Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Oliver Zettinig , Tommaso Mansi , Bogdan Georgescu , Elham Kayvanpour , Farbod Sedaghat-Hamedani , Ali Amr , Jan Haas , Henning Steen , Benjamin Meder , Hugo Katus , Nassir Navab , Ali Kamen , Dorin Comaniciu

ABSTRACT

Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in ≈3s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5ms for QRS duration and 2° for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization. More... »

PAGES

1-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-40811-3_1

DOI

http://dx.doi.org/10.1007/978-3-642-40811-3_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037009715

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24505642


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