Xu Yang, Luo Mingzhang, Li Tao, Song Gangbing
Electronics & Information School, Yangtze University, Jingzhou 434023, China.
National Demonstration Center for Experimental Electrotechnics and Electronics Education, Yangtze University, Jingzhou 434023, China.
Sensors (Basel). 2017 Nov 28;17(12):2754. doi: 10.3390/s17122754.
A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors' best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT-BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently.
提出了一种基于自适应噪声完备总体经验模态分解（CEEMDAN）和小波阈值的新型心电图（ECG）信号去噪及基线漂移校正方法。尽管CEEMDAN基于经验模态分解（EMD），但它通过克服模态混叠问题对原始EMD进行了显著改进。然而，据作者所知，此前尚无关于使用CEEMDAN对ECG信号进行去噪的研究。在所提出的方法中，原始带噪ECG信号通过CEEMDAN分解为一系列从高频到低频排序的本征模态函数（IMF）。然后通过自相关方法对每个IMF进行分析，找出包含随机噪声的前几个高频IMF，并通过小波阈值对这些IMF进行去噪。计算包括最终残差在内的所有IMF的过零率（ZCR），并去除ZCR小于某一值的IMF。最后，对剩余的IMF进行重构以获得干净的ECG信号。所提算法通过使用MIT - BIH ECG数据库进行实验验证，结果表明ECG信号中的随机噪声能够得到有效抑制，同时基线漂移也能得到有效校正。
