Non-invasive estimation of atrial fibrillation driver position using long-short term memory neural networks and body surface potentials-Reference-Cited by-同舟云学术

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Non-invasive estimation of atrial fibrillation driver position using long-short term memory neural networks and body surface potentials

Published:2024-04 Issue: Volume:246 Page:108052
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Gutiérrez-Fernández-Calvillo Miriam^ORCID,Cámara-Vázquez Miguel Ángel^ORCID,Hernández-Romero Ismael^ORCID,Guillem María S.^ORCID,Climent Andreu M.^ORCID,Fambuena-Santos Carlos^ORCID,Barquero-Pérez Óscar^ORCID

Funder

Instituto de Salud Carlos III

Universidad Rey Juan Carlos

Comunidad de Madrid Consejeria de Educacion Ciencia y Universidades

EIT Health

España Ministerio de Ciencia Innovación y Universidades

Publisher

Elsevier BV

Reference36 articles.

1. A deep learning approach for real-time detection of atrial fibrillation;Andersen;Expert Syst. Appl.,2019

2. Deep learning formulation of ecgi for data-driven integration of spatiotemporal correlations and imaging information;Bacoyannis,2019

3. Relating epicardial to body surface potential distributions by means of transfer coefficients based on geometry measurements;Barr;IEEE Trans. Biomed. Eng.,1977

4. Advantages and pitfalls of noninvasive electrocardiographic imaging;Bear;J. Electrocardiol.,2019

5. Accurate detection of atrial fibrillation from 12-lead ecg using deep neural network;Cai;Comput. Biol. Med.,2020

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