Robust Transmural Electrophysiological Imaging: Integrating Sparse and Dynamic Physiological Models into ECG-Based Inference
Open accessAbstract
Noninvasive inference of patient-specific intramural electrical activity from surface electrocardiograms (ECG) lacks a unique solution in the absence of prior assumptions. While 3D cardiac electrophysiological models emerged to be a viable vehicle for constraining this inference with knowledge about the spatiotemporal dynamics of cardiac excitation, it is important for the inference to be robust to errors in these high-dimensional model predictions given the limited ECG data. We present an innovative solution to this problem by exploiting the low-dimensional structure of the solution space – a powerful regularizer in overcoming the lack of measurements – within the dynamic inference guided by physiological models. We present the first Bayesian inference framework that allows the exploration of both the spatial sparsity of cardiac excitation and its complex nonlinear spatiotemporal dynamics for an improved inference of patient-specific intramural electrical activity. The benefit of this integration is verified in both synthetic and real-data experiments, where we present one of the first detailed, point-by-point comparison of the reconstructed electrical activity to in-vivo catheter mapping data. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Incorporating theory-guided spatial distributions as priors along with externally collected ECG data in a hierarchical Bayesian model provides promising results over traditional data science models.
“Incorporating such theory-guided spatial distributions as priors and using it along with externally collected ECG data in a hierarchical Bayesian model has been shown to provide promising results over traditional data science models [26], [27].”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [27]
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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752 · see citing works
- Authors
- Xu, J., Sapp, J. L., Dehaghani, A. R., Gao, F., Horacek, M., & Wang, L.
- Affiliations
- Rochester Institute of Technology (US)
- Dalhousie University (CA)
- Siemens Healthcare (United States) (US)
- Corresponding author
- Xu, Jingjia
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 519-527
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2398062891
- Cited by
- 15
- References
- 12
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 3 · 2020: 2 · 2019: 2 · 2018: 1 · 2017: 1 · 2015: 2
- FWCI
- 3.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion, cc-by)
- Funders
- National Science Foundation (CAREER, 1350374)
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Keywords
- electrocardiogram, Bayesian inference, patient-specific cardiac modeling, catheter mapping
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