基于经验模态分解的胎心音信号除噪方法

Denoising of Fetal Heart Sound Based on Empirical Mode Decomposition Method

胎心音是一种非线性非平稳信号,采集时含有大量噪声,信号处理时需要一种良好的除噪方法。本文先用截止频率为200Hz的巴特沃斯低通滤波器和重采样做预处理,再使用经验模态分解法(EMD)做信号分解,选择含有目标信号的分量进行类小波软阈值自适应算法除噪,最后组合重构得到除噪后的胎心音信号。模态分解时,使用添加掩模信号等方法消除模态混叠,用镜像延拓法消除端点效应,并引用Rilling的研究设定停止规则。该除噪方法一次性地消除了基线漂移和噪声,同小波变换(WT)、数学形态学(MM)和傅里叶变换(FT)比较,信噪比(SNR)明显改善,均方根误差(RMSE)最小,能够满足实际应用的需要。

Fetal heart sound is nonlinear and non-stationary, which contains a lot of noise when it is colleced, so the denoising method is important. We proposed a new denoising method in our study. Firstly, we chose the preprocessing of low-pass filter with a cutoff frequency of 200 Hz and the re-sampling. Secondly, we decomposed the signal based on empirical mode decomposition method （EMD） of Hilbert-Huang transform, then denoised some selected target components with wavelet soft threshold adaptive noise cancellation algorithm. Finally we got the clean fetal heart sound by combining the target components. In the EMD, we used a mask signal to eliminate the mode mixing problem, used mirroring extension method to eliminate the end effect, and referenced the stopping rule from the research of Rilling. This method eliminated the baseline drift and noise at once. To compare with wavelet transform （WT）, mathematical morphology （MM） and the Fourier transform （FT）, the SNR was improved obviously, and the RMSE was the minimum, which could satisfy the need of the practical application.