基于径向基函数神经网络的胎儿心电提取

Fetal electrocardiogram extraction based on radial basis function neural networks

针对胎儿心电难以提取问题,提出一种从母体腹壁混合信号中提取胎儿心电的方法。利用径向基函数(radial basis function,RBF)神经网络估计母体心电信号传导至腹壁的非线性变换,将非线性变换后的母体心电信号从腹壁混合信号中减去,再通过小波包去噪技术抑制胎儿心电的基线漂移和噪声,得到清晰的胎儿心电。应用临床心电数据完成实验,在胎儿心电和母体心电QRS波完全重叠情况下,提取出清晰的胎儿心电。实验结果验证了方法的有效性。

A novel method for extracting fetal electrocardiogram （FECG） from the abdominal composite signal of a pregnant woman is proposed. The maternal component in the abdominal electrocardiogram （ECG） signal is a nonlinearly transformed version of the mother＇s ECG （MECG）. This nonlinear relationship was identified using radial basis function （RBF） neural networks. The FECG is extracted by subtracting the nonlinearly transformed version of the MECG from the abdominal ECG signal. The baseline shift and noise in the FECG are suppressed by wavelet packet denoising technique. Experimental results obtained from the actual ECG signals demonstrate the effectiveness of the proposed method in extracting FECG even when it is totally embedded within the maternal（QRS） complex.