Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder

Most existing electrocardiogram (ECG) feature extraction methods rely on rule-based approaches. It is difficult to manually define all ECG features. We propose an unsupervised feature learning method using a convolutional variational autoencoder (CVAE) that can extract ECG features with unlabeled da...

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Detalles Bibliográficos
Autores principales: Jang, Jong-Hwan, Kim, Tae Young, Lim, Hong-Seok, Yoon, Dukyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635334/
https://www.ncbi.nlm.nih.gov/pubmed/34852002
http://dx.doi.org/10.1371/journal.pone.0260612

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635334/
https://www.ncbi.nlm.nih.gov/pubmed/34852002
http://dx.doi.org/10.1371/journal.pone.0260612

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