Cargando…
Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning fr...
Autores principales: | Chen, Ke-Wei, Bear, Laura, Lin, Che-Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951148/ https://www.ncbi.nlm.nih.gov/pubmed/35336502 http://dx.doi.org/10.3390/s22062331 |
Ejemplares similares
-
Design of Loss Functions for Solving Inverse Problems Using Deep Learning
por: Rivera, Jon Ander, et al.
Publicado: (2020) -
Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators
por: Evangelista, Davide, et al.
Publicado: (2023) -
Discretization of Learned NETT Regularization for Solving Inverse Problems
por: Antholzer, Stephan, et al.
Publicado: (2021) -
Association of lifestyle with deep learning predicted electrocardiographic age
por: Zhang, Cuili, et al.
Publicado: (2023) -
Discriminating electrocardiographic responses to His-bundle pacing using machine learning
por: Arnold, Ahran D., et al.
Publicado: (2020)