A lightweight U-Net model for denoising and noise localization of ECG signals

Electrocardiogram (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. The 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. The 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. The proposed method is highly effective in removing acceptable noise from ECG signals of varying lengths and locating the positions of unacceptable noise. This 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.