Lange, M, Lassila, T orcid.org/0000-0001-8947-1447 and Frangi, AF (2019) Predicting Plausible Human Purkinje Network Morphology from Simulations. In: Proceedings of the 2019 International Conference in Computing in Cardiology (CinC 2019). CinC 2019, 08-11 Sep 2019, Singapore. IEEE ISBN 9781728169361
Abstract
The Purkinje network (PN) gains more clinically importance as it becomes target for pacing in rate control and defibrillation. However, our understanding of the PN morphology arises from animal experiments, which might not transfer to humans. Therefore, we propose an automated computer simulation predicting physiological PN morphologies depending on the heart shape. It starts by generating virtual heart shapes from a statistical shape atlas and generates virtual PNs on the endocardial surface. For the combined virtual models the eikonal equation is solved to estimate the local activation times throughout the myocardium, which then feed forward to an simulation of the 12-lead surface ECG. From the simulated ECG the QRS-complex is compared against a healthy standard QRS-complex ,which allows to estimate how physiological a PN morphology is.
In our model, only bundle branch bifurcation points near the base or near the apex result in physiological QRS wave forms. For the right bundle, more physiological QRS waves can be obtained when the branching point is at the apex. Only a minor dependency of the ECG on the heart shape is found. However, a strong correlation between the bundle branch bifurcation points themselves is observed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This conference paper is protected by copyright. This is an open access paper under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jan 2020 10:05 |
Last Modified: | 27 Jan 2020 11:23 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.22489/cinc.2019.168 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156049 |