Blind ECG Restoration by Operational Cycle-GANs
Author | Kiranyaz, Serkan |
Author | Devecioglu, Ozer Can |
Author | Ince, Turker |
Author | Malik, Junaid |
Author | Chowdhury, Muhammad |
Author | Hamid, Tahir |
Author | Mazhar, Rashid |
Author | Khandakar, Amith |
Author | Tahir, Anas |
Author | Rahman, Tawsifur |
Author | Gabbouj, Moncef |
Available date | 2023-04-17T06:57:41Z |
Publication Date | 2022 |
Publication Name | IEEE Transactions on Biomedical Engineering |
Resource | Scopus |
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. 1964-2012 IEEE. |
Sponsor | This work was supported in part by Huawei and Academy of Finland project AwCHa. |
Language | en |
Publisher | IEEE Computer Society |
Subject | convolutional neural networks ECG restoration Generative adversarial networks operational neural networks |
Type | Article |
Pagination | 3572-3581 |
Issue Number | 12 |
Volume Number | 69 |
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Electrical Engineering [2611 items ]