Spatial-temporal hierarchical decoupled masked autoencoder: A self-supervised learning framework for electrocardiogram

The difficulty of labeling Electrocardiogram (ECG) has prompted researchers to use self-supervised learning to enhance diagnostic performance. Masked autoencoders (MAE) are a mainstream paradigm where models learn a latent representation of the signal by reconstructing masked portions of the ECG. Ho...

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Veröffentlicht in:	Expert systems with applications Jg. 298; S. 129603
Hauptverfasser:	Wei, Xiaoyang, Li, Zhiyuan, Tian, Yuanyuan, Wang, Mengxiao, Jin, Yanrui, Ding, Weiping, Liu, Chengliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2026
Schlagworte:	Electrocardiogram (ECG) Masked autoencoder Representation learning Self-supervised learning Representation learning Masked autoencoder Electrocardiogram (ECG) Self-supervised learning
ISSN:	0957-4174
Online-Zugang:	Volltext
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