Liu Guangchen, Luan Yihui
School of Mathematics, Shandong University, Jinan, 250100, Shandong, People's Republic of China.
Med Biol Eng Comput. 2015 Nov;53(11):1113-27. doi: 10.1007/s11517-015-1389-1. Epub 2015 Oct 1.
High-resolution fetal electrocardiogram (FECG) plays an important role in assisting physicians to detect fetal changes in the womb and to make clinical decisions. However, in real situations, clear FECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander, high-frequency noise. In this paper, we proposed a novel integrated adaptive algorithm based on independent component analysis (ICA), ensemble empirical mode decomposition (EEMD), and wavelet shrinkage (WS) denoising, denoted as ICA-EEMD-WS, for FECG separation and noise reduction. First, ICA algorithm was used to separate the mixed abdominal ECG signal and to obtain the noisy FECG. Second, the noise in FECG was reduced by a three-step integrated algorithm comprised of EEMD, useful subcomponents statistical inference and WS processing, and partial reconstruction for baseline wander reduction. Finally, we evaluate the proposed algorithm using simulated data sets. The results indicated that the proposed ICA-EEMD-WS outperformed the conventional algorithms in signal denoising.
高分辨率胎儿心电图（FECG）在协助医生检测子宫内胎儿变化及做出临床决策方面发挥着重要作用。然而，在实际情况中，清晰的FECG很难提取，因为它通常被占主导地位的母体心电图以及其他诸如基线漂移、高频噪声等干扰噪声所淹没。在本文中，我们提出了一种基于独立成分分析（ICA）、总体经验模态分解（EEMD）和小波收缩（WS）去噪的新型集成自适应算法，记为ICA-EEMD-WS，用于FECG分离和降噪。首先，使用ICA算法分离混合的腹部心电图信号并获得带噪声的FECG。其次，通过由EEMD、有用子成分统计推断和WS处理组成的三步集成算法降低FECG中的噪声，并进行部分重建以减少基线漂移。最后，我们使用模拟数据集评估所提出的算法。结果表明，所提出的ICA-EEMD-WS在信号去噪方面优于传统算法。
