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- W2794471364 abstract "Respiratory motion is known to cause beat-to-beat variation of the ECG. This observation suggests that it may be possible to use this variation to track position and orientation of the heart. Electrocardiographic Imaging (ECGI) would benefit from such a reconstruction since one contribution to errors in its solutions is respiratory motion of the heart. ECGI solutions generally rely on prior computation of a forward model that relates cardiac electrical activity to ECGs. However, the ill-posed nature of the inverse solution leads to large errors in ECGI even for small amounts of error in the forward model. The current work is a first step towards reducing those errors using a nominal forward model and the ECG itself. We describe a method that can reconstruct cardiac position / orientation using known potentials on both the heart and torso. Our current implementation is based on Bayesian Optimization and efficiently optimizes for the position / orientation of the heart to minimize error between measured and forward-computed torso potentials. We evaluated our approach with synthesized torso potentials under a model of respiratory motion and also using potentials recorded in a tank experiment on a canine epicardium and the tank surfaces. Our results show that our method performs accurately in synthetic experiments and can account for part of the error between forward-computed and measured ECGs in the tank experiments." @default.
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- W2794471364 date "2017-09-14" @default.
- W2794471364 modified "2023-09-23" @default.
- W2794471364 title "ECG-Based Reconstruction of Heart Position and Orientation with Bayesian Optimization" @default.
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- W2794471364 doi "https://doi.org/10.22489/cinc.2017.054-387" @default.
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