用双循环网络提升心电图去噪效果，兼顾波形与图像特征。
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network
- 设计双循环结构，融合心电波形与重构图像的反馈信息。
- 在MIT-BIH数据集上PSNR、SSIM优于传统图像去噪方法。
- 适合需要高精度心电信号处理的研究者或医疗应用开发者。
心电图（ECG）信号常受基线漂移（BW）、肌电干扰（MA）和电极运动（EM）等噪声影响，严重降低其诊断价值。为此，我们提出ECGDeDRDNet，一种基于双循环密集网络的深度学习去噪框架。与传统方法不同，该模型采用双重循环机制，增强对心电波形及估计干净图像的信息复用。基础模型由级联的LSTM层与DenseNet模块构成，通过从含噪输入中减去预测噪声分量获得估计干净图像，并将其迭代反馈至模型。该双循环架构充分结合了时间波形特征与空间图像细节，实现更有效的噪声抑制。在MIT-BIH数据集上的实验表明，该方法在PSNR与SSIM指标上优于传统图像去噪方法，同时在信噪比（SNR）与均方根误差（RMSE）方面也超越经典心电图去噪技术。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) signals are frequently corrupted by noise, such as baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), which significantly degrade their diagnostic utility. To address this issue, we propose ECGDeDRDNet, a deep learning-based ECG Denoising framework leveraging a Double Recurrent Dense Network architecture. In contrast to traditional approaches, we introduce a double recurrent scheme to enhance information reuse from both ECG waveforms and the estimated clean image. For ECG waveform processing, our basic model employs LSTM layers cascaded with DenseNet blocks. The estimated clean ECG image, obtained by subtracting predicted noise components from the noisy input, is iteratively fed back into the model. This dual recurrent architecture enables comprehensive utilization of both temporal waveform features and spatial image details, leading to more effective noise suppression. Experimental results on the MIT-BIH dataset demonstrate that our method achieves superior performance compared to conventional image denoising methods in terms of PSNR and SSIM while also surpassing classical ECG denoising techniques in both SNR and RMSE.
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