INTRODUCTION: Changes in conduction velocity are indicative of a wide variety of cardiac abnormalities yet measuring conduction velocity is challenging, especially within the myocardial volume. In this study we investigated a novel technique to reconstruct activation fronts and estimate three-dimensional (3D) conduction velocity (CV) from experimental intramural recordings. METHODS: From the intermittently sampled electrograms we both reconstruct the activation profile and compute the reciprocal of the gradient of activation times and a series of streamlines that allows for the CV estimation. RESULTS: The reconstructed activation times agreed closely with simulated values, with 50% to 70% of the nodes ≤ 1ms of absolute error. We found close agreement between the CVs calculated using reconstructed versus simulated activation times. Across the reconstructed stimulation sites we saw that the reconstructed CV was on average 3.8% different than the ground truth CV. DISCUSSION: This study used simulated datasets to validate our methods for reconstructing 3D activation fronts and estimating conduction velocities. Our results indicate that our method allows accurate reconstructions from sparse measurements, thus allowing us to examine changes in activation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs.
INTRODUCTION: Changes in conduction velocity are indicative of a wide variety of cardiac abnormalities yet measuring conduction velocity is challenging, especially within the myocardial volume. In this study we investigated a novel technique to reconstruct activation fronts and estimate three-dimensional (3D) conduction velocity (CV) from experimental intramural recordings. METHODS: From the intermittently sampled electrograms we both reconstruct the activation profile and compute the reciprocal of the gradient of activation times and a series of streamlines that allows for the CV estimation. RESULTS: The reconstructed activation times agreed closely with simulated values, with 50% to 70% of the nodes ≤ 1ms of absolute error. We found close agreement between the CVs calculated using reconstructed versus simulated activation times. Across the reconstructed stimulation sites we saw that the reconstructed CV was on average 3.8% different than the ground truth CV. DISCUSSION: This study used simulated datasets to validate our methods for reconstructing 3D activation fronts and estimating conduction velocities. Our results indicate that our method allows accurate reconstructions from sparse measurements, thus allowing us to examine changes in activation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs.
Authors: Brett M Burton; Kedar K Aras; Wilson W Good; Jess D Tate; Brian Zenger; Rob S MacLeod Journal: Ann Biomed Eng Date: 2018-05-21 Impact factor: 3.934
Authors: Jason Bayer; Anton J Prassl; Ali Pashaei; Juan F Gomez; Antonio Frontera; Aurel Neic; Gernot Plank; Edward J Vigmond Journal: Med Image Anal Date: 2018-02-02 Impact factor: 8.545
Authors: Wilson W Good; Burak Erem; Brian Zenger; Jaume Coll-Font; Dana H Brooks; Rob S MacLeod Journal: J Electrocardiol Date: 2018-08-13 Impact factor: 1.438
Authors: Aurel Neic; Fernando O Campos; Anton J Prassl; Steven A Niederer; Martin J Bishop; Edward J Vigmond; Gernot Plank Journal: J Comput Phys Date: 2017-10-01 Impact factor: 3.553