Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Comput Biol Med. 2013 Oct;43(10):1622-7. doi: 10.1016/j.compbiomed.2013.07.028. Epub 2013 Aug 2.
Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.
提取干净的胎儿心电图（ECG）信号在胎儿监测中非常重要。在本文中，我们提出了一种基于小波分析、最小均方（LMS）自适应滤波算法和空间选择性噪声滤波（SSNF）算法的胎儿 ECG 提取新方法。首先，对腹部信号和胸部信号进行平稳小波变换（SWT）处理，得到各尺度的小波系数。对于每个尺度，对细节系数进行 LMS 算法处理。将腹部信号的系数作为 LMS 自适应滤波系统的原始输入，将胸部信号的系数作为参考输入。然后，计算处理后的小波系数的相关性。通过 SSNF 算法设置阈值并去除噪声分量。最后，通过逆 SWT 重建处理后的小波系数，得到胎儿 ECG。使用了 20 例模拟数据和 12 例临床数据。实验结果表明，与 LMS 算法相比，所提出的方法具有更好的性能：（1）在胎儿 ECG 和母体 ECG 的 R 峰叠加情况下，性能有所提高；（2）通过结合 SSNF 算法消除了噪声干扰，提取的波形更稳定；（3）通过 SNR 计算进行定量评估，性能得到证明。结果表明，该算法可用于从腹部信号中提取胎儿 ECG。
