噪声环境下基于胎心电检测信号相关性判别的盲分离分析

胎心电是目前研究的一个热点.基于线性叠加盲分离模型，孕妇腹部的胎心电检测信号在原理上是可以通过盲分离算法进行分离的.然而临床复杂的采样环境往往会破坏信号的线性叠加特性导致一般盲分离方法难以提取出微弱的胎心电信号.为此，本文提出了一种带噪情况下基于判断多路孕妇腹部混叠信号是否满足时域稀疏线性叠加特性判决方法.通过现有的心电检测手段，找到孕妇腹部混叠心电信号的稀疏区域并计算信号间的相关性，以度量多路信号的稀疏线性混叠程度.仿真结果表明，本文提出的基于胎心电检测信号的判别方法直观地反映了多路信号间的混叠线性程度，可为FastICA算法在胎心电的应用提供一个前期的判断依据，以便获得更优的分离效果或选择另外的盲分离算法.

A Discriminant Method of Blind Source Separation Based on FECG Correlations in Noisy Environment

Extraction of fetal electrocardiogram (FECG) is currently focused as a prominent issue of medical research. Theoretically FECG can be extracted from the maternal abdominal recordings by conventional blind source separation algorithm. But in the complexity of the real environment, there must be with the proviso that the recordings need to meet the linear mixing model of Blind Source Separation (BSS). This problem will be more difficult in noisy environment. In view of this fact, our paper introduces a discriminant method of BSS based on FECG correlations analysis. The sparse areas of signals are found firstly, and then we can calculate the correlation values of these areas as a judging criterion on the basis of whether the recordings meet the model of BSS so that FECG can be extracted from these observations by conventional BSS methods. Finally, using this discriminant method, the researchers can obtain better performance of extraction or choose other methods which are more interesting.