Learning geometry-dependent lead-field operators for forward ECG modeling
#ECG modeling #Lead-field operators #Machine learning #Anatomical accuracy #Torso representation #Electrocardiogram #Geometry-dependent #Data-limited settings
📌 Key Takeaways
- Researchers developed a new ML approach for ECG modeling using geometry-dependent lead-field operators
- The method achieves high accuracy in approximating lead fields with minimal computational cost
- The framework consists of geometry-encoding and neural surrogate components
- The approach can be deployed in data-limited clinical settings
- It outperforms existing methods while preserving anatomical fidelity
📖 Full Retelling
Researchers Arsenii Dokuchaev, Francesca Bonizzoni, Stefano Pagani, Francesco Regazzoni, and Simone Pezzuto published a new machine learning approach for electrocardiogram (ECG) modeling on the arXiv preprint server on February 25, 2026, addressing the challenge of achieving high anatomical accuracy in torso representation for ECG simulations, which has been difficult in clinical practice due to imaging protocols typically focusing only on the heart. The research introduces a shape-informed surrogate model of the lead-field operator that can replace full-order models in forward ECG simulations. Their framework consists of two main components: a geometry-encoding module that maps anatomical shapes into a low-dimensional latent space, and a geometry-conditioned neural surrogate that predicts lead-field gradients from spatial coordinates, electrode positions, and latent codes. This innovative approach achieves high accuracy in approximating lead fields both within the torso and inside the heart, resulting in highly accurate ECG simulations with a relative mean squared error of less than 2.5%. The traditional lead-field method, while enabling fast ECG simulations while preserving geometric fidelity, has limitations in computational cost that scales linearly with the number of electrodes, restricting its use in high-density recording settings. The new method addresses these limitations by consistently outperforming the widely used pseudo lead-field approximation while maintaining negligible inference cost, making it particularly valuable for clinical applications where computational resources and detailed torso imaging may be limited.
🏷️ Themes
Medical Technology, Machine Learning, Computational Modeling, Healthcare Innovation
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Electrocardiography
Examination of the heart's electrical activity
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Machine learning
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Original Source
--> Computer Science > Machine Learning arXiv:2602.22367 [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 Abstract: Modern forward electrocardiogram 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 geometry-conditioned neural surrogate that predicts lead-field gradients from spatial coordinates, electrode positions and latent codes. The proposed method achieves high accuracy in approximating lead fields both within the torso (mean angular error 5°) and inside the heart, resulting in highly accurate ECG simulations (relative mean squared error <2.5%. The surrogate consistently outperforms the widely used pseudo lead-field approximation while preserving negligible inference cost. Owing to its compact latent represe...
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