Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

In recent years, a wide variety of Machine Learning (ML) algorithms, including Deep Learning (DL) methods, have been proposed for electrocardiogram (ECG) beat classification. However, accurately discerning ECG beat types faces challenges due to noise interference and inherent imbalances among differ...

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Bibliographic Details
Main Authors: Alba Vadillo-Valderrama, Jacobo Chaquet-Ulldemolins, Rebeca Goya-Esteban, Raul P. Caulier-Cisterna, Juan Jose Sanchez-Munoz, Arcadi Garcia-Alberola, Jose Luis Rojo-Alvarez
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Electrocardiography
machine learning
unsupervised learning
neural networks
dimensionality reduction
manifold learning
Online Access:https://ieeexplore.ieee.org/document/10418234/

Internet

https://ieeexplore.ieee.org/document/10418234/

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