The electrocardiographic forward problem: A benchmark study. (July 2021)

• Record Type: Journal Article
• Authors: Bergquist, Jake A.; Good, Wilson W.; Zenger, Brian; Tate, Jess D.; Rupp, Lindsay C.; MacLeod, Rob S.
• Is Part Of: Computers in biology and medicine. Volume 134(2021)
• Publication Date: 2021-07
• Subjects: Electrocardiographic forward problem -- Electrocardiographic imaging -- Cardiac electrophysiology -- Experimental cardiac electrophysiology -- Experimental validation -- Cardiac bioelectricity; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104476
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Electrocardiographic 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. Objective: To test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors. Methods: We 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. Results: We 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. Conclusion: First, 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. Highlights: The electrocardiographic forward problem is an essential component of electrocardiographic imaging. The forward model, despite being solved, produces higher errors than expected in experimental validation studies. This study describes a benchmark experimental preparation for forward problem solution accuracy across multiple beat types. Errors reported previously could be explained by incomplete cardiac source sampling above the atrioventricular plane. We generated an experimental dataset to test and verify ECGI, which is publicly available on the EDGAR database.