A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification

Electrocardiogram (ECG) is an effective non-invasive tool that can detect arrhythmias. Recently, deep learning (DL) has been widely used in ECG classification algorithms. However, differences between subjects lead to data shifts, hindering the further extension of DL algorithms. To solve this proble...

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Vydáno v:	Expert systems with applications Ročník 221; s. 119711
Hlavní autoři:	He, Ziyang, Chen, Yufei, Yuan, Shuaiying, Zhao, Jianhui, Yuan, Zhiyong, Polat, Kemal, Alhudhaif, Adi, Alenezi, Fayadh, Hamid, Arwa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.07.2023
Témata:	Deep learning ECG classification Graph convolutional network Individual differences Multi-level unsupervised domain adaptation Deep learning Graph convolutional network Multi-level unsupervised domain adaptation ECG classification Individual differences
ISSN:	0957-4174, 1873-6793
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Electrocardiogram (ECG) is an effective non-invasive tool that can detect arrhythmias. Recently, deep learning (DL) has been widely used in ECG classification algorithms. However, differences between subjects lead to data shifts, hindering the further extension of DL algorithms. To solve this problem, we propose a novel multi-level unsupervised domain adaptation framework (MLUDAF) to diagnose arrhythmias. During feature extraction, we use the atrous spatial pyramid pooling residual (ASPP-R) module to extract spatio-temporal features of the samples. Then the graph convolutional network (GCN) module is used to extract the data structure features. During domain adaptation, we design three alignment mechanisms: domain alignment, semantic alignment, and structure alignment. The three alignment strategies are integrated into a unified deep network to guide the feature extractor to extract domain sharing and distinguishable semantic representations, which can reduce the differences between the source and target domains. Experimental results based on the MIT-BIH database show that the proposed method achieves an overall accuracy of 96.8% for arrhythmia detection. Compared to other methods, the proposed method achieves competitive performance. Cross-domain experiments between databases also demonstrate its strong generalizability. Therefore, the proposed method is promising for application in medical diagnosis systems. •We design a novel unsupervised domain adaptation framework for ECG classification.•GCN is used to extract the data structure features.•Our method integrates domain alignment, semantic alignment and structure alignment.•Our method exhibits satisfactory results compared to previous work.
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2023.119711