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Blind ECG Restoration by Operational Cycle-GANs.
- Source :
- IEEE Transactions on Biomedical Engineering; Dec2022, Vol. 69 Issue 12, p3572-3581, 10p
- Publication Year :
- 2022
-
Abstract
- Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve. [ABSTRACT FROM AUTHOR]
- Subjects :
- GENERATIVE adversarial networks
ARRHYTHMIA
PHYSICIANS
ELECTROCARDIOGRAPHY
Subjects
Details
- Language :
- English
- ISSN :
- 00189294
- Volume :
- 69
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 160620945
- Full Text :
- https://doi.org/10.1109/TBME.2022.3172125