High-quality and high-fidelity removal of noise in the Electrocardiogram (ECG) signal is of great significance to the auxiliary diagnosis of ECG diseases. In view of the single function of traditional denoising methods and the insufficient performance of signal details after denoising, a new method of ECG denoising based on the combination of the Generative Adversarial Network (GAN) and Residual Network is proposed. The method adopted in this paper is based on the GAN structure, and it restructures the generator and discriminator. In the generator network, residual blocks and Skip-Connecting are used to deepen the network structure and better capture the in-depth information in the ECG signal. In the discriminator network, the ResNet framework is used. In order to optimize the noise reduction process and solve the lack of local relevance considering the global ECG problem, the differential function and overall function of the maximum local difference are added in the loss function in this paper. The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm. Experiments show that the Root Mean Square Error (RMSE) of this method in the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) noise pressure database is 0.0102, and the Signal-to-Noise Ratio (SNR) is 40.8526 dB, which is compared with that of the most advanced experimental methods. Our method improves the SNR by 88.57% on average. Besides the three noise intensities for comparison experiments, additional noise reduction experiments are also performed under four noise intensities in our paper. The experimental results verify the scientific nature of the model, which is that our method can effectively retain the important information conveyed by the original signal.

中文翻译：

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基于生成对抗性残差网络的心电信号降噪方法

高质量，高保真度去除心电图（ECG）信号中的噪声对于心电图疾病的辅助诊断具有重要意义。鉴于传统去噪方法的单一功能以及去噪后信号细节的不足，提出了一种基于生成对抗网络和残差网络相结合的心电图去噪新方法。本文采用的方法基于GAN结构，并重新构造了生成器和鉴别器。在生成器网络中，残留块和跳过连接用于加深网络结构并更好地捕获ECG信号中的深度信息。在鉴别器网络中，使用ResNet框架。为了优化降噪过程，解决全局ECG问题，解决局部不相关问题，本文在损失函数中加入了最大局部差的微分函数和整体函数。实验结果证明，本文所使用的方法具有比当前出色的S变换（ST）算法，小波变换（WT）算法，堆叠降噪自动编码器（S-DAE）算法和改进的降噪自动编码器（I-）更好的性能。 DAE）算法。实验表明，该方法在麻省理工学院和贝斯以色列医院（MIT-BIH）噪声压力数据库中的均方根误差（RMSE）为0.0102，信噪比（SNR）为40.8526 dB，与最先进的实验方法相比。我们的方法将SNR平均提高了88.57％。除了用于比较实验的三种噪声强度外，本文还针对四种噪声强度进行了额外的降噪实验。实验结果验证了该模型的科学性，即我们的方法可以有效地保留原始信号传达的重要信息。