He Zixiao, Liu Xinwen, He Hao, Wang Huan
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:779-782. doi: 10.1109/EMBC46164.2021.9630123.
Electrocardiogram (ECG) signal is one of the most important methods for diagnosing cardiovascular diseases but is usually affected by noises. Denoising is therefore necessary before further analysis. Deep learning-related methods have been applied to image processing and other domains with great success but are rarely used for denoising ECG signals. This paper proposes an effective and simple model of encoder-decoder structure for denoising ECG signals (APR-CNN). Specifically, Adaptive Parametric ReLU (APReLU) and Dual Attention Module (DAM) are introduced in the model. Rectified Linear Unit (ReLU) is replaced with the APReLU for better negative information retainment. The DAM is an attention-based module consisting of a channel attention module and spatial attention module, through which the inter-spatial and inter-channel relationship of the input data are exploited. We tested our model on the MIT-BIH dataset, and the results show that the APR-CNN can handle ECG signals with a different signal-to-noise ratio (SNR). The comparative experiment proves our model is better than other deep learning and traditional methods.Clinical Relevance- This paper proposed a method capable of denoising ECG signals with strong noise to alleviate difficulties for further medical analysis.
心电图（ECG）信号是诊断心血管疾病的最重要方法之一，但通常会受到噪声影响。因此，在进行进一步分析之前有必要进行去噪。与深度学习相关的方法已在图像处理和其他领域得到成功应用，但很少用于ECG信号去噪。本文提出了一种有效且简单的用于ECG信号去噪的编码器-解码器结构模型（APR-CNN）。具体而言，该模型引入了自适应参数修正线性单元（APReLU）和双重注意力模块（DAM）。用APReLU替换修正线性单元（ReLU）以更好地保留负信息。DAM是一个基于注意力的模块，由通道注意力模块和空间注意力模块组成，通过它可以利用输入数据的空间间和通道间关系。我们在麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）数据集上测试了我们的模型，结果表明APR-CNN可以处理不同信噪比（SNR）的ECG信号。对比实验证明我们的模型优于其他深度学习方法和传统方法。临床相关性——本文提出了一种能够对强噪声ECG信号进行去噪的方法，以减轻进一步医学分析的困难。
