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。     

Separation method of the Doppler ultrasound blood flow from vessel wall pulsation signals

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.     

眼部血流多普勒信号的检测研究

研制以超声波为载体的电路系统,探测眼部血流多普勒信号,并进行频谱分析。硬件设计包括:信号控制、超声波发射/接收、高频放大、正交检波以及距离选通等电路;将检测信号数字化后,进行数字信号处理并绘制频谱分析图。同时,构建物理仿真模型验证系统功能;对人体正常眼组织的检测,获得眼动脉中血流信号的频谱分析图。结果表明,系统有效实现了血流流速的检测、血流方向的辨别和距离选通等功能;对人体正常眼组织检测所获得的频谱分析图中三峰双切迹状明显,与正常人体眼动脉血流参数吻合。作为专用眼科超声多普勒诊断仪的前期探索性研究,本设计能够准确的提取被测血流的多普勒频移信号,具有很好的应用前景。     

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。     

多普勒血流信号中频谱展宽效应产生的原因及其影响因素

在连续波多普勒血流测量系统中，实际接收到的频移信号是血流束祺矢量的综合反映，是速度矢量在各方面上产生频移信号的叠加，本文建立一个模型来分析在连续波多产为勒血流测量系统中频谱展宽产生的原因以及其影响因素，并且对于圆形探头给出了详细的讨论结果。     

高通滤波器对多普勒血流信号的影响

在绝大多数连波多普勒血流测量系统中，通常采用一个高通滤波器来滤除由固定目标和血管壁产生的强反射信号。本文从理论上推导出引入高通滤波器会导致血流平均速率估值偏高。     

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。     

血流速度绝对值的几种超声多普勒测量方法

作者综述了迄今为止国内外有关血流速度绝对值无创测量的多种超声多普勒测量方法,并指出每种方法的优缺点。     

多普勒血流信号仿真模型的建立及谱估计方法的研究

本文建立了多普勒血流信号的仿真模型，在计算机上完成了血流信号谱估计方法的仿真研究。最后，通过对人体颈动脉血流信号的研究，进一步分析了各种谱估计方法的有效性。所得结论对多普勒血流信号谱分析器的设计有重要的指导意义。     

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.     

Brachial arterial blood flow during static handgrip exercise of short duration at varying intensities studied by a Doppler ultrasound method

The purpose of this study was to determine forearm blood flow changes during static handgrip exercise at different intensities in relation to heart rate and blood pressure. Seven active women performed static handgrip exercise at intensities of 10, 30, 50 and 70% maximum voluntary contraction (MVC) in a supine position for 1 min. During exercise at different intensities, the brachial arterial blood flow (Doppler ultrasound method), calculated from vessel diameter, flow velocity and heart rate (measured by ECG), increased to a similar level (137.3 ± 20.2 – 160.9 ± 26.1 mL min^?1) from pre-exercise control value (87.5 ± 14.1 mL min^?1). These increases at the lower intensities were attributable to increased in-flow during one cardiac cycle, whereas at the higher intensities, they were due to increased heart rate. Both systolic and diastolic blood pressure (Finapres) changes increased from 10% MVC (16.1 ± 3.4, 9.0 ± 1.7 mmHg) up to 50% MVC (33.8 ± 6.7, 25.0 ± 4.9 mmHg), but were disproportionately more elevated at 70% MVC (46.1 ± 7.9, 42.9 ± 8.9 mmHg), suggesting neural vasoconstriction had occurred. Immediate post-exercise hyperaemia, used as an indicator of poor blood supply, became greater as the exercise intensity increased. These results suggest that the brachial arterial blood flow was maintained at a similar level during 60-s static handgrip exercise at different intensities by elevating the blood pressure and heart rate, which probably counteracted the increased intramuscular pressure and neural vasoconstriction occurring at the higher exercise intensity. The magnitude of the post-exercise hyperemic response increased as exercise level increased despite increased blood flow to the arm during exercise. This suggests a worsening imbalance in oxygen delivery in forearm muscles at higher levels of exercise.     

Doppler ultrasound investigation of uterine and ovarian blood flow in infertility and early pregnancy

This review describes the current use of Doppler ultrasoundto examine blood flow in the uterus and ovaries in infertilepatients and during early pregnancy. The basics of Doppler ultrasoundand the different methods of measuring blood flow are discussedfrom the viewpoint of the clinician who may be unfamiliar withDoppler physics and terminology. Normal values in the menstrualcycle and the relationship of uterine and ovarian blood flowto infertility and to implantation following in-vitro fertilizationare presented. Normal values for uterine blood flow in the first16 weeks of pregnancy and the effect of sex steroids and ovulationinduction on their values are described. The possible relationshipof defective uterine blood flow, and the effect of drugs areexplored. The findings of this review indicate that Dopplerblood flow studies may provide significant information aboutpossible causes of some disorders of infertility and early pregnancyand methods of treatment for the same.     

多普勒超声观察创伤性寰枢椎不稳手术前后椎动脉血流的变化

目的探讨创伤性寰枢椎不稳患者内固定手术前后椎动脉血流的变化。方法32例创伤性寰枢椎不稳患者,其中男性22例,女性10例;年龄22～57岁,平均年龄38岁。手术前及内固定术后,均应用彩色多普勒超声测量颈椎1、2间和颈椎5、6间椎动脉血流速度、阻力指数。并对检查结果进行对比分析。结果32例创伤性寰枢椎不稳患者术前椎动脉血流39侧异常,25侧正常。内固定术后39侧异常者中35侧恢复正常,4侧无变化,3侧术前正常变为术后异常。结论创伤性寰枢椎不稳可导致椎动脉血流变化,手术复位内固定后增加了寰枢椎稳定性,可改善椎动脉血液供应。     

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.     

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。     

Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals

The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the PNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the PNNs trained on these features achieved high classification accuracies.     

Versatile microcomputer-based system for the capture,storage and processing of spectrum-analysed Doppler ultrasound blood flow signals

The evaluation of any method of analysis of Doppler ultrasound blood flow signals is involved and time consuming because of the considerable time necessary to investigate a statistically significant representative population of arteriopathic blood flow waveforms. To overcome these problems we have developed a microcomputer-based system for the capture, storage and processing of spectrum-analysed Doppler ultrasound blood flow signals. This system allows the collection and storage on floppy disk of waveforms from many sites in a large population of arteriopaths and their later analysis using any desired method. Having thus created on disk a suitable population of arteriopathic waveforms the evaluation of any method of waveform analysis, whether existing or new, is a much more convenient and far less time-consuming process. The system described is extremely versatile, for example in addition to the collection of data for postprocessing the system is also used for the real-time analysis of blood flow waveforms.