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https://hdl.handle.net/20.500.14365/1978
Title: | Blind ECG Restoration by Operational Cycle-GANs | Authors: | Kiranyaz, Serkan Devecioglu, Ozer Can İnce, Türker Malik, Junaid Chowdhury, Muhammad Hamid, Tahir Mazhar, Rashid |
Keywords: | Generative adversarial networks convolutional neural networks operational neural networks ECG restoration Noise-Reduction Neural-Networks |
Publisher: | IEEE-Inst Electrical Electronics Engineers Inc | 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. | URI: | https://doi.org/10.1109/TBME.2022.3172125 https://hdl.handle.net/20.500.14365/1978 |
ISSN: | 0018-9294 1558-2531 |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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