Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling

[2502.11378] Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2502.11378 (eess) [Submitted on 17 Feb 2025] Title: Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling Authors: Lingzhen Zhu , Kenneth Bilchick , Jianxin Xie View a PDF of the paper titled Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling, by Lingzhen Zhu and 2 other authors View PDF HTML (experimental) Abstract: Electrocardiographic imaging aims to noninvasively reconstruct the electrical dynamic patterns on the heart surface from body-surface ECG measurements, aiding the mechanistic study of cardiac function. At the core of ECGI lies the inverse ECG problem, a mathematically ill-conditioned challenge where small body measurement errors or noise can lead to significant inaccuracies in the reconstructed heart-surface potentials. %Leveraging a well-developed electrophysiological (EP) model, our previous study developed an EP-informed deep learning framework, demonstrating promising effectiveness in improving cardiac map predictions. To improve the accuracy of ECGI and ensure that cardiac predictions adhere to established physical principles, recent advances have incorporated well-established electrophysiology (EP) laws into their model formulations. However, traditional EP-informed models encounter significant challenges, including overfitting to EP constraints, limitations in network scalability, and suboptimal initialization. These issues compromise prediction accuracy and stability, hindering their effectiveness in practical applications. This highlights the need for an advanced data analytic and predictive tool to achieve reliable cardiac electrodynamic restoration. Here, we present a Numerical Differentiation-based Electrophysiology-Aware Adaptive Residual neural Network (EAND-ARN) for robust inverse ECG modeling. Our method employs numerical differentiation to compute the spatiotemporal derivative, enabling EP constraints to be applied across a local spatiotemporal region, thereby strengthening the overall EP enforcement. Additionally, we design an adaptive residual network to improve gradient flow, enhancing predictive accuracy and mitigating issues with poor initialization. Experimental results show that EAND-ARN significantly outperforms existing methods in current practice. Subjects: Signal Processing (eess.SP) Cite as: arXiv:2502.11378 [eess.SP] (or arXiv:2502.11378v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2502.11378 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1038/s41598-025-15687-1 Focus to learn more DOI(s) linking to related resources Submission history From: Jianxin Xie [ view email ] [v1] Mon, 17 Feb 2025 02:49:09 UTC (7,221 KB) Full-text links: Access Paper: View a PDF of the paper titled Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling, by Lingzhen Zhu and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2025-02 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Faster and More Robust CK Reaction Rate Estimation at 3T Using Acquisition-Weighted &lt;sup&gt;31&lt;/sup&gt;P Cardiac 1D-MRSI With Compartment-Based Reconstruction#475792
• Physics-Informed Neural Networks for Solving Physical Partial Differential Equations in Aerospace Engineering#99137
• Single-channel EEG-based seizure prediction using deep learning.#221081
• Large-scale neuromorphic modeling of cortical networks on FPGA for investigating anesthetic-induced neural dynamics.#678631
• RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models#639671