Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging-Reference-Cited by-同舟云学术

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Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging

Published:2024-08 Issue:8 Volume:43 Page:2733-2744
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Jiang Xiajun¹^ORCID,Missel Ryan¹,Toloubidokhti Maryam¹,Gillette Karli²^ORCID,Prassl Anton J.²^ORCID,Plank Gernot²^ORCID,Horáček B. Milan³^ORCID,Sapp John L.⁴^ORCID,Wang Linwei¹^ORCID

Affiliation:

1. Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA

2. Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria

3. School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada

4. QEII Health Sciences Centre and the Department of Medicine, Dalhousie University, Halifax, NS, Canada

Funder

BioTechMed-Graz under the ILearnHeart Flagship Project

Austrian Science Fund

NIH National Heart, Lung, and Blood Institute

National Institute of Nursing Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10619995/10471622.pdf?arnumber=10471622

Reference58 articles.

1. Physiological-Model-Constrained Noninvasive Reconstruction of Volumetric Myocardial Transmembrane Potentials

2. Forward Problem of Electrocardiography

3. The electrocardiographic inverse problem;Rudy;Crit. Rev. Biomed. Eng.,1992

4. Inverse electrocardiography by simultaneous imposition of multiple constraints

5. Application of L1-Norm Regularization to Epicardial Potential Solution of the Inverse Electrocardiography Problem

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