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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Bibliographic Details

Published in:	Expert systems with applications Vol. 221; p. 119711
Main Authors:	He, Ziyang, Chen, Yufei, Yuan, Shuaiying, Zhao, Jianhui, Yuan, Zhiyong, Polat, Kemal, Alhudhaif, Adi, Alenezi, Fayadh, Hamid, Arwa
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.07.2023
Subjects:	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 Access:	Get full text
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