts.The Journal of physiology · 2026Article
- A Review of the Forward Problem in Electrocardiographic Imaging.Journal of imaging · 2026Review
- Impact of rigid cardiac motion on the accuracy of electrocardiographic imaging.Frontiers in physiology · 2025Article
- Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models.Frontiers in physiology · 2023Article
- Investigation into the importance of using natural PVCs and pathological models for potential-based ECGI validation.Frontiers in physiology · 2023Article
- Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data.Frontiers in physiology · 2023Article
- Reconstruction of cardiac position using body surface potentials.Computers in biology and medicine · 2022Article
- Body Surface Potential Mapping: Contemporary Applications and Future Perspectives.Hearts (Basel, Switzerland) · 2021Article
- Novel Experimental Preparation to Assess Electrocardiographic Imaging Reconstruction Techniques.Computing in cardiology · 2020Article
- Improving Localization of Cardiac Geometry Using ECGI.Computing in cardiology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundElectrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high.
objectiveTo test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors.
methodsWe used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling.
resultsWe studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane.
conclusionFirst, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.