Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces-Reference-Cited by-同舟云学术

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Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

Published:2024 Issue: Volume:12 Page:27501-27517
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Vadillo-Valderrama Alba¹^ORCID,Chaquet-Ulldemolins Jacobo¹^ORCID,Goya-Esteban Rebeca¹^ORCID,Caulier-Cisterna Raúl P.²^ORCID,Sánchez-Muñoz Juan José³^ORCID,García-Alberola Arcadi³,Rojo-Álvarez José Luis¹^ORCID

Affiliation:

1. Department of Signal Theory and Communications and Telematic Systems and Computation, Rey Juan Carlos University, Fuenlabrada, Madrid, Spain

2. Department of Informatics and Computing, Faculty of Engineering, Universidad Tecnológica Metropolitana, Santiago, Chile

3. Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, Murcia, Spain

Funder

Research Grants meHeart, LATENTIA, and PCardioTools

Agencia Nacional de Investigación y Desarrollo (ANID) Fondecyt Postdoctorado 2021

Autonomous Community of Madrid [European Laboratory for Learning and Intelligent Systems (ELLIS) Madrid Node]

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10418234.pdf?arnumber=10418234

Reference46 articles.

1. Differential Beat Accuracy for ECG Family Classification Using Machine Learning

2. A survey on ECG analysis

3. Holter Monitoring and Loop Recorders: From Research to Clinical Practice

4. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

5. Applications of Machine Learning in Ambulatory ECG

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