IEEE/ACM Trans Comput Biol Bioinform. 2021 Mar-Apr;18(2):759-764. doi: 10.1109/TCBB.2020.2976981. Epub 2021 Apr 6.
This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks. The current ECG denoising techniques are based on the time domain signal decomposition methods. These methods use some kind of thresholding and filtering approaches. In our proposed technique, convolutional neural network (CNN) based GAN model is effectively trained for ECG noise filtering. In contrast to existing techniques, we performed end-to-end GAN model training using the clean and noisy ECG signals. MIT-BIH Arrhythmia database is used for all the qualitative and quantitative analyses. The improved ECG denoising performance open the door for further exploration of GAN based ECG denoising approach.
本文提出了一种基于生成对抗网络（GAN）的新型心电图（ECG）去噪方法。噪声通常与 ECG 信号记录过程相关。去噪是大多数 ECG 信号处理任务的核心。目前的 ECG 去噪技术基于时域信号分解方法。这些方法使用某种阈值和滤波方法。在我们提出的技术中，基于卷积神经网络（CNN）的 GAN 模型被有效地训练用于 ECG 噪声滤波。与现有技术相比，我们使用干净和嘈杂的 ECG 信号进行端到端 GAN 模型训练。MIT-BIH 心律失常数据库用于所有定性和定量分析。改进的 ECG 去噪性能为进一步探索基于 GAN 的 ECG 去噪方法开辟了道路。
