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[2602.22367] Learning geometry-dependent lead-field operators for forward ECG modeling
Summary
This article presents a novel approach to forward electrocardiogram (ECG) modeling using geometry-dependent lead-field operators, enhancing accuracy and efficiency in simulations.
Why It Matters
The research addresses significant challenges in ECG modeling, particularly the need for high anatomical fidelity and computational efficiency. By proposing a surrogate model that reduces data requirements while maintaining accuracy, this work has implications for clinical practices where detailed torso imaging is often limited.
Key Takeaways
- Introduces a shape-informed surrogate model for ECG simulations.
- Achieves high accuracy in lead-field approximations with minimal data.
- Outperforms traditional pseudo lead-field methods in efficiency.
- Reduces the need for detailed torso segmentation in clinical settings.
- Maintains low computational costs while ensuring high-fidelity simulations.
Computer Science > Machine Learning arXiv:2602.22367 (cs) [Submitted on 25 Feb 2026] Title:Learning geometry-dependent lead-field operators for forward ECG modeling Authors:Arsenii Dokuchaev, Francesca Bonizzoni, Stefano Pagani, Francesco Regazzoni, Simone Pezzuto View a PDF of the paper titled Learning geometry-dependent lead-field operators for forward ECG modeling, by Arsenii Dokuchaev and 4 other authors View PDF HTML (experimental) Abstract:Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the torso domain. The lead-field method enables fast ECG simulations while preserving full geometric fidelity. Achieving high anatomical accuracy in torso representation is, however, challenging in clinical practice, as imaging protocols are typically focused on the heart and often do not include the entire torso. In addition, the computational cost of the lead-field method scales linearly with the number of electrodes, limiting its applicability in high-density recording settings. To date, no existing approach simultaneously achieves high anatomical fidelity, low data requirements and computational efficiency. In this work, we propose a shape-informed surrogate model of the lead-field operator that serves as a drop-in replacement for the full-order model in forward ECG simulations. The proposed framework consists of two components: a geometry-encoding module that maps anatomical shapes into a low-dimensional latent space, and a geomet...
