Long-term monitoring of ECG via wearable monitoring systems has already been widely adopted to detect and prevent heart diseases. However, one of the main issues faced by wearable ECG monitoring systems is that motion artifacts significantly affect the systems' stability and reliability. Therefore, motion artifact reduction is a very challenging task in filtering and processing physiological signals. Based on the existing algorithms and ECG prior knowledge, in this paper, we propose an algorithm, CEEMDAN-IMFx-PCA-CICA, for motion artifact reduction in ambulatory ECG signals using single-channel blind source separation technique. Our algorithm first utilizes CEEMDAN to decompose the mixed signals into IMFs (intrinsic mode function) containing different source signal features, thereby forming new multi-dimensional signals. Using the correlation between IMFx (IMF component with the most ECG features) and each IMF, and PCA are then applied to reduce the dimension of each IMF. Finally, the blind separation of the source ECG signals is achieved by using CICA with IMFx as the constraint reference component. The results of our experiments indicate that our algorithm outperformed CEEMDAN-CICA, CEEMDAN-PCA-CICA, and improved CEEMDAN-PCA-CICA. Besides, the number of iterations of the CICA is significantly reduced; the separated source signal is better; the obtained result is stable. Furthermore, the separated ECG signal has a higher correlation with the source ECG signal and a lower RRMSE, especially in the case of high noise-to-signal ratios.

通过可穿戴监测系统对心电图进行长期监测已被广泛用于检测和预防心脏病。但是，可穿戴式ECG监控系统面临的主要问题之一是运动伪影会严重影响系统的稳定性和可靠性。因此，减少运动伪影是过滤和处理生理信号中的非常具有挑战性的任务。基于现有算法和心电图先验知识，本文提出一种算法CEEMDAN-IMFx-PCA-CICA，用于通过单通道盲源分离技术减少动态心电图信号的运动伪影。我们的算法首先利用CEEMDAN将混合信号分解为包含不同源信号特征的IMF（固有模式函数），从而形成新的多维信号。利用IMFx（具有最多ECG功能的IMF组件）与每个IMF之间的相关性，然后应用PCA来减小每个IMF的尺寸。最后，通过使用以IMFx作为约束参考分量的CICA来实现源ECG信号的盲分离。实验结果表明，我们的算法优于CEEMDAN-CICA，CEEMDAN-PCA-CICA，并改进了CEEMDAN-PCA-CICA。此外，大大减少了CICA的迭代次数。分离的源信号更好；得到的结果是稳定的。此外，分离的ECG信号与源ECG信号具有较高的相关性，而RRMSE较低，尤其是在高信噪比的情况下。最后，通过使用以IMFx作为约束参考分量的CICA来实现源ECG信号的盲分离。实验结果表明，我们的算法优于CEEMDAN-CICA，CEEMDAN-PCA-CICA，并改进了CEEMDAN-PCA-CICA。此外，大大减少了CICA的迭代次数。分离的源信号更好；得到的结果是稳定的。此外，分离的ECG信号与源ECG信号具有较高的相关性，而RRMSE较低，尤其是在高信噪比的情况下。最后，通过使用以IMFx作为约束参考分量的CICA来实现源ECG信号的盲分离。实验结果表明，我们的算法优于CEEMDAN-CICA，CEEMDAN-PCA-CICA，并改进了CEEMDAN-PCA-CICA。此外，大大减少了CICA的迭代次数。分离的源信号更好；得到的结果是稳定的。此外，分离的ECG信号与源ECG信号具有较高的相关性，而RRMSE较低，尤其是在高信噪比的情况下。CICA的迭代次数大大减少；分离的源信号更好；得到的结果是稳定的。此外，分离的ECG信号与源ECG信号具有较高的相关性，而RRMSE较低，尤其是在高信噪比的情况下。CICA的迭代次数大大减少；分离的源信号更好；得到的结果是稳定的。此外，分离的ECG信号与源ECG信号具有较高的相关性，而RRMSE较低，尤其是在高信噪比的情况下。