Denoising method of ECG signal based on Channel Attention Mechanism

ECG is an important medium for doctors to observe the working state of the patient’s heart, and the monitoring of patients based on ECG is very important in clinical diagnosis. The movement of the patient or the activities of other physiological organs in the process of collecting ECG will bring a lot of noise to the ECG signal acquisition, so it is necessary to de-noise the ECG signal with noise. In this paper, an U-net network based on Style-based Recalibration Module (SRM) channel attention is proposed to automatically denoise the noisy ECG signal. The ECG signals used are from clinical data marked by expert diagnostics. In addition, we also compared several popular denoising methods proposed in the past to conduct comparative experiments to verify the denoising performance of the model. The experimental results show that the proposed U-net network based on SRM channel attention can remove the noise contained in the ECG signal while retaining the characteristic shape of the ECG signal, and has a good effect in terms of signal-to-noise ratio and root mean square error.