A denoising method of ECG signal based on variational autoencoder and masked convolution

Wearable electrocardiogram (ECG) equipment can realize continuous monitoring of cardiovascular diseases, but these devices are more susceptible to interference from various noises, which will seriously reduce the diagnostic correctness. In this work, a novel noise reduction model for ECG signals is proposed based on variational autoencoder and masked convolution. The variational Bayesian inference is conducted to capture the global features of the ECG signals by encouraging the approximate posterior of the latent variables to fit the prior distribution, and we use the skip connection and feature concatenation to realize the information interaction across the channels. To strengthen the connection of local features of the ECG signals, the masked convolution module is used to extract local feature information, which supplement the global features and the noise reduction performance of whole model can be greatly improved. Experiments are carried out on the MIT-BIH arrythmia database, and the results display that the performance metrics of signal-to-noise ratio (SNR) and root mean square error (RMSE) are significantly improved compared with other approaches while causing less signal distortion.