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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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. 27; no. 12; pp. 5779 - 5790
Main Authors: Chen, Guanyu, Shi, Tianyi, Xie, Baoxing, Zhao, Zhicheng, Meng, Zhu, Huang, Yadong, Dong, Jin
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 methods still lack generalization across different datasets, hindering their widespread use. Methods: In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. Results: SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. Conclusion: The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. Significance: SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2023.3314698