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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| Vydané v: | Expert systems with applications Ročník 298; s. 129603 |
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| Jazyk: | English |
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01.03.2026
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| ISSN: | 0957-4174 |
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| Abstract | 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. However, existing methods lack a specific design for the spatial–temporal characteristics of ECG. Specifically, leads represent spatial projections of cardiac activity, while timestamps capture temporal patterns, and the two correspond to different axes of information. Existing MAE frameworks tend to unify them prematurely, potentially weakening critical local dependencies. In this paper, we propose a Spatial-Temporal Hierarchical Decoupled Masked Autoencoder (STHD-MAE). This framework decouples ECG into isolated leads or time steps in the shallow layer to capture local dependencies with different views, then aligns spatial–temporal representations and re-establishes global dependencies in the deep layer to comprehensively represent pathological information. We also design a medical report fusion module during pre-training, which uses cross-attention to align the ECG report text encoded by a medical language model with the signal’s latent representation, thereby guiding the encoder to focus on pathological information through implicit cross-modal learning. We validate the effectiveness of STHD-MAE on multiple downstream classification and reconstruction tasks. The results show that STHD-MAE outperforms existing self-supervised learning methods by approximately 2% in F1-scores for both coarse-grained and fine-grained classification performance, and its reconstruction quality also exceeds the baseline generative model. |
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| AbstractList | 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. However, existing methods lack a specific design for the spatial–temporal characteristics of ECG. Specifically, leads represent spatial projections of cardiac activity, while timestamps capture temporal patterns, and the two correspond to different axes of information. Existing MAE frameworks tend to unify them prematurely, potentially weakening critical local dependencies. In this paper, we propose a Spatial-Temporal Hierarchical Decoupled Masked Autoencoder (STHD-MAE). This framework decouples ECG into isolated leads or time steps in the shallow layer to capture local dependencies with different views, then aligns spatial–temporal representations and re-establishes global dependencies in the deep layer to comprehensively represent pathological information. We also design a medical report fusion module during pre-training, which uses cross-attention to align the ECG report text encoded by a medical language model with the signal’s latent representation, thereby guiding the encoder to focus on pathological information through implicit cross-modal learning. We validate the effectiveness of STHD-MAE on multiple downstream classification and reconstruction tasks. The results show that STHD-MAE outperforms existing self-supervised learning methods by approximately 2% in F1-scores for both coarse-grained and fine-grained classification performance, and its reconstruction quality also exceeds the baseline generative model. |
| ArticleNumber | 129603 |
| Author | Ding, Weiping Wang, Mengxiao Jin, Yanrui Li, Zhiyuan Wei, Xiaoyang Tian, Yuanyuan Liu, Chengliang |
| Author_xml | – sequence: 1 givenname: Xiaoyang orcidid: 0009-0005-8775-6875 surname: Wei fullname: Wei, Xiaoyang email: victor0926@sjtu.edu.cn organization: School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China – sequence: 2 givenname: Zhiyuan orcidid: 0000-0003-1323-6795 surname: Li fullname: Li, Zhiyuan email: lzy2030@sjtu.edu.cn organization: School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China – sequence: 3 givenname: Yuanyuan orcidid: 0000-0003-4432-1546 surname: Tian fullname: Tian, Yuanyuan email: tian102@sjtu.edu.cn organization: School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China – sequence: 4 givenname: Mengxiao orcidid: 0000-0002-4979-0723 surname: Wang fullname: Wang, Mengxiao email: mengxiaowang@sjtu.edu.cn organization: School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China – sequence: 5 givenname: Yanrui orcidid: 0000-0001-9489-5447 surname: Jin fullname: Jin, Yanrui email: jinyanrui@sjtu.edu.cn organization: School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China – sequence: 6 givenname: Weiping surname: Ding fullname: Ding, Weiping email: ding.wp@ntu.edu.cn organization: School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China – sequence: 7 givenname: Chengliang surname: Liu fullname: Liu, Chengliang email: chlliu@sjtu.edu.cn organization: School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China |
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| Keywords | Representation learning Masked autoencoder Electrocardiogram (ECG) Self-supervised learning |
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| Title | Spatial-temporal hierarchical decoupled masked autoencoder: A self-supervised learning framework for electrocardiogram |
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