总体经验模态细分法提取血流超声多普勒信号的研究

提出基于总体经验模态分解（EEMD）血流细分法提高血流超声多普勒信号提取精度.首先估计辅助分析所需的白噪声幅度，进而用EEMD得到无模态混叠的本征模态函数（IMF）组，最后分离出血流信号的IMF.将本方法应用于计算机仿真和人体实测超声多普勒信号，并与高通滤波器法、原EMD法和EMD细分法比较.结果表明本文方法，提取的血流信号精度最高，特别对WBSR=70dB的混合信号，其精度比上述方法分别提高35%、38%及17%.

Extraction of Doppler Ultrasound Blood Signals Using the Delicate Separation Method Based on the EEMD Algorithm

A fine separation based on the ensemble empirical mode decomposition (EEMD) algorithm is proposed to improve the accuracy of the blood flow signal extraction.Firstly,a white noise with proper amplitude is estimated according to the energy of blood flow signals.Intrinsic mode functions (IMFs) without mode mixing are obtained by EEMD.Finally,those IMFs belong to the blood flow are delicately separated.Experimental results from both simulation and real human carotid Doppler signals based on the proposed method are compared with those by using the high pass filter,the original empirical mode decomposition (EMD) method and the improved EMD delicate separation method.It is shown that the proposed method provides the highest separation accuracy.Especially for those signals with larger WBSR=70dB,the accuracy is higher than those based on the methods mentioned above by 35%、38% and 17%,respectively.