Abstract
ObjectiveElectrocardiogram (ECG) is a diagnostic tool for cardiovascular disease in clinical medicine, but noisy ECG signals can easily lead to misdiagnosis. This paper presents a lightweight U-Net model (LUNet) for denoising and noise localization of ECG signals of varying lengths. MethodsThe proposed model acts as a denoising autoencoder, which accepts an ECG signal containing noises and outputs the denoised signal. The locations of unacceptable noise are determined by comparing the difference between the input and output signals of the model. ResultsThe proposed method for ECG signal denoising achieved Root Mean Square Error (RMSE) scores of 0.0116 ± 0.0016, 0.0149 ± 0.0033, 0.0122 ± 0.0015, and 0.0098 ± 0.0012 for baseline wander (BW), muscle artifacts (MA), electrode motion artifacts (EM), and Additive white Gaussian noise (AWGN), respectively. For ECG signal noise localization, the proposed method obtained high Intersection over Union (IoU) scores and Dice scores. Meanwhile, our model showed excellent generalization performance on two public ECG databases. On the 12-lead ECG arrhythmia classification task, the results using the processed dataset with the proposed method were 2.4% higher in F1 score than using the original dataset. ConclusionThe proposed method is highly effective in removing acceptable noise from ECG signals of varying lengths and locating the positions of unacceptable noise. SignificanceThis paper presents a promising solution for denoising and noise localization of ECG signals, which can improve ECG interpretation and potentially lead to better diagnosis and treatment of cardiovascular diseases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.