Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910216096841728 |
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| author | Ogbomo-Harmitt, Shaheim Magnetti, Cesare Grzelak, Jakub Aslanidi, Oleg |
| author_facet | Ogbomo-Harmitt, Shaheim Magnetti, Cesare Grzelak, Jakub Aslanidi, Oleg |
| contents | Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is highly disorganised. Conventional physics-based forward models require explicit specification of intracellular conductivity tensors, which are not directly measurable in clinical practice and introduce structural modelling errors. This proof-of-concept study presents a deep learning approach that learns a direct mapping from left atrial intracellular electrical potentials to far-field ECGs without requiring explicit intracellular conductivity inputs at inference time. Despite training only on 74 subjects, the model achieved an R2 of 0.949 \pm 0.037, highlighting potential to reduce structural uncertainty and improve non-invasive AF assessment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_13366 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor Ogbomo-Harmitt, Shaheim Magnetti, Cesare Grzelak, Jakub Aslanidi, Oleg Computer Vision and Pattern Recognition Machine Learning Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is highly disorganised. Conventional physics-based forward models require explicit specification of intracellular conductivity tensors, which are not directly measurable in clinical practice and introduce structural modelling errors. This proof-of-concept study presents a deep learning approach that learns a direct mapping from left atrial intracellular electrical potentials to far-field ECGs without requiring explicit intracellular conductivity inputs at inference time. Despite training only on 74 subjects, the model achieved an R2 of 0.949 \pm 0.037, highlighting potential to reduce structural uncertainty and improve non-invasive AF assessment. |
| title | Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.13366 |