School of Life Sciences, Tiangong University, Tianjin 300387, China.
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices, Tianjin 300384, China.
Rev Sci Instrum. 2024 Sep 1;95(9). doi: 10.1063/5.0222123.
Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals.
动态心电图（ECG）检测在心血管疾病的早期发现、诊断、治疗评估和预防中起着至关重要的作用。清晰的 ECG 信号对于后续分析这些病症至关重要。然而，运动过程中获取的 ECG 信号容易受到各种噪声干扰，包括电极运动伪影、基线漂移和肌肉伪影等。这些干扰会使特征性 ECG 波形模糊，可能导致医生误诊。为了更有效地抑制 ECG 信号中的噪声，本文提出了一种基于深度学习的新型降噪方法。该方法通过引入条件噪声来增强扩散模型网络，设计了基于噪声预测的多核卷积变换网络结构，并集成了扩散模型逆过程以实现降噪。在 QT 数据库和 MIT-BIH 噪声应激测试数据库上进行了实验，并与其他论文中的算法进行了比较，验证了本方法的有效性。结果表明，该方法在统计和基于距离的评估指标以及波形可视化方面均实现了最优的降噪性能，优于其他八种最先进的方法。本文提出的网络在处理电极运动伪影、基线漂移、肌肉伪影以及这三种噪声的混合复杂噪声方面表现出稳定的性能，预计将应用于未来临床动态 ECG 信号的降噪分析中。
