Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization

• Published in: Information sciences Vol. 633; pp. 245 - 263
• Main Authors: Yuan, Shuaiying, He, Ziyang, Zhao, Jianhui, Yuan, Zhiyong, Alhudhaif, Adi, Alenezi, Fayadh
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2023
• Subjects: Electrocardiogram; Hypergraph; Myocardial infarction; Patient individual differences; Unsupervised domain adaptation; Hypergraph; Myocardial infarction; Unsupervised domain adaptation; Patient individual differences; Electrocardiogram
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2023.03.078

Solving individual differences between subjects is critical for the promotion of electrocardiogram (ECG) classification algorithms in the intelligent health monitoring industry. Popular inter-subject-based solutions usually require the manual labeling of heartbeats and frequent updating of the model for new subjects. To track these problems, we propose a hypergraph and cross-attention-based unsupervised domain adaptation (HGCA-UDA) framework for the myocardial infarction localization. Specifically, we first build a hypergraph-based dual-channel network, that can simultaneously learn specific feature representations from an ECG lead and disease category levels for samples from different domains. We then design a cross-attention module to align cross-domain locally similar samples. Subsequently, a domain alignment strategy based on the Wasserstein distance is proposed to align the global edge feature distribution. Finally, a pseudo-label generation scheme is proposed to further align fine-grained category information. We conduct extensive experiments on two public benchmark datasets (the Physikalisch-Technische Bundesanstalt (PTB) and PTB_XL database), and the results show that the proposed HGCR-UDA (with unlabeled patients) achieves comparable results compared with state-of-the-art inter-patient-based methods (with labeled patients) and has excellent applications prospects in the field of intelligent health monitoring.