1D operational Cycle-GANs restored corrupted ECG signals to clinical-level quality, being preferred by cardiologists for arrhythmia diagnosis 95.51% of the time compared to 4.49% for original signals.
Does 1D operational Cycle-GANs improve the quality and usability of corrupted ECG signals for arrhythmia diagnosis?
A novel 1D operational Cycle-GAN approach can restore artifact-corrupted ECG signals to clinical-level quality, potentially improving automated and manual arrhythmia diagnosis.
Absolute Event Rate: 95.51% vs 4.49%
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.
Kıranyaz et al. (Tue,) conducted a other in ECG artifacts (n=10). 1D Operational Cycle-GANs vs. Original corrupted ECG was evaluated on Cardiologist preference for arrhythmia diagnosis. 1D operational Cycle-GANs restored corrupted ECG signals to clinical-level quality, being preferred by cardiologists for arrhythmia diagnosis 95.51% of the time compared to 4.49% for original signals.