An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction

• Published in: Science China. Information sciences Vol. 52; no. 10; pp. 1863 - 1874
• Main Authors: Ye, YaLan, Sheu, Phillip C. -Y., Zeng, JiaZhi, Wang, Gang, Lu, Ke
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science in China Press 01.10.2009; Springer Nature B.V
• Subjects: Algorithms; Biomedical engineering; Computer Science; Errors; Information Systems and Communication Service; Kurtosis; Neural networks; Outliers (statistics); Robustness; Stability analysis; 信号提取; 先验信息; 提取算法; 有用信号; 生物医学工程; 神经网络; 高效率; independent component analysis; electrocardiogram; blind source extraction; fetal ECG; blind source separation; Atrial Fibrillation
• ISSN: 1009-2757; 1674-733X; 1862-2836; 1869-1919
• DOI: 10.1007/s11432-009-0163-0

In many applications, such as biomedical engineering, it is often required to extract a desired signal instead of all source signals. This can be achieved by blind source extraction （BSE） or semi-blind source extraction, which is a powerful technique emerging from the neural network field. In this paper, we propose an efficient semi-blind source extraction algorithm to extract a desired source signal as its first output signal by using a priori information about its kurtosis range. The algorithm is robust to outliers and spiky noise because of adopting a classical robust contrast function. And it is also robust to the estimation errors of the kurtosis range of the desired signal providing the estimation errors are not large. The algorithm has good extraction performance, even in some poor situations when the kurtosis values of some source signals are very close to each other. Its convergence stability and robustness are theoretically analyzed. Simulations and experiments on artificial generated data and real-world data have confirmed these results.