血膜中白细胞分布的分形特征刻划

把分形引入血膜中中性粒细胞分布的研究,发现血膜中中性粒细胞分布为多重分形,建立了中性粒细胞多重分形的f(α）－α谱,探索了分形在临床医学和临床检验中的应用。     

基于子波变换及其“WD倒谱”法的肾组织超声散射微结 …

本文提出了一种对超声散射信号分析的新方法－－“ＷＤ倒谱”法,并分别利用该方法和子波变换法对人体正常肾和肾肿瘤组织的超声回波信号进行了分析,对软组织中散射子的平均间距进行了估计,结果表明：两种肾组织散射子的平均间距明显不同;子波谱换及其“ＷＤ倒谱”能很即地反映软组织的微观结构特征,初步说明两种方法是软组织回波信号分析与软组织散射子平均间距定征的有效方法,但“ＷＤ倒谱”提高了子波变换对平均间距估计的分     

人活化T淋巴细胞通过细胞间直接接触激活致敏嗜中性粒细胞

活化T 淋巴细胞可通过其膜表面结合分子与嗜中性粒细胞直接作用,激活嗜中性粒细胞,或使之处于一种致敏状态,增加其对其它刺激的敏感性,从而增强嗜中性粒细胞在抗微生物免疫和组织损伤中的作用。以贴壁法去除单核细胞,尼龙毛吸附去除B 细胞,从正常人外周血PBMC 中得到纯     

基于子波变换及其"WD倒谱"法的肾组织超声散射微结构特征分析

本文提出了一种对超声散射信号分析的新方法--"WD倒谱"法,并分别利用该方法和子波变换法对人体正常肾和肾肿瘤组织的超声回波信号进行了分析,对软组织中散射子的平均间距进行了估计,结果表明:两种肾组织散射子的平均间距明显不同;子波变换及其"WD倒谱"能很好地反映软组织的微观结构特征,初步说明两种方法是软组织回波信号分析与软组织散射子平均间距定征的有效方法,但"WD倒谱"提高了子波变换对平均间距估计的分辨率.     

烹饪油油烟凝结物刺激大鼠腹腔中性粒细胞产生活性氧损伤膜的研究

抽油烟机收集烹饪植物油油烟凝结物，以高速分散器处理为油、水乳浊液，其丙二醛含量显著增高，以此刺激的大鼠腹腔中性粒细胞化学发光明显增强。ＤＰＨ和Ｎ－（３芘）马来酰亚胺标记刺激的中性粒细胞和细胞悬液中存在的微粒体膜脂、膜蛋白质、荧光偏振度均显著增大。提示凝结物乳液可刺激中性粒细胞呼吸爆发产生活性氧引起自身及基质中膜系统的脂和蛋白质损伤。     

基于小波变换和拉普拉斯算子的细胞图像边缘检测方法

目的：介绍一种基于小波变换和拉普拉斯算子的血液细胞图像边缘识别方法。材料和方法：取正常人血样5mL。制成血液细胞图片12片，对血图像进行预处理后，利用小波变换的多分辨率特性滤除细胞图像中的干扰成份。根据血液细胞边缘附近的灰度分布梯度较大的特点，采用拉普拉斯算子及双阈值法对其进行边缘检测和识别。结果和结论：实验结果表明，结合小波变换和拉普拉斯算子的边缘提取算法对血液细胞图像边缘提取有良好的效果．为下一步对血液细胞的形态学分析、分类和识别提供了新途径。     

Chirp-Z变换频谱细化倒谱定征生物软组织散射子平均间距的研究

用信号处理方法,提取生物软组织声散射特征,从而对生物组是进行鉴别,是超声医学的基础研究一直关注的重要课题,生物软组织的超声散射主要由组织内部的散射子引起,散射子平均间距与组织的微结构特征相关。我们分析和总结了一般基于FFT倒谱,AR倒谱等算法波动性较大、分辨率不高产生的原因,提出利用Chirp-Z变换频谱得到细化倒谱,在超声检测中估计生物软组织散射子平均间距及其变化的新方法,并利用该方法对仿体和生物软组织的回波信号进行了分析。结果表明:该方法是软组织的超声散射信号分析和散射子平均间距定征的一种有效方法。     

基于"WD倒谱"法分析人体脾组织的超声散射微结构特征

本文提出了一种对超声散射信号分析的新方法--“WD倒谱”法,并利用该方法对人体正常脾和脾增生组织的回波信号进行了分析,对软组织中散射子的平均间距进行了估计,结果表明：两种脾组织散射子的平均同距明显不同;“WD倒谱”能有效的反映软组织的微观结构特征,说明“WD倒谱”是软组织超声散射信号分析与软组织散射子平均间距定征的一种有效方法。     

重型肝炎时中性粒细胞的形态改变

目的：观察并探讨重型肝炎时血中性粒细胞的形态变化及其意义。方法：２０例重型肝炎患者血中性粒细胞标本、常规固定、包埋制片、电镜检查。结果：所有患者的中性粒细胞均有明显异常,其改变程度与患者肝衰竭分期相平行。主要包括：中性粒细胞质变性,吞噬泡欠缺,细胞器及细胞颗粒减少,线粒体肿胀,细胞核浓缩等。核面积测量、线粒体和细胞颗粒电镜计数结果与上述改变一致。结论：该结果可视为严重肝病患者中性粒细胞功能减退及其     

ONO-AE-248诱发线粒体膜势能降低导致中性粒细胞非凋亡性程序化细胞死亡

目的研究线粒体在ONO—AE-248诱导中性粒细胞非凋亡性程序化细胞死亡中形态结构及功能的变化情况，为进一步确立这一新的死亡形式和找寻其独特的死亡信号转导途径提供实验依据。方法体外新鲜分离人外周血中性粒细胞与ONO—AE-248共同培养，流式细胞术检测其线粒体膜势能的变化；激光共聚焦扫描显微镜观察线粒体形态结构和分布情况。结果ONO—AE-248迅速导致中性粒细胞线粒体膜势能的溃散；ONO—AE-248刺激后中性粒细胞在线粒体形态、结构、分布和胞浆内染色特性等方面均与阴性对照有明显差异。结论线粒体变化是ONO—AE-248诱导中性粒细胞非凋亡性程序化细胞死亡早期形态学上区别于凋亡的显著变化之一。线粒体膜通透性转变以及线粒体膜势能下降可能是ONO—AE-248诱导的非凋亡性程序化细胞死亡的主要原因。     

Bileaflet mechanical valve sound analysis using a continuous wavelet transform

Bileaflet mechanical valve closing sounds have splits, the duration of which is not constant in normally functioning valves. However, no reports have discussed the influences of valve malfunction on the split interval, neither have any studies discussed the fact that mechanical valve closing sound signals must be analyzed using a time-frequency analysis because they are nonstationary signals. The continuous wavelet transform (CWT), a time-frequency analyzing method using mother wavelets modified by scale numbers, was selected in this study for analyzing bileaflet valve closing sounds because it is easy to understand and has no limitations such as the cross-terms in the Wigner–Ville distribution or the tradeoff between time and frequency resolutions of the short-time Fourier transform. This study compares the properties of the mother wavelets of various CWTs and selects one that is suitable for detection of the clear split in bileaflet mechanical valve closing sound signals. This article also establishes a standard frequency analyzing system for bileaflet mechanical valve sounds. A preliminary study with chirp Doppler signals for comparing the frequency properties of the mother wavelets of various CWTs suggested that Ishikawa's modified Morlet CWT has better time and frequency resolution at the highest frequency scale. Morlet/power CWT analysis of normal in vivo bileaflet valve closing sounds of the ST. Jude Medical (SJM), ATS, and Carbomedics (CM) valves demonstrated clear splits of very short interval at the highest level of frequency. Detection of the disappearance of the split by using this analytical method may be the key to identifying bileaflet mechanical valve malfunction in outpatient departments.     

Design of tree structured matched wavelet for HRV signals of menstrual cycle

An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method. The proposed filter banks are used for the decomposition of the HRV signal. For reconstructing the original signal, the tree structure method is used. In this approach, decomposed sub-bands are selected based upon their energy in each sub-band. Thus, instead of using all sub-bands for reconstruction, sub-bands having high energy content are used for the reconstruction of signal. Thus, a lower number of sub-bands are required for reconstruction of the original signal which shows the effectiveness of newly created filter coefficients. Results show that proposed wavelets are able to differentiate HRV variations between phases of the menstrual cycle accurately than standard wavelets.     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity=99.88% and predictivity=99.89%), and a total detection failure of 0.24%.     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity = 99.88% and predictivity = 99.89%), and a total detection failure of 0.24%.     

Wavelet based R-peak detection for heart rate variability studies

Detection of QRS complex in electrocardiogram (ECG) signals is of immense importance in cardiac health prognosis. In this paper a new symmetric wavelet for detection of R-peak is presented, which has been designed based on spectral characteristics and morphology of QRS complex. The detection of R-peak was carried out using this designed wavelet, and with existing symmetric wavelets such as db3, db6, haar and bior2.2. The detection accuracy with this wavelet is 99.99%, which is higher than those with existing symmetric wavelets. The algorithm has been tested on standard databases such as Fantasia database of normal and healthy subjects, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia database, and on self-recorded electrocardiograms of normal subjects and patients under diseased stress. The study of heart rate variability (HRV) through computation of RR-tachogram using the new wavelet has proved to be effective in reliably evaluating HRV parameters.     

外磁驱动轴流式血泵磁场分布及红细胞电磁特性分析

外磁驱动轴流式血泵较强的磁场强度会对血液及周围组织细胞产生影响,因此对血泵及其周围红细胞进行电磁场理论计算和仿真分析。利用ANSYS Electronics Desktop中3D瞬态磁场模块对血泵进行瞬态磁场仿真,用理论方法建立细胞膜磁场分布模型,综合利用3D瞬态电场和磁场模块对红细胞膜及其内外电磁场进行研究。给出了血泵稳定状态时的3D和2D磁感应强度分布云图,得到了细胞膜受到的最大磁感应强度值;通过最大磁感应强度值和血泵工况特点得到红细胞膜电场时域上的分布规律和幅值;综合细胞膜静息电位得到细胞膜电场耦合分布规律;基于以上条件求得细胞膜上感应磁场分布及细胞膜所受最大磁场力。尽管钕铁硼材料剩余磁感应强度很大,但血液和红细胞所受最大磁感应强度值仅为812 mT。由此得到的各项红细胞电磁特性参数值可为红细胞受驱动磁场影响下受到的电磁损伤和血泵的临床应用以及优化设计提供理论基础。     

Electroencephalogram analysis using fast wavelet transform

The continuous wavelet transform is a new approach to the problem of time-frequency analysis of signals such as electroencephalogram (EEG) and is a promising method for EEG analysis. However, it requires a convolution integral in the time domain, so the amount of computation is enormous. In this paper, we propose a fast wavelet transform (FWT) that the corrected basic fast algorithm (CBFA) and the fast wavelet transform for high accuracy (FWTH). As a result, our fast wavelet transform can achieve high computation speed and at the same time to improve the computational accuracy. The CBFA uses the mother wavelets whose frequencies are 2 octaves lower than the Nyquist frequency in the basic fast algorithm. The FWT for high accuracy is realized by using upsampling based on a L-Spline interpolation. The experimental results demonstrate advantages of our approach and show its effectiveness for EEG analysis.     

基于小波变换的心电图QRS波群检测方法研究

本文就心电图信号的QRS波群检测提出了一种基于小波变换的信号特征提取方法,此方法对心电信号中QRS波群的时变特性及几种常见的心电干扰具有较强的鲁棒性.文中我们采用两种不同性质的小波为母小波对含有噪声污染的心电信号进行多尺度的小波分解,在没有预先消噪处理的情况下,较为准确、快速地检测出QRS波群的信息,并且以国际上广泛承认的心电数据库MIT-BIH中的记录对算法进行检验.     

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.