mlsanityData to 14 Oct 2026

Physics-constrained Deep Learning for Robust Inverse ECG Modeling

arXiv 2107.12780 ↗First posted 26 Jul 2021cs.LGcited by 12

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Abstract (arXiv)

The rapid developments in advanced sensing and imaging bring about a data-rich environment, facilitating the effective modeling, monitoring, and control of complex systems. For example, the body-sensor network captures multi-channel information pertinent to the electrical activity of the heart (i.e., electrocardiograms (ECG)), which enables medical scientists to monitor and detect abnormal cardiac conditions. However, the high-dimensional sensing data are generally complexly structured and realizing the full data potential depends to a great extent on advanced analytical and predictive methods. This paper presents a physics-constrained deep learning (P-DL) framework for high-dimensional inverse ECG modeling. This method integrates the physical laws of the complex system with the advanced deep learning infrastructure for effective prediction of the system dynamics. The proposed P-DL approach is implemented to solve the inverse ECG model and predict the time-varying distribution of electric potentials in the heart from the ECG data measured by the body-surface sensor network. Experimental results show that the proposed P-DL method significantly outperforms existing methods that are commonly used in current practice.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 16 of its 87 references that are in the map. First posted on arXiv 26 Jul 2021.

Builds on

16 of 87 references.

References · 16

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4 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 3; 2026: 3.

12 papers.

  1. 2023Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inferencecited by 18
  2. 2022Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signalscited by 15
  3. 2024Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Surveycited by 14
  4. 2024MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Recordscited by 4
  5. 2025Geometry-aware Active Learning of Spatiotemporal Dynamic Systemscited by 3
  6. 2025Full-field surrogate modeling of cardiac function encoding geometric variabilitycited by 3
  7. 2024The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimationcited by 1
  8. 2025Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamicscited by 1
  9. 2024Physically Informed Synchronic-adaptive Learning for Industrial Systems Modeling in Heterogeneous Media with Unavailable Time-varying Interfacecited by 0
  10. 2026Chain of Flow: ECG-Conditioned 4D Cardiac Cine Generation from Patient-Specific Anatomical Anchorcited by 0
  11. 2026Multi-Modal Machine Learning for Breast Cancer Recurrence Predictioncited by 0
  12. 2026P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolutioncited by 0