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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Veröffentlicht in:	Expert systems with applications Jg. 221; S. 119711
Hauptverfasser:	He, Ziyang, Chen, Yufei, Yuan, Shuaiying, Zhao, Jianhui, Yuan, Zhiyong, Polat, Kemal, Alhudhaif, Adi, Alenezi, Fayadh, Hamid, Arwa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2023
Schlagworte:	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
Online-Zugang:	Volltext
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