高分辨心电信号的时频平面维纳滤波研究进展与应用

时频平面上的维纳滤波器（ＴＦＰＷ）是一个建立在后验维纳滤波器原理上的新方法，其目的为了增强整体平均的效果。这篇文章从数学角度发展了ＴＦＰＷ滤波器，并且利用基本信号诸如正弦波、线性调频信号和真实的高频心电信号（ＨＲＥＣＧ）来估计这种滤波器的性能，ＴＦＰＷ滤波器的显著特征就是在于利用时频平面去适应信号的非平稳性。通过使用后验的整体计算统计学方法，这个滤波器可使其自身与被估计信号的时频结构相匹配，这个方     

高分辨心电信号的时频平面维纳滤波研究进展与应用

时频平面上的维纳滤波器（ＴＦＰＷ）是一个建立在后验维纳滤波器原理上的新方法，其目的为了增强整体平均的效果。这篇文章从数学角度发展了ＴＦＰＷ滤波器，并且利用基本信号诸如正弦波、线性调频信号和真实的高频心电信号（ＨＲＥＣＧ）来估计这种滤波器的性能，ＴＦＰＷ滤波器的显著特征就是在于利用时频平面去适应信号的非平稳性。通过使用后验的整体计算统计学方法，这个滤波器可使其自身与被估计信号的时频结构相匹配，这个方法一般都能被应用到任何具有确定的时频结构和整体加性噪声的周期信号簇中。结论：与常规的整体平均方法相比较，使用ＴＦＰＷ滤波器有可能在估计的信号保真度和噪声减小上获得显著的改善     

NFG和TGF—β1的表达与滋养细胞肿瘤相关性的免疫组化研究

为了探讨非分蚀性葡萄胎（ＨＭ）转化为侵蚀性葡萄胎（ＩＨＭ）的细胞生物学机制，本研究采用免疫组织化学法观察了ＮＧＦ和ＴＧＦ－β１在ＨＭ和ＩＨＭ中的分布。结果显示：在ＨＭ绒毛中，合体滋养细胞（ＳＴ）和新生基质细胞ＮＧＦ和ＴＧＦ－β１呈阳性和阳性；老年基质细胞和细胞滋养细胞（ＣＴ）呈阴性；在ＨＭ的中间型滋养细胞，ＮＧＦ呈阳强性着色，ＴＧＦ－β１阴性；在ＨＭ绒毛干中，ＳＴ的ＮＧＦ呈强性阳，ＮＧＦ和ＴＧＦ－     

瞬态诱发耳声发射的时频分析

本文简要介绍了基于Ｗｉｇｎｅｒ Ｖｉｌｌｅ分布及其改进型的时频分析方法及其数字实现方法 ,然后 ,利用计算机计算了来自正常人耳的瞬态诱发耳声发射 (ＴｒａｎｓｉｅｎｔＥｖｏｄｅｄＯｔｏａｃｏｕｓｔｉｃＥ ｍｉｓｓｉｏｎｓ ,ＴＥＯＡＥｓ)的时频分布。根据试验和计算结果 ,分析了其时频分布的特点 ,并对不同的频率成分与潜伏期的关系进行了描述。0　引言耳声发射 (ＯｔｏａｃｏｕｓｔｉｃＥｍｉｓｓｉｏｎｓ ,ＯＡＥｓ)是一种产生于耳蜗 ,经听骨链及鼓膜传导释放入外耳道的音频能量信号 ,它以机械振动的形式起源于耳蜗 ,是由耳蜗耗…     

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。     

包含离散谱线的电生理信号的参数模型谱估计问题

参数模型谱估计是的这二十多年来得到词发展的现代谱估计方法。根据极大熵原理，ＡＲ谱估计受到了普遍重视。极大熵谱估计要求信号必须是具有连续谱分布函数的一般线性疗列。然而在生物医学工程中遇到的实际信号，其谱函数往往包含很尖锐的谱峰（如脑电信号的α峰），甚至包含离散谱线。实验证明，这种情况下对谱峰频率的估计仍然很精确，但对谱幅估计误差较大，国外近些年提出的一种最佳Ｂｕｒｇ修正算法，有一定的改善。我们从工程     

人眼时空调制传递函数测试系统

本文介绍在激光视网膜空间ＭＴＦ测试装置基础上，增加时间ＭＴＦ测试，自动打印和显示ＩＶＡ、ＭＴＦ、频率等参数；打印不同频率下的ＭＴＦ－ＩＶＡ曲线的单片机控制系统。     

基于小波能量熵特征的阻抗胃动力信号识别

采用生物电阻抗技术从人体上腹部体表提取的电阻抗信号,不但包含了常规的胃蠕动频率特征,而且携带有反映胃动力状况的更深层次的信息.提取并分析这些信息,对胃动力的检测与评价具有重要意义.对20名糜烂性胃炎患者的胃阻抗和胃电信号进行研究,经过小波滤波去噪后,进行多层小波包变换,计算小波能量熵并作为特征向量,采用3层BP神经网络进行模式分类.经一周治疗后,14名患者胃阻抗和胃电信号的小波能量熵值下降,以小波能量熵为特征向量的BP神经网络对治疗前后的识别正确率为80%.结果表明,小波能量熵能够从整体上表征胃动力信号时域和频域能量分布的复杂程度,可为胃肠病患者的疗效评价提供有效的特征描述.     

基于改进的伪Wigner分布检测癫痫EEG棘波的方法

本文提出了一种新的检测癫痫EEG棘波的方法，采用改进的伪Wigner分布对脑电信号进行时频变换，这种时频分布不仅能够有效地消除交叉项，而且可以快速实现，在时频平面上，以每个时间点上的中心频率划分背景脑电和癫痫样瞬态特征，通过一个镜像滤波器提取瞬态分量，最后，利用瞬态分量的能量局部极值检测棘波，临床应用表明，这种方法能够快速有效地从背景脑电中提取癫痫样瞬态特征。     

抗人肿瘤坏死因子—α单链抗体基因在大肠杆菌中的融合表达

目的：将抗ｈＴＮＦ－α单链抗体基因克隆入融合表达载体ｐＧＥＸ４Ｔ－１中，以期得到ＧＳＴ－ＳｃＦｖ融合表达蛋白。方法：将限制性内切酶酶切拼接法获得的Ｅ６ＳｃＦｖ基因克隆入融合表达载体ｐＧＥＸ４Ｔ－１中，转化大肠杆菌ＤＨ５α，经异丙基－β－Ｄ－硫代半乳糖苷（ＩＰＴＧ）诱导，１２％ＳＤＳ－ＰＡＧＥ检测表达产物，光密度扫描和Ｗｅｓｔｅｒｎ－ｂｌｏｔ验证表达产物。结果：ＳＤＳ－ＰＡＧＥ显示，Ｅ６ＳｃＦｖ表达产物约为５２ｋｕ左右，与预期的结果相符；光密度扫描结果表明，ＧＳＴ－Ｅ６ＳｃＦｖ融合蛋白占菌体总蛋白的４０％；Ｗｅｓｔｅｒｎ－ｂｌｏｔ证实，在相应分子量处，有ＧＳＴ－Ｅ６ＳｃＦｖ融合蛋白的显色印迹；进一步对表达产物的形式分析，ＧＳＴ－Ｅ６ＳｃＦｖ融合蛋白的表达产物为包涵体形式。结论：在大肠杆菌中成功地表达了抗ｈＴＮＦ－α单链抗体基因与谷胱甘肽巯基转移酶（ＧＳＴ）基因的融合蛋白     

Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects' electrogastrogram

Electrogastrography (EGG) is a noninvasive way to record gastric electrical activity of stomach muscle by placing electrodes on the abdominal skin. Our goal was to investigate the frequency of abnormalities of the EGG in real clinical diabetic gastroparesis patients using WT method and to compare performance of STFT and WT methods in the case of time–frequency resolution. The results showed that WT sonograms can be used to classify patients successfully as healthy or sick. And also, due to the fact that the WT method does not suffer from some intrinsic problems that affect the STFT method, one can see that the WT method can help improve the quality of the sonogram of the EGG signals.     

Minimization of EOG artefacts from corrupted eeg signals using a neural network approach

In this paper, we propose a neural network (NN) approach to the enhancement of EEG signals in the presence of EOG artefacts. We recast the EEG enhancement problem into the optimization framework by developing an appropriate cost function. The cost function is nothing but the energy in the enhanced EEG signal obtained through a nonlinear filter formulation, unlike the conventionally-used linear filter formulation. The minimization property of feedback-type neural networks is exploited to solve this problem. An analysis has been performed to characterize the stationary points of the suggested energy function. The hardware set-up of the developed neural network has also been derived. The optimum nonlinear filter coefficients obtained from this minimization algorithm are used to estimate the EOG artefact which is then subtracted from the corrupted EEG signal, sample by sample, to get the artefact minimized signal. The time plots as the LP spectrum show that the proposed method is very effective. Thus the power and efficacy of the NN approach have been exploited for the purpose of minimizing EOG artefacts from corrupted EEG signals.     

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.     

Computerized heart sounds analysis

This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency (TF) PCG signal characteristics and consequently aid diagnosis. Similarly, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties. The studied techniques (FT, STFT, WD, CWT, DWT and PWT) of analysis can thus be regarded as complementary in the TF analysis of the PCG signal; each will relate to a part distinct from the analysis in question.     

Time-frequency analysis of the second cardiac sound in phonocardiogram signals

The paper is concerned with the analysis of the phonocardiogram signals (PCG) in the time-frequency domain. Three techniques are studied and evaluated in PCG signal analysis. These are the short time Fourier transform (STFT), the Wigner distribution function (WD) and the continuous wavelet transforms (CWTs). The analysis is first carried out on the second cardiac sound (S2) in order to show the aptitude of each method in distinguishing the internal components of this sound. The results we obtain show that the STFT cannot detect the two internal components of S2 (A2 and P2, respectively, the aortic and pulmonary components). The WD can provide time-frequency characteristics of S2, but with insufficient diagnostic information: the two components are not accurately detected and appear to be only one component. It is found that the CWT (it can also provide the time-frequency characteristic of S2) is capable of detecting its two components, A2 and P2, allowing therefore the measurement of the delay between them. This delay, called the split, is very important in the diagnosis of many pathological cases, as it is emphasized in the results we obtain by applying the CWT on different pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect).     

Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks

Electrogastrogram is a surface measurement of gastric myoelectrical activity, and electrogastrography has been an attractive method for physiological and pathophysiological studies of the stomach due to its nonivasive nature. Motion artifacts, however, ruin the electrogastrogram (EGG), and make the analysis very difficult and sometimes even impossible. They must be eliminated from EGG signals before analysis. Up to now, this can only be done by visual inspection, which is not only time-consuming but also subjective. In this study, a method using feature analysis and neural networks has been developed to realize automatic detection and elimination of the motion artifacts in EGG recordings by computer. Experiments were conducted to investigate the characteristics of different motion artifacts. Useful features were extracted, and different combinations of the features used as the input of the neural network were compared to obtain the optimal performance for the detection of motion artifacts using the artificial neural network.     

Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks

A neural network approach is proposed for the automated classification of the normal and abnormal EGG. Two learning algorithms, the quasi-Newton and the scaled conjugate gradient method for the multilayer feedforward neural networks (MFNN), are introduced and compared with the error backpropagation algorithm. The configurations of the MFNN are determined by experiment. The raw EGG data, its power spectral data, and its autoregressive moving average (ARMA) modelling parameters are used as the input to the MFNN and compared with each other. Three indexes (the percent correct, sumsquared error and complexity per iteration) are used to evaluate the performance of each learning algorithm. The results show that the scaled conjugate gradient algorithm performs best, in that it is robust and provides a super-linear convergence rate. The power spectral representation and the ARMA modelling parameters of the EGG are found to be better types of the input to the network for this specific application, both yielding a percent correctness of 95% on the test set. Although the results are focused on the classification of the EGG, this paper should provide useful information for the classification of other biomedical signals.     

Continuous Wavelet Analysis as an Aid in the Representation and Interpretation of Electrogastrographic Signals

This paper presents an application of the continuous wavelet transform (CWT) in the analysis of electrogastrographic (EGG) signals. Due to the nonstationary nature of EGG signals, the CWT method, which uses multiresolution scaled windows, gives a better time-frequency resolution than the short-time Fourier transform, which uses a fixed window. Spike activity due to gastric contraction was investigated through experiments on dogs. During spike activity we observed an increase in magnitude of the slow wave and the appearance of a low frequency component with half the frequency of the slow wave. Studies of the EGG signals from the small intestine are also presented to investigate the hypothesis that its slow wave might be confounded with spike activity in the stomach due to the similarity of their frequency ranges. © 1998 Biomedical Engineering Society. PAC98: 0230-f, 8759Wc     

一种新型胃动力检测系统的设计与实现

目的 以生物阻抗技术为基础设计胃动力检测系统.方法 检测系统由采集平台和分析平台2部分构成.前者实现胃电和胃动力阻抗信号的同步采集和数据传输,后者对提取的胃动力阻抗信号和胃电运用小波多分辨分析进行处理,并提取信号的时域、频域和支配频率等参数完成统计分析.结果 实验结果表明功能性消化不良患者（病理组）的胃运动正常节律百分比（PNF）和正常功率百分比（PNP）明显小于健康大学生志愿者（对照组）,病理组的节律变异系数（FIC）和功率变异系数（PIC）大于对照组.结论 胃动力检测系统的实现不仅使胃电和胃阻抗信息得到同步获取,同时为胃动力研究和临床检测提供一种电-机复合过程的检测方法.     

一种新型胃动力检测系统的设计与实现

目的 以生物阻抗技术为基础设计胃动力检测系统.方法 检测系统由采集平台和分析平台2部分构成.前者实现胃电和胃动力阻抗信号的同步采集和数据传输,后者对提取的胃动力阻抗信号和胃电运用小波多分辨分析进行处理,并提取信号的时域、频域和支配频率等参数完成统计分析.结果 实验结果表明功能性消化不良患者(病理组)的胃运动正常节律百分比(PNF)和正常功率百分比(PNP)明显小于健康大学生志愿者(对照组),病理组的节律变异系数(FIC)和功率变异系数(PIC)大于对照组.结论 胃动力检测系统的实现不仅使胃电和胃阻抗信息得到同步获取,同时为胃动力研究和临床检测提供一种电-机复合过程的检测方法.