MP-PCNN超声多普勒血流信号声谱图降噪去斑方法

为有效抑制超声多普勒血流信号声谱图中的背景噪声和多普勒斑点,提出了Matching Pursuit(MP)及单向衰减阈值脉冲耦合神经网络(MP-PCNN)模型。首先将分段的多普勒超声信号进行MP循环分解,分离噪声与信号,然后用单向衰减阈值PCNN模型计算声谱图在各个灰度等级上的点火时刻图并定位斑点,用中值滤波器抑制斑点。通过对各种信噪比的仿真超声多普勒血流信号处理,实验结果表明,MP-PCNN方法可有效地滤除声谱图中的噪声与斑点,并较好地保持边缘与细节信息,在主观及客观性能比较上优于同类降噪去斑方法。     

Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations

A novel de-noising method for improving the signal-to-noise ratio of knee-joint vibration signals (also known as vibro-arthrographic (VAG) signals) is proposed. The de-noising methods considered are based on signal decomposition techniques, such as wavelets, wavelet packets and the matching pursuit (MP) method. Performance evaluation with synthetic signals simulated with the characteristics expected of VAG signals indicates good de-noising results with the MP method. Statistical pattern classification of non-stationary signal features extracted from time-frequency distributions of 37 (19 normal and 18 abnormal) MP method-de-noised VAG signals shows a sensitivity of 83.3%, a specificity of 84.2% and an overall accuracy of 83.8%.     

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。     

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。     

基于小波变换的脉搏波信号去噪

人体脉象信号是一种信噪比较低的非平稳随机信号,在分析脉象信号之前去噪是一项十分重要的工作。针对小波变换中的阈值法进行公式上的改进,并利用ZM—ⅢC型智能化中医脉象仪采集到的亚健康人群左关外桡动脉脉搏信号进行去噪处理,实验结果表明,改进后的阈值法可以取得更好的去噪效果。     

Denoising performance of modified dual-tree complex wavelet transform for processing quadrature embolic Doppler signals

Quadrature signals are dual-channel signals obtained from the systems employing quadrature demodulation. Embolic Doppler ultrasound signals obtained from stroke-prone patients by using Doppler ultrasound systems are quadrature signals caused by emboli, which are particles bigger than red blood cells within circulatory system. Detection of emboli is an important step in diagnosing stroke. Most widely used parameter in detection of emboli is embolic signal-to-background signal ratio. Therefore, in order to increase this ratio, denoising techniques are employed in detection systems. Discrete wavelet transform has been used for denoising of embolic signals, but it lacks shift invariance property. Instead, dual-tree complex wavelet transform having near-shift invariance property can be used. However, it is computationally expensive as two wavelet trees are required. Recently proposed modified dual-tree complex wavelet transform, which reduces the computational complexity, can also be used. In this study, the denoising performance of this method is extensively evaluated and compared with the others by using simulated and real quadrature signals. The quantitative results demonstrated that the modified dual-tree-complex-wavelet-transform-based denoising outperforms the conventional discrete wavelet transform with the same level of computational complexity and exhibits almost equal performance to the dual-tree complex wavelet transform with almost half computational cost.     

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。     

超声多普勒血流信号的小波特征提取及分类

利用小波变换对超声多普勒血流信号的最大频率曲线进行多尺度分析， 并从时间－尺度图上提取出模极大值的变化曲线。将这种方法应用到颈动脉血流的分析中，发现：该曲线对于脑血管床正常和异常的病例具有不同的形态。通过对该曲线进行多项式拟合，并将拟合的系数作为非线性变换单元组成的前馈网络（BP网络）的输入进行分类，临床试用效果良好，表明该方法为临床诊断脑血管疾病提供了一个新的依据。     

Preservation of quadrature Doppler signals from bidirectional slow blood flow close to the vessel wall using an adaptive decomposition algorithm

A novel approach based on the phasing-filter (PF) technique and the empirical mode decomposition (EMD) algorithm is proposed to preserve quadrature Doppler signal components from bidirectional slow blood flow close to the vessel wall. Bidirectional mixed Doppler ultrasound signals, which were echoed from the forward and reverse moving blood and vessel wall, were initially separated to avoid the phase distortion of quadrature Doppler signals (which is induced from direct decomposition by the nonlinear EMD processing). Separated unidirectional mixed Doppler signals were decomposed into intrinsic mode functions (IMFs) using the EMD algorithm and the relevant IMFs that contribute to blood flow components were identified and summed to give the blood flow signals, whereby only the components from the bidirectional slow blood flow close to the vessel wall were retained independently. The complex quadrature Doppler blood flow signal was reconstructed from a combination of the extracted unidirectional Doppler blood flow signals. The proposed approach was applied to simulated and clinical Doppler signals. It is concluded from the experimental results that this approach is practical for the preservation of quadrature Doppler signal components from the bidirectional slow blood flow close to the vessel wall, and may provide more diagnostic information for the diagnosis and treatment of vascular diseases.     

用小波变换的模极大值提取胎儿心率的方法

对胎儿监护的主要方法是监听胎儿心率,而超声多普勒测量胎儿心率是一种很好的无创方法.但是,由于测量得到的原始信号成分非常复杂,干扰严重,从而使其对胎心率的提取造成很大困难.本文利用小波变换系数的模的平方值与信号奇异性指数之间的关系,从超声回波信号中提取出了胎儿的心率.由于噪声的小波变换系数随尺度的增大而减小,因此,该方法具有较高的抗干扰能力.通过模拟仿真和实际信号处理,证明该方法能准确地从超声多普勒信号中提取胎心率信号.     

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.     

Time delay estimation using wavelet transform for pulsed-wave ultrasound

The windowed cross-correlation (WCC) technique has recently attracted attention in pulsed-wave (PW) ultrasound for measurement of tissue motion and blood flow velocity because of its performance advantages over the conventional Doppler method. The WCC measures tissue motion and blood flow velocity via estimation of time delays of backscattered signals in two consecutive echoes. In this paper, we propose a wavelet transform-based cross-correlation (WTCC) technique for the time delay estimation in PW ultrasound. The WTCC consists of three steps: (i) computing wavelet transforms (WTs) of received echoes, (ii) computing cross-correlations in the wavelet domain, and (iii) estimating the time delays by maximizing the estimated cross-correlations. Dyadic or continuous wavelets may be used in the proposed approach. The WTCC has a unique feature of using varying time-frequency windows in processing compared with the WCC which only uses a single fixed window. Our computer simulations show that, compared with the WCC, the WTCC provides a better estimation of time delays (lower failure rate and lower estimate error) and its performance is more consistent under various conditions, and more robust with window size. In the simulations, we also tested a specific continuous wavelet for the WTCC that was the emitted pulse itself and found the corresponding WTCC outperforms the WTCC with a regular dyadic wavelet.     

利用小波变换提取声谱图参数的一种方法

超声多普勒声谱技术在医学临床上应用时 ,往往需从声谱上计算声谱参数 ,这些参数已被用作医学诊断的依据。为了利用计算机对声谱图参数进行自动提取 ,本文提出了应用基于Mallat算法分析信号在小波变换后不同尺度性质的方法 ,在最大频率曲线上识别特征点 ,从而获得声谱图参数。应用该方法的仿真和临床实验都取得了较好的结果。     

Passive fetal monitoring by adaptive wavelet denoising method

Fetal Heart Rate (FHR) monitoring is one of the most important fetal well being tests. Existing FHR monitoring methods are based on Doppler ultrasound technique, which has several disadvantages. Passive fetal monitoring by phonocardiography is an appropriate alternative; however, its implementation is a challenging task due to low energy of fetal heart sounds and multiple interference signals presence. In this paper, an advanced signal processing method for passive fetal monitoring based on adaptive wavelet denoising is presented. The method's performance is compared with Doppler ultrasound monitor. The results show 94-97.5% accuracy, including highly disturbed cases.     

基于EMD-ICA的心冲击信号降噪研究

心冲击信号(BCG)是反映心脏机械运动的生理信号,能实现无电极束缚条件下的连续采集测量。但BCG信号微弱,易受干扰,测量时经常会淹没在噪声中。为了消除噪声,有效识别BCG信号特征,提出一种基于经验模态分解(EMD)联合独立分量分析的BCG信号降噪方法。首先,将含噪BCG信号进行EMD分解,获得一系列按频率从高到低的固有模态分量(IMF),采用模态相关准则进行信号层与噪声层的判定;其次,将分界之上的IMF分量构建虚拟噪声通道,基于ICA算法对原始BCG信号进行盲源分离,从而得到降噪后的BCG信号。采集10名健康受试者的BCG信号进行降噪处理。量化评价结果表明,与小波方法和EMD方法相比,降噪后信噪比均显著提高(小波方法11.01±1.58,EMD方法5.19±1.29,所提出方法14.87±3.04,P<0.05),能量百分比也均显著提高(小波方法88.81%±2.81%,EMD方法96.15%±2.96%,所提出方法96.64%±2.92%,P<0.05),从而证明所提出方法降噪效果明显,能够有效还原BCG信号特征。     

Application of the adaptive wavelet transform for analysis of blood flow oscillations in the human skin

An original method for the analysis of oscillations of cutaneous blood flow has been developed, which makes use of laser Doppler flowmetry (LDF) data and is based on the continuous wavelet transform and adaptive wavelet theory. The potential of the method has been demonstrated in experiments with the response of microcirculatory bed to the local linearly-increasing heating of a skin spot. The use of adaptive wavelet transform for analysis of peripheral blood flow oscillations enables one to process short (5 min) LDF signals in a wide frequency range (0.009-2 Hz). The major advantage of the method proposed, as compared to traditional wavelet analysis, has been shown to be a significant reduction of 'border effects'. This makes possible a correct low-frequency component analysis of much shorter LDF signals compared to those used in traditional wavelet processing.     

Doppler ultrasound wall removal based on the spatial correlation of wavelet coefficients

In medical Doppler ultrasound systems, a high-pass filter is commonly used to reject echoes from the vessel wall. However, this leads to the loss of the information from the low velocity blood flow. Here a spatially selective noise filtration algorithm cooperating with a threshold denoising based on wavelets coefficients is applied to estimate the wall clutter. Then the blood flow signal is extracted by subtracting the wall clutter from the mixed signal. Experiments on computer simulated signals with various clutter-to-blood power ratios indicate that this method achieves a lower mean relative error of spectrum than the high-pass filtering and other two previously published separation methods based on the recursive principle component analysis and the irregular sampling and iterative reconstruction, respectively. The method also performs well when applied to in vivo carotid signals. All results suggest that this approach can be implemented as a clutter rejection filter in Doppler ultrasound instruments.     

Directional dual-tree complex wavelet packet transforms for processing quadrature signals

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time–frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time–frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.