Geometry-Free Conditional Diffusion Modeling for Solving the Inverse Electrocardiography Problem

• URL: http://arxiv.org/abs/2601.18615v1
• Date: Mon, 26 Jan 2026 15:53:54 GMT
• Title: Geometry-Free Conditional Diffusion Modeling for Solving the Inverse Electrocardiography Problem
• Authors: Ramiro Valdes Jara, Adam Meyers,
• Abstract summary: We present a conditional diffusion framework that learns a probabilistic mapping from noisy body surface signals to heart surface electric potentials.<n>The proposed approach leverages the generative nature of diffusion models to capture the non-unique and underdetermined nature of the ECGI inverse problem.<n>We evaluate the method on a real ECGI dataset and compare it against strong deterministic baselines, including a convolutional neural network, long short-term memory network, and transformer-based model.
• Score: 0.5729426778193398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a data-driven model for solving the inverse problem of electrocardiography, the mathematical problem that forms the basis of electrocardiographic imaging (ECGI). We present a conditional diffusion framework that learns a probabilistic mapping from noisy body surface signals to heart surface electric potentials. The proposed approach leverages the generative nature of diffusion models to capture the non-unique and underdetermined nature of the ECGI inverse problem, enabling probabilistic sampling of multiple reconstructions rather than a single deterministic estimate. Unlike traditional methods, the proposed framework is geometry-free and purely data-driven, alleviating the need for patient-specific mesh construction. We evaluate the method on a real ECGI dataset and compare it against strong deterministic baselines, including a convolutional neural network, long short-term memory network, and transformer-based model. The results demonstrate that the proposed diffusion approach achieves improved reconstruction accuracy, highlighting the potential of diffusion models as a robust tool for noninvasive cardiac electrophysiology imaging.

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