Fast data-driven calibration of a cardiac electrophysiology model from images and ECG
Zettinig O, Mansi T, Georgescu B, Kayvanpour E, Sedaghat-Hamedani F, Amr A, Haas J, Steen H, Meder B, Katus H, Navab N, Kamen A, Comaniciu D (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8149 LNCS
Pages Range: 1-8
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: JPN
ISBN: 9783642408106
DOI: 10.1007/978-3-642-40811-3_1
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. © 2013 Springer-Verlag.
Involved external institutions
Siemens Corporate Research
United States (USA) (US)
Universitätsklinikum Heidelberg
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
United States (USA) (US)
Universitätsklinikum Heidelberg
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Zettinig, O., Mansi, T., Georgescu, B., Kayvanpour, E., Sedaghat-Hamedani, F., Amr, A.,... Comaniciu, D. (2013). Fast data-driven calibration of a cardiac electrophysiology model from images and ECG. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1-8). JPN.
MLA:
Zettinig, Oliver, et al. "Fast data-driven calibration of a cardiac electrophysiology model from images and ECG." Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, JPN 2013. 1-8.
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