Blind ECG Restoration by Operational Cycle-GANs.
IEEE Trans Biomed Eng
; 69(12): 3572-3581, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-35503842
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Eletrocardiografia
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
/
Qualitative_research
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article