Delicate seperation of Doppler blood flow and vessel wall beat signals by using the EEMD-based algorithm

Wenjing Lin, Yufeng Zhang, Zhiguo Jia, Han Chen, Kexin Zhang, Zhiyao Li
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引用次数: 0

Abstract

Based on the ensemble empirical mode decomposition (EEMD) time-frequency analysis for avoiding mode mixing, an algorithm to delicately separate the Doppler blood flow and vessel wall beat signals is proposed in this paper. Firstly, the proper amplitude of added noise and number of ensemble average for noise cancellation are estimated, and then the mixed Doppler ultrasound signal is decomposed into IMFs by using EEMD method. Finally, the IMFs around the division between the blood flow and vessel wall signals are delicately separated using soft-threshold denoising method. Experiments on both computer simulated with WBSR of 20dB, 40dB and 70dB as well as real human carotid Doppler ultrasound signals are carried out to compare the proposed method with the high pass filter, the original empirical mode decomposition (EMD) method and the improved EMD delicate separation method. It is shown that method proposed in this paper provides the highest accuracy of extracting blood flow signals by elimination of the mode mixture, especially for those signals with larger wall-to-blood signal ratio.
利用基于eemd的算法对多普勒血流和血管壁搏动信号进行精细分离
基于集成经验模态分解(EEMD)时频分析避免模态混叠,提出了一种精细分离多普勒血流和血管壁拍频信号的算法。首先估计适当的加噪幅度和用于消噪的集合平均数,然后利用EEMD方法将混合多普勒超声信号分解成多普勒分量。最后,采用软阈值去噪方法对血流和血管壁信号周围的信号进行精细分离。在WBSR分别为20dB、40dB和70dB的计算机模拟以及真实的人颈动脉多普勒超声信号上进行了实验,将所提方法与高通滤波器、原始经验模态分解(EMD)方法和改进的EMD精细分离方法进行了比较。结果表明,本文提出的方法通过消除模态混合对血流信号的提取具有最高的精度,特别是对于壁血比较大的血流信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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