Abstract
The accurate generation of forward models is an important element in general research in electrocardiography, and in particular for the techniques for ElectroCardioGraphic Imaging (ECGI). Recent research efforts have been devoted to the reliable and fast generation of forward models. However, these model can suffer from several sources of inaccuracy, which in turn can lead to considerable error in both the forward simulation of body surface potentials and even more so for ECGI solutions. In particular, the accurate localization of the heart within the torso is sensitive to movements due to respiration and changes in position of the subject, a problem that cannot be resolved with better imaging and segmentation alone. Here, we propose an algorithm to localize the position of the heart using electrocardiographic recordings on both the heart and torso surface over a sequence of cardiac cycles. We leverage the dependency of electrocardiographic forward models on the underlying geometry to parameterize the forward model with respect to the position (translation) and orientation of the heart, and then estimate these parameters from heart and body surface potentials in a numerical inverse problem. We show that this approach is capable of localizing the position of the heart in synthetic experiments and that it reduces the modeling error in the forward models and resulting inverse solutions in canine experiments. Our results show a consistent decrease in error of both simulated body surface potentials and inverse reconstructed heart surface potentials after re-localizing the heart based on our estimated geometric correction. These results suggest that this method is capable of improving electrocardiographic models used in research settings and suggest the basis for the extension of the model presented here to its application in a purely inverse setting, where the heart potentials are unknown.
Highlights
Subject-specific solutions to the forward problem of electrocardiography, that is, producing a mathematical model that can estimate body surface potential maps (BSPMs) from knowledge of cardiac electrical activity and an individualized thoracic volume conductor model, is important in a number of settings
Forward solutions depend on the underlying geometry, since it specifies the boundary conditions for the underlying partial differential equation, and even though this sensitivity is well-behaved, it is critical when solving the ElectroCardioGraphic Imaging (ECGI) inverse problem, since that inverse problem is ill-posed and very sensitive to errors in the forward model
Methods to improve forward modeling have received considerable attention in the ECGI community (Coll-Font et al, 2016a) and there is open discussion about, for example, what is the “best” forward model to use, which source models better characterize the electrical activity of the heart, how many, and which, organs should be included in the geometry, and how their respective conductivities should be estimated (Ferguson and Stroink, 1997; Ramanathan and Rudy, 2001a,b; Weber et al, 2011; Jones et al, 2013; Bear et al, 2015; Dehaghani, 2015; Potyagaylo et al, 2016; Punshchykova et al, 2016)
Summary
Subject-specific solutions to the forward problem of electrocardiography, that is, producing a mathematical model that can estimate body surface potential maps (BSPMs) from knowledge of cardiac electrical activity and an individualized thoracic volume conductor model, is important in a number of settings. These include tools for understanding and pedagogy about the ECG Tracking of Heart Position. Methods to improve forward modeling have received considerable attention in the ECGI community (Coll-Font et al, 2016a) and there is open discussion about, for example, what is the “best” forward model to use, which source models better characterize the electrical activity of the heart, how many, and which, organs should be included in the geometry, and how their respective conductivities should be estimated (Ferguson and Stroink, 1997; Ramanathan and Rudy, 2001a,b; Weber et al, 2011; Jones et al, 2013; Bear et al, 2015; Dehaghani, 2015; Potyagaylo et al, 2016; Punshchykova et al, 2016)
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