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المؤلفKiranyaz, Serkan
المؤلفDevecioglu, Ozer Can
المؤلفInce, Turker
المؤلفMalik, Junaid
المؤلفChowdhury, Muhammad
المؤلفHamid, Tahir
المؤلفMazhar, Rashid
المؤلفKhandakar, Amith
المؤلفTahir, Anas
المؤلفRahman, Tawsifur
المؤلفGabbouj, Moncef
تاريخ الإتاحة2023-04-17T06:57:41Z
تاريخ النشر2022
اسم المنشورIEEE Transactions on Biomedical Engineering
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TBME.2022.3172125
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41934
الملخصObjective: 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.
راعي المشروعThis work was supported in part by Huawei and Academy of Finland project AwCHa.
اللغةen
الناشرIEEE Computer Society
الموضوعconvolutional neural networks
ECG restoration
Generative adversarial networks
operational neural networks
العنوانBlind ECG Restoration by Operational Cycle-GANs
النوعArticle
الصفحات3572-3581
رقم العدد12
رقم المجلد69
dc.accessType Open Access


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