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

机译:使用基于EEMD的算法微妙地分离多普勒血流和血管壁搏动信号

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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.
机译:基于整体经验模态分解(EEMD)时频分析,避免了模态混合,提出了一种精确分离多普勒血流信号和血管壁搏动信号的算法。首先,估计添加噪声的适当幅度和用于消除噪声的整体平均次数,然后使用EEMD方法将混合的多普勒超声信号分解为IMF。最后,使用软阈值去噪方法将血流和血管壁信号之间的分隔周围的IMF精细分离。用计算机模拟了20dB,40dB和70dB的WBSR以及真实的人颈多普勒超声信号进行了实验,以比较该方法与高通滤波器,原始经验模式分解(EMD)方法和改进的EMD精度。分离方法。结果表明,本文提出的方法通过消除模式混合提供了最高的提取血流信号的准确性,特别是对于那些具有较大的血对血比的信号。

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