小波包分解Welch算法在鼾声谱估计中的研究

目的利用鼾声信号各频带的功率谱估计特征,诊断鼾声的阻塞部位。方法针对Welch功率谱估计法处理典型非平稳信号时是单一分辨率分析的问题,提出了一种小波包分解的Welch算法对鼾声信号功率谱估计的新方法。首先对采集的鼾声信号进行小波包多层分解,然后对分解系数重构,最后参照人耳听觉模型并结合各频带信号时频分布的特点,对各频带信号采用Welch算法结合不同的窗宽进行功率谱估计,同时初步分析了不同阻塞部位鼾声各频带的功率谱估计特性。结果该方法在对鼾声信号功率谱估计时,信号中低频和高频部分具有不同的频率和时间分辨率,3 000～5 000 Hz频带有无功率谱分布可以作为判断鼾声阻塞部位的依据。结论实验结果验证了该方法有效性,同时针对不同阻塞部位导致的鼾声,初步分析了其不同频带的功率谱估计特点。     

基于SBF算法的HRV信号信息熵分析

目的：HRV信号是发生在非均匀间隔时间点上的RR序列,其传统的频域分析方法为功率谱分析,但对于非均匀采样的HRV信号,快速傅里叶变换（FFT）并不适用,同时HRV信号是具有混沌性和非平稳性的信号,功率谱也不善于表现HRV信号的非平稳性质。SBF（Similar Basis Function）算法是相对于FFT的另一种傅里叶积分估计方法,适用于均匀与非均匀采样信号,片段谱是基于SBF算法定义的表示信号能量分布的参数,相对于功率谱其主要的优点是能表现谱随时间的变化,对于处理非平稳信号也有一定的优势。因此本文探究用片段谱信息熵作为HRV信号的参数指标,分析HRV信号在不同频段能量分布复杂度随年龄的变化。方法：本文以20名年轻（21～34）岁与20名年老（68～81）岁二组健康人的HRV信号为实验数据,用SBF算法计算出二组人的片段谱,再算出多个不同频率段上的信息熵,同时用全频段分割法计算两组人的信息熵。结果：在0．003Hz～0．04Hz和0．04Hz～0．15Hz频段内,年轻组的片段谱熵明显大于老年组（p〈0．001,p〈0．01）;由全频段分割法也得到类似的结果。结论：因而基于SBF算法的片段谱熵是分析HRV信号的有效指标。     

Burg算法在基于AR模型多普勒血流信号时频分析中的应用

多普勒超声血流信号是一个非平稳的高斯随机过程,其时频分布与血流的速度及其变化有密切的关系。由于假设信号在一定时间间隔内是平稳的,实际上难以获得同时具有较好的时间、频率分辨率的超声多普勒时频分布。一种估计多普勒超声血流信号时频分布的方法是基于Levinson-Durbin算法的自回归(AR)模型法。但用该算法估计出的参数的误差随时间间隔的缩短而增大。Burg提出一种递推算法,不需要计算自相关,而是用使前向与后向预测误差能量之和最小的方法求出模型的参数。我们将用两种算法估计出的多普勒时频分布及理论的时频分布进行比较,发现用Burg算法估计出的多普勒时频分布比用Levinson-Durbin递推算法估计出的多普勒时频分布更接近理论的时频分布,尤其是频率带宽性能得到了明显的改善。     

超声多普勒血流信号的分析方法

超声多普勒技术是无损诊断血管疾病的一种重要手段,因此对超声多普勒血流信号的分析处理可以为疾病诊断提供重要依据.为了分析和处理像超声多普勒这类非平稳信号,人们对基于傅立叶变换的传统信号分析方法进行了推广乃至根本性的革命,提出并发展了一系列新的信号分析理论.本文对应用于超声多普勒血流信号分析的短时傅立叶变换、小波变换、参数模型法和Cohen类的时频分布等方法作了着重论述.     

基于小波变换估计的超声多普勒血流频谱中频带展宽及修正

利用脉冲多普勒技术检测血流时，信号的频谱带宽是诊断疾病的重要依据之一。与传统的短时傅立叶变换比较，小波变换使用了不同宽度的函数窗，具有多分辨能力，对多普勒血流信号频谱的估计可以获得更精确的时频分辨率。但是，基于小波变换的频谱带宽中仍然包含有分析窗口和信号自身非平稳性引入的误差。本研究针对小波变换中窗口宽度变化的多分辨特点，给出了窗口效应和信号非平稳性频谱均方根带宽的表达式，提出了两种修正频谱带宽中窗口和非平稳展宽误差的方法。通过对模拟多普勒信号的修正实验，两种方法都取得了很好的修正效果。     

超声多普勒管壁搏动和血流信号的分离研究

本研究提出利用经验模式分解(EMD)算法分解混叠有管壁成分的超声多普勒血流信号来实现管壁搏动和血流信号的分离。该方法首先将混叠有管壁搏动的超声多普勒血流信号分解为少量有限的分量,即内模函数(IMFs),然后根据管壁搏动信号与血流信号的功率比变化曲线,用比值法自动确定并去除低频管壁博动成分。在仿真实验中用提出的方法处理模拟的多普勒信号,对于靠近管腔内壁的血流信号其在频域功率谱上的相对误差为50%,在时域幅度的相对误差为45%,与高通滤波器方法的相对误差95%相比,准确性得到提高。基于个人计算机用C语言编程实现提出的算法,对实际采集的人体颈动脉多普勒信号可实现实时分离处理。结果表明:基于经验模式分解的滤波方法能有效客观地滤除管壁搏动信号,更准确地保留低频血流信号成分。     

基于coif5小波的多普勒胎心音信号提取算法的研究

胎心率监护是围产期胎儿监护的关键技术指标，超声多普勒测量胎心率是最常用的无创方法。由于胎心多普勒信号具有信噪比低、非平稳的随机性特点，提出基于coif5小波，结合双重阈值方法的胎心音信号提取算法。实验表明，该算法有效地解决了由于胎心率加倍、减半所引起的胎心率曲线翻转问题，提高了多普勒胎心音信号提取的准确性。     

基于分段式变步长截断误差LMS算法的心率信号提取

自适应滤波对于心率信号这类非平稳随机信号处理是一种有效的方法。提出了一种分段式变步长截断误差LMS算法，采用自回归预测滤波器估计出通过超声多普勒传感器采集到的心率信号中的噪声，用它作为自适应噪声抵消系统中的参考信号，其结果是抵消了主输入通道中的部分噪声，使信噪比提高了36个dB左右，实现了心率信号的提取。     

血栓多普勒信号的计算机仿真研究

利用超声多普勒技术检测血栓,对临床诊断有十分积极的意义。为了研究多普勒信号特征提取方法检测血栓的性能,在计算机上生成仿真的血栓多普勒信号,仿真的参数,如血栓半径、血栓进入采样容积的时间等参数可事先设定,通过分析、研究不同给定参数的仿真信号,证实了利用超声多普勒技术无损检测血栓的可行性,并提出了表征血栓多普勒信号的敏感参数。     

超声彩色血流成像中非平稳杂波的抑制

为了获取高质量的彩色血流图像，必须对来自组织的杂波信号进行抑制。传统的杂波抑制器如有限脉冲响应（FIR）、无限脉冲响应（IIR）和回归型滤波器，都是针对平稳杂波信号抑制的。然而实际的临床诊断中存在相当程度的组织加速运动，使得杂波信号是非平稳随机过程。在这种情况下，采用传统的杂波抑制器会给血流速度估计带来很大偏差。本研究提出了一种新的非平稳杂波抑制方案。它首先用相位建模法估计出杂波瞬时频率，然后利用此频率对解调后的多普勒信号进行混频，混频后，再通过传统的高通滤波器对杂波进行衰减。实验结果表明，利用此滤波方案可以对非平稳杂波进行充分地抑制，在低速血流和高强度杂波的情况下效果更加明显。     

Cardiac Doppler blood-flow signal analysis

The normality (Gaussian property) and stationarity of the cardiac Doppler blood-flow signal were evaluated on short-time segments distributed over the cardiac cycle. The basic approaches used to perform statistical tests on the nonstationary and quasiperiodic cardiac Doppler signal are presented. The results obtained from the data of ten patients having a normal aortic valve and ten patients having a stenotic valve indicate that a complex Gaussian random process is an acceptable approximation for the clinical cardiac Doppler signal. For segments of 10 ms or less, 82 per cent of them were accepted to be stationary with a significance level of 0.05, whereas for durations greater than 40 ms, the percentage of stationary segments was less than 75 per cent. It was concluded that the 10ms window generally used in practice is a good choice for Doppler spectrogram estimation, but a shorter time interval would be preferable.     

A comparison of the wavelet and short-time fourier transforms for Doppler spectral analysis

Doppler spectrum analysis provides a non-invasive means to measure blood flow velocity and to diagnose arterial occlusive disease. The time-frequency representation of the Doppler blood flow signal is normally computed by using the short-time Fourier transform (STFT). This transform requires stationarity of the signal during a finite time interval, and thus imposes some constraints on the representation estimate. In addition, the STFT has a fixed time-frequency window, making it inaccurate to analyze signals having relatively wide bandwidths that change rapidly with time. In the present study, wavelet transform (WT), having a flexible time-frequency window, was used to investigate its advantages and limitations for the analysis of the Doppler blood flow signal. Representations computed using the WT with a modified Morlet wavelet were investigated and compared with the theoretical representation and those computed using the STFT with a Gaussian window. The time and frequency resolutions of these two approaches were compared. Three indices, the normalized root-mean-squared errors of the minimum, the maximum and the mean frequency waveforms, were used to evaluate the performance of the WT. Results showed that the WT can not only be used as an alternative signal processing tool to the STFT for Doppler blood flow signals, but can also generate a time-frequency representation with better resolution than the STFT. In addition, the WT method can provide both satisfactory mean frequencies and maximum frequencies. This technique is expected to be useful for the analysis of Doppler blood flow signals to quantify arterial stenoses.     

超声多普勒血流和管壁搏动信号的分离研究

目的针对超声多普勒血流检测中,传统的高通滤波法在滤除管壁搏动信号的同时也会滤除低频血流信号的问题,本研究提出一种以心电信号（electrocardiography,ECG）作为参考信号的自适应滤波的方法消除管壁干扰。方法包括两方面：其一,采用心电信号作为参考信号对超声多普勒信号进行自适应滤波;其二,采用多级自适应滤波并选择不同的参考信号的滤波方案。分别使用上述方法和高通滤波法对仿真的超声多普勒信号进行处理,并将结果进行比较。结果与传统的高通滤波法相比,该方法在有效抑制管壁搏动信号的同时保留一部分低频血流信号成分。结论该方法能较准确地提取出完整的血流超声多普勒信号,具有一定的临床应用价值。     

Correction for broadening in Doppler blood flow spectrum estimated using wavelet transform

The conventionally used spectral estimation technique for Doppler blood flow signal analysis is short-time Fourier transform (STFT). But this method requires stationarity of the signal during the window interval. Wavelet transform (WT), which has a flexible time-frequency window, is particularly suitable for nonstationary signals. In recently years, the WT has been used to investigate its advantages and limitations for the analysis of Doppler blood flow signals. In these studies, the estimated spectral width of Doppler blood flow signals using the WT might include significant window and nonstationarity broadening errors. These broadening errors of the time-varying spectrum were clearly undesirable since it would tend to mask the effect of flow disturbance on the spectra width. In this paper, a closed form expression for window and nonstationary root-mean-squared (rms) spectral width is given when using the WT to estimate the Doppler blood flow spectrum. The increases in the rms spectral width can be calculated and then the spectral width estimation based on the WT can be corrected.     

一种基于自相关和平均模差算法的胎儿心率提取方法

目的 胎儿心率是判断孕期胎儿健康状况的一项重要指标,使用超声多普勒测量胎心率是常用的无创方法.其中,自相关算法是常用的测量胎心率的算法,但是其抗噪性差,容易出错.方法 本文提出一种基于自相关与平均标准模差相结合的新算法,先通过自相关函数突显其周期性,然后用累计平均标准模差函数提取准确周期,最后通过使用该算法处理模拟器数据和临床数据,验证其精确度和抗噪性.结果 相比滑动窗算法,基于自相关和平均标准模差的算法具有更高的精确度和抗噪性.结论 改进后的算法可以有效计算出胎心率值.     

一种用于测量胎儿心率的改进自相关算法

在围产期对肥儿心率进行监护是非常重要的，超声Doppler测量胎心率是最常用的无创方法。但是，由于Doppler信号成分很复杂，从而使信号处理比较困难。我们提出了一种改进的自相关方法－－指数加权自相关方法，并在其中利用了自己适应技术，通过临床试验发现，与以前的方法相比，使用这种方法从超声Doppler信号中提取的胎率较准确，尤其是在信号存在衰减和丢失的情况下。     

Noise reduction in Doppler ultrasound signals using an adaptive decomposition algorithm

A novel de-noising method for improving the signal-to-noise ratio (SNR) of Doppler ultrasound blood flow signals, called the matching pursuit method, has been proposed. Using this method, the Doppler ultrasound signal was first decomposed into a linear expansion of waveforms, called time-frequency atoms, which were selected from a redundant dictionary named Gabor functions. Subsequently, a decay parameter-based algorithm was employed to determine the decomposition times. Finally, the de-noised Doppler signal was reconstructed using the selected components. The SNR improvements, the amount of the lost component in the original signal and the maximum frequency estimation precision with simulated Doppler blood flow signals, have been used to evaluate a performance comparison, based on the wavelet, the wavelet packets and the matching pursuit de-noising algorithms. From the simulation and clinical experiment results, it was concluded that the performance of the matching pursuit approach was better than those of the DWT and the WPs methods for the Doppler ultrasound signal de-noising.     

ChirP-z变换在超声多普勒血流声谱图中的应用

本文将Chirp-z变换方法用于超声多普勒血流信号的测量,利用该方法的局部频谱分析能力,有效实现了血流声谱图的局部放大。该方法可以在高采样频率条件下实现低流速 血流信号的局部可视化,同时由于局部范围内采样分辨率的提高,将获得更细致的血流声谱图,训测试该方法的有效性,将实测超声血流数据用该方法进行处理,成功地将低流速 血流信号进行了局部放大。     

Role of models in understanding and interpreting clinical Doppler ultrasound

Mathematical and physical models are essential tools in both fundamental and clinically applied Doppler ultrasound research. In this paper we illustrate a variety of models and show how they can be used to understand and interpret clinical Doppler ultrasound signals, particularly from stenosed arteries. The physical models discussed include both steady and pulsatile flow systems, and also a flow visualization technique that can be used to interpret the Doppler signals at a fundamental hemodynamic level. The mathematical models deal with three different aspects of the Doppler signal: models that describe the mechanism of ultrasound scattering by blood, a model to stimulate the returned Doppler signal and a model that may be used to aid in the analysis of clinical recordings. Each of these models provides a more complete understanding of blood flow through normal and stenosed vessels and contributes to the interpretation of clinical Doppler signals.