SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder

Objective: Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these meth...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 27; H. 12; S. 5779 - 5790
Hauptverfasser:	Chen, Guanyu, Shi, Tianyi, Xie, Baoxing, Zhao, Zhicheng, Meng, Zhu, Huang, Yadong, Dong, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Benchmarking Commonality Data augmentation Data models Datasets Deep learning Denoising AutoEncoder ECG quality assessment EKG Electric Power Supplies Electrocardiography Encoding Feature extraction Humans Localization Machine learning Noise reduction Quality assessment Quality control Research Design Sensitivity Sensors Signal quality Signal reconstruction Statistical analysis swin Transfromer Teaching methods Transfer learning Transformers waveform component localization Waveforms
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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