基于小波变换与基于短时傅立叶变换的超声多普勒血流信号时频分布比较研究

本研究讨论了一种改进的Morlet(MMORL)小波变换在分析超声多普勒血流信号时的优点。用小波变换和短时傅立叶变换计算得出时频分布与理论的时频分布进行了比较。结果得出小波变换能提供较好的时频分辨率的折中效果，能产生更精确的平均频率和带宽。     

考虑频谱展宽校正的最大血流速度估计方法

在超声多普勒血流测量中，接收信号的频谱宽度与许多因素有关。除血流的流动特性引起的谱展宽外，测量系统的声学特性的非理想性也会引起附加的频谱展宽。因此，常规的多普勒血流速度估计方法将不可避免地导致最大流速的高估。本文提出了一种考虑固有频谱展宽效应的峰值血流速度估计的校正方法，介绍了该方法的基本原理及校正公式，并且给出了在体实验结果。     

基于自适应局部余弦变换的正交多普勒超声信号降噪方法的研究

多普勒超声信号的谱图已经被广泛用于医疗诊断。来自系统内部的噪声及外部的干扰会产生附加的频谱成分，从而影响谱图的主观分析及进一步的定量分析。为抑制噪声的影响，本文提出利用一种新的基于自适应局部余弦变换和非负Garrote取阈值的方法对正交多普勒超声信号进行降噪。首先，由正交信号提取正向和逆向血流信息；然后对其分别进行降噪；最后利用Hilbert变换进行重构得到真实信号的估计。在仿真研究中，采用平均频率波形和谱宽波形的估计精度作为性能改善的指标。结果表明这种方法优于基于小波变换的降噪方法，特别是在低信噪比情况下。     

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.     

基于小波变换的心电信号去噪处理

人体心电信号随着检测状态及时间的变化具有明显的非平稳性及包含许多干扰的特点.本文将小波变换的时频定位特性运用于心电信号的测量,利用小波变换多尺度多分辨的特点对心电信号进行分解,不同频带的信号便显现在小波分解的不同尺度上.进行信号重构时,去除各种干扰成份,从而获得精确的心电波形,为医疗诊断提供了更加准确的依据.     

小波变换在人体血流灌注信号分析中的应用

目的：激光多普勒血流仪测得的皮肤平均血流灌注量和血流速度等这些传统的评估微循环的参数指标在电磁场对人体微循环影响的研究中并没有得到跟其在动物实验中相似的结果。因而本研究欲利用时频分析技术寻求新的评估人体微循环的参数指标．给人体激光多普勒血流灌注信号的分析和处理提供新的思路和方法。为后续电磁场对人体微循环影响的研究奠定基础。方法：运用在低频段具有较高频率分辨率的连续小波变换将二维激光多普勒血流灌注信号转换为包含时间和频率信息的三维信号，由此可以清楚地同时观察小波变换后的小波系数在不同频段随着时间的变化情况。结果：小波变换后的小波系数在0．005Hz～2Hz频率范围内存在6个可能与微循环控制机理有关的特征峰值．峰值的幅度随着时间有所变化。结论：小波变换可作为分析人体激光多普勒血流灌注信号的一种有效工具，为后续电磁场对人体微循环影响的研究奠定基础。并且其对微弱、背景噪声强的医学信号的分析和处理提供了新的思路和方法。     

用脉冲多普勒超声进行的谱测量

我们将四种谱分析技术用于脉冲多普勒超声正交信号以比较每种技术在估算血流速度和多普勒谱方面的相对优点。这四种技术是:1)快速傅里叶变换怯;2)最大似然法;3)Burg自回归算法;4)修正的协方差算法到自回归建模。我们对模拟信号和从在体血流系统获得的信号均作了研究。我们确定了每种方法的最佳参数值(例如模型阶数)并研究了信噪比和信号带宽的影响。选择适当的模型阶数可以发现现代     

小波分析理论在脑电分析中的应用

小波变换是一种把时间、频率（或尺度）两域结合起来的分析方法。它具有：（１）多分辨率；（２）相对带宽恒定；（３）适当地选择基本小波，可使小波在时、频两域都具有表征信号局部特征的能力的特点，被誉为“分析信号的显微镜”。本系统以Ｗｉｎｄｏｗｓ为操作系统平台，将小波变换用于脑电信号分析，实现病历管理，１００Ｈｚ脑电信号采样，１０分钟脑电数据存储等功能，是一个在Ｗｉｎｄｏｗｓ３．１下开发的脑电分析系统。从脑电信号小波变换后的波形可以看出，各尺度信号不仅反映信号的频率信息，同时也反映信号的时间信息，意即反映此时ＥＥＧ的状态。而传统的傅里叶分析只能获得信号的整体频谱，不能反映时域信息     

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。     

基于小波变换的心电信号基线矫正方法

本文介绍一种基于小波变换的心电信号基线漂移去除方法。该方法利用小波变换多分辨分析的特性,将含噪声及基线漂移心电信号进行多尺度分解,结果表明,某尺度下的分解信号较好地反映了心电信号基线漂移,在重构过程中可直接将其去除。     

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.     

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.     

Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform

In this study, short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of spectral broadening in the presence of ophthalmic artery stenosis. A qualitative improvement in the appearance of the sonograms obtained using the WT over the STFT was noticeable. Despite the qualitative improvement in the individual sonograms, no quantitative advantage in using the WT over the STFT for the determination of spectral broadening index was obtained due to the poorer variance of the wavelet transform-based spectral broadening index and the additional computational requirements of the wavelet transform.     

Detection of arterial disorders by spectral analysis techniques

This paper intends to an integrated view of the spectral analysis techniques in the detection of arterial disorders. The paper includes illustrative information about feature extraction from signals recorded from arteries. Short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial (OA) Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of OA stenosis. The author suggest that the content of the paper will assist to the people in gaining a better understanding of the STFT and WT in the detection of arterial disorders.     

A computer simulation model for Doppler ultrasound signals from pulsatile blood flow in stenosed vessels

A computer simulation model based on an analytic flow velocity distribution is proposed to generate Doppler ultrasound signals from pulsatile blood flow in the vessels with various stenosis degrees. The model takes into account the velocity field from pulsatile blood flow in the stenosed vessels, sample volume shape and acoustic factors that affect the Doppler signals. By analytically solving the Navier-Stokes equations, the velocity distributions of pulsatile blood flow in the vessels with various stenosis degrees are firstly calculated according to the velocity at the axis of the circular tube. Secondly, power spectral density (PSD) of the Doppler signals is estimated by summing the contribution of all scatterers passing through the sample volume grouped into elemental volumes. Finally, Doppler signals are generated using cosine-superposed components that are modulated by the PSD functions that vary over the cardiac cycle. The results show that the model generates Doppler blood flow signals with characteristics similar to those found in practice. It could be concluded that the proposed approach offers the advantages of computational simplicity and practicality for simulating Doppler ultrasound signals from pulsatile blood flow in stenosed vessels.     

Developments in cardiovascular ultrasound: Part 1: Signal processing and instrumentation

One of the major contributions to the improvement of spectral Doppler and colour flow imaging instruments has been the development of advanced signal-processing techniques made possible by increasing computing power. Model-based or parametric spectral estimators, time-frequency transforms, stationarising algorithms and spectral width correction techniques have been investigated as possible improvements on the FFT-based estimators currently used for real-time spectral estimation of Doppler signals. In colour flow imaging some improvement on velocity estimation accuracy has been achieved by the use of new algorithms but at the expense of increased computational complexity compared with the conventional autocorrelation method. Polynomial filters have been demonstrated to have some advantages over IIR filters for stationary echo cancellation. Several methods of velocity vector estimation to overcome the problem of angle dependence have been studied, including 2D feature tracking, two and three beam approaches and the use of spectral width in addition to mean frequency. 3D data acquisition and display and Doppler power imaging have also been investigated. The use of harmonic imaging, using the second harmonic generated by encapsulated bubble contrast media, seems promising particularly for imaging slow flow. Parallel image data acquisition using non-sequential scanning or broad beam transmission, followed by simultaneous reception along a number of beams, has been studied to speed up ‘real-time’ imaging.     

Analysis of application possibilities of autoregressive modelling to doppler blood flow signal spectral analysis

The spectral analysis of Doppler blood flow velocity signals enjoys wide-spread interest owing to the exhaustive information on the signal which it yields. The discrete Fourier transform is the most extensively used method of analysis. However, the statistical stability of such analysis is poor; spectral smoothing, which improves the statistical stability, also results in greater width and poorer resolution of the spectrum. Autoregressive modelling has been found to give better results when analysing small sample volumes obtained from a pulsed velocimeter (narrow spectrum), even for short data lengths.     

Determination of carotid disease with the application of STFT and CWT methods

In this study, Doppler signals were recorded from the output of carotid arteries of 40 subjects and transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the subjects, and then analyzed by using short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) methods to obtain their sonograms. These sonograms were then used to determine the relationships of applied methods with medical conditions. The sonograms that were obtained by CWT method gave better results for spectral resolution than the STFT method. The sonograms of CWT method offer net envelope and better imaging, so that the measurement of blood flow and brain pressure can be made more accurately. Simultaneously, receiver operating characteristic (ROC) analysis has been conducted for this study and the estimation performance of the spectral resolution for the STFT and CTW has been obtained. The STFT has shown a 80.45% success for the spectral resolution while CTW has shown a 89.90% success.     

Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

In this study, carotid artery Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Doppler signals were processed using fast Fourier transform (FFT) with different window types, Hilbert transform and Welch methods. After these processes, Doppler signals were classified using complex-valued artificial neural network (CVANN). Effects of window types in classification were interpreted. Results for three methods and five window types (Bartlett, Blackman, Boxcar, Hamming, Hanning) were presented as comparatively. CVANN is a new technique for solving classification problems in Doppler signals. Furthermore, examining the effects of window types in addition to CVANN in this classification problem is also the first study in literature related with this subject. Results showed that CVANN, whose input data were processed by Welch method for each window types stated above, had classified all training and test patterns, which consist of 36 healthy, 34 unhealthy and four healthy, four unhealthy subjects, respectively, with 100% classification accuracy for both training and test phases.