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超声多普勒在临床医学中具有广泛的应用,但由于超声多普勒血流信号中夹杂了大量的噪声严重影响了时频声谱图的清晰度,所以必须采用一定的措施消除噪声。本文分别利用离散小波变换算法、小波包变换算法和匹配追踪算法对超声多普勒血流信号进行分解、变换以及降噪处理,并通过仿真实验,给出了不同信噪比情况下,对基于三种算法处理后的信号时域波形和频域波形进行了比较,证实了匹配追踪算法是一种非常适合于对像超声多普勒血流信号这样的频率带宽随时间变化很快的强噪声背景的信号进行噪声处理的算法。
Ultrasound Doppler in clinical medicine has a wide range of applications, but due to ultrasonic Doppler blood flow signal mixed with a lot of noise seriously affect the definition of time-frequency spectrogram, so some measures must be taken to eliminate the noise. In this paper, the discrete wavelet transform algorithm, the wavelet packet transform algorithm and the matching pursuit algorithm are respectively used to decompose, transform and de-noising the ultrasonic Doppler blood flow signal. Through simulation experiments, Comparison of the time-domain and frequency-domain waveforms of the signal processed by the three algorithms shows that the matching pursuit algorithm is a very robust noise signal with very fast frequency bandwidth over time for ultrasound Doppler flow signals Background noise signal processing algorithm.