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AuthorKiranyaz, Serkan
AuthorDevecioglu, Ozer Can
AuthorInce, Turker
AuthorMalik, Junaid
AuthorChowdhury, Muhammad
AuthorHamid, Tahir
AuthorMazhar, Rashid
AuthorKhandakar, Amith
AuthorTahir, Anas
AuthorRahman, Tawsifur
AuthorGabbouj, Moncef
Available date2023-04-17T06:57:41Z
Publication Date2022
Publication NameIEEE Transactions on Biomedical Engineering
ResourceScopus
URIhttp://dx.doi.org/10.1109/TBME.2022.3172125
URIhttp://hdl.handle.net/10576/41934
AbstractObjective: 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.
SponsorThis work was supported in part by Huawei and Academy of Finland project AwCHa.
Languageen
PublisherIEEE Computer Society
Subjectconvolutional neural networks
ECG restoration
Generative adversarial networks
operational neural networks
TitleBlind ECG Restoration by Operational Cycle-GANs
TypeArticle
Pagination3572-3581
Issue Number12
Volume Number69
dc.accessType Open Access


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