Two-stage ECG signal denoising based on deep convolutional network

Abstract

Abstract Background. An electrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. Methods. The ECG data used are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database. In the experiment, the signal-to-noise ratio (SNR), the root mean square error (RMSE), and the correlation coefficient P are used to evaluate the performance of the network. The method proposed is divided into two stages. In the first stage, a U de -net model is designed for ECG signal denoising to eliminate noise. The DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U de -net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. Result. In SNR, RMSE and P indicators, U de -net + DR-net proposed in this paper can achieve the best performance compared with the other five schemes (FCN, U-net etc). In the three data sets, SNR can be increased by 11.61 dB, 13.71 dB and 14.40 dB and RMSE can be reduced by 10.46 × 10−2, 21.55 × 10−2 and 15.98 × 10−2. Conclusions. Despite the contradictory results, the proposed two-stages method can achieve both the elimination of noise and the preservation of effective details to a large extent of the signals. The proposed method has good application prospects in clinical practice.