Kiranyaz, SerkanDevecioglu, Ozer Canİnce, TürkerMalik, JunaidChowdhury, MuhammadHamid, TahirMazhar, Rashid2023-06-162023-06-1620220018-92941558-2531https://doi.org/10.1109/TBME.2022.3172125https://hdl.handle.net/20.500.14365/1978Objective: 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.eninfo:eu-repo/semantics/openAccessGenerative adversarial networksconvolutional neural networksoperational neural networksECG restorationNoise-ReductionNeural-NetworksArticle10.1109/TBME.2022.31721252-s2.0-85129683826