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首页> 外文期刊>Signal processing >Extraction Of Mean Frequency Information From Doppler Blood Flow Signals Using A Matching Pursuit Algorithm
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Extraction Of Mean Frequency Information From Doppler Blood Flow Signals Using A Matching Pursuit Algorithm

机译：使用匹配追踪算法从多普勒血流信号中提取平均频率信息

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摘要

The intensity-weighted mean frequency (IWMF) waveform of Doppler blood flow signals associates with the instantaneous mean blood velocities and has been found to be very useful to measure volumetric flow and detect arterial stenosis. These applications involving Doppler signals require the accurate estimation of the IWMF over short durations of the signal due to its nonstationarity. The traditional short-time Fourier transform (STFT) method requires stationarity of the signal during a finite window, making it inaccurate to analyze signals having relatively wide bandwidths that change rapidly with time. In order to accurately estimate the Doppler IWMF waveform, even when the temporal flow velocity is rapid (high nonstationarity), we extract the Doppler IWMF waveform from the time-frequency distribution estimated using the matching pursuit (MP) with stochastic time-frequency dictionaries in the present study. Because of its local adaptivity to transient structures, the MP algorithm provides a remarkably compact time-frequency description and high time-frequency resolution of a signal. A comparative evaluation has been made between the classic (STFT-based) and the MP-based algorithms. Experimental results indicate that the Doppler IWMF waveform estimated using the MP with stochastic dictionaries is more accurate than that based on the STFT.
机译：多普勒血流信号的强度加权平均频率（IWMF）波形与瞬时平均血流速度相关，并且已发现对于测量体积流量和检测动脉狭窄非常有用。这些涉及多普勒信号的应用由于其不平稳性，需要在信号的短时间内准确估计IWMF。传统的短时傅立叶变换（STFT）方法需要在有限的窗口内保持信号平稳，从而难以分析带宽相对较宽且随时间快速变化的信号。为了准确估计多普勒IWMF波形，即使在瞬时流速较快（非平稳性较高）时，我们也使用随机时频字典通过匹配追踪（MP）从时频分布估计的时频分布中提取多普勒IWMF波形。本研究。由于其对瞬态结构的局部适应性，MP算法提供了非常紧凑的时频描述和信号的高时频分辨率。在经典算法（基于STFT的算法）和基于MP的算法之间进行了比较评估。实验结果表明，使用带有随机字典的MP估计的多普勒IWMF波形比基于STFT的多普勒IWMF波形更准确。

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