Yuan Li, Zhou Zhuhuang, Yuan Yanchao, Wu Shuicai
College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
Comput Math Methods Med. 2018 May 17;2018:7061456. doi: 10.1155/2018/7061456. eCollection 2018.
The fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG.
First, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton's iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal.
An annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean (1) of the improved method were 99.37%, 99.00%, and 99.19%, respectively, while these metrics of the conventional FastICA method were 99.03%, 98.53%, and 98.78%, respectively.
The proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.
独立成分分析的快速定点算法（FastICA）已广泛应用于胎儿心电图（ECG）提取。然而，FastICA算法对初始权重向量敏感，这会影响算法的收敛。为了解决这个问题，提出了一种改进的FastICA方法来提取胎儿心电图。
首先，对母体腹部混合信号进行中心化和白化处理，并将超松弛因子纳入牛顿迭代算法，对随机生成的初始权重向量进行处理。采用改进的FastICA算法分离源成分，从分离出的源成分中选择最佳母体心电图，并检测母体心电图的R波位置。最后，通过奇异值分解（SVD）方法去除每个通道中的母体心电图成分，得到纯净的胎儿心电图信号。
使用一个带注释的临床胎儿心电图数据库来评估改进算法和传统FastICA算法。算法的平均迭代次数从改进前的35次减少到13次。相应地，使用改进算法时，平均运行时间从1.25秒减少到1.04秒。基于改进算法特征值的信噪比（SNR）为1.55，而传统FastICA算法为0.99。基于传统算法互相关系数的SNR也从0.59提高到2.02。改进方法的灵敏度、阳性预测准确率和谐波均值（1）分别为99.37%、99.00%和99.19%，而传统FastICA方法的这些指标分别为99.03%、98.53%和98.78%。
所提出的基于超松弛因子的改进FastICA算法在保持收敛速度的同时，放宽了对初始权重向量的要求，避免了收敛不平衡，减少了迭代次数，提高了收敛性能。
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