ECG Based Assessment of the Heart Position in Standard Torso Model

Svehlikova, Jana; Lenkova, Jana; Drkosova, A.; Foltin, Martin; Tyšler, Milan

doi:10.1007/978-3-642-23508-5_123

Cited by 1 publication

(1 citation statement)

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“…Closer to our work, there have been a few reports attempting to track the changes in position of the heart using BSPM. Shvelikhova et al estimated the vertical position of the heart by characterizing its electrical activity with a moving dipole whose position was tracked from the ECG (Svehlikova et al, 2011). Recently, Rodrigo et al proposed to pre-compute a set of candidate forward models and then used a metric derived from the L-curve in Tikhonov regularization to select the “best” candidate forward model (Rodrigo et al, 2017, 2018).…”

Section: Introductionmentioning

confidence: 99%

Tracking the Position of the Heart From Body Surface Potential Maps and Electrograms

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Brooks

2018

Front. Physiol.

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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.

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Section: Introductionmentioning

confidence: 99%