听觉诱发脑电小波分量的混沌研究——婴儿痉挛症认知功能障碍的机制探讨

通过对婴幼儿期难治性癫痫———婴儿痉挛症(infantile spasms, IS)听觉诱发脑电细貌混沌特性的研究,探讨与IS相伴的认知功能障碍的发生机制。研究方法是分别记录IS组及正常对照组对象的听觉诱发脑电,经Mexihat连续小波变换后,分别计算信号各尺度小波分量的相关维数。结果表明IS组与正常对照组的各小波分量相关维数的差别主要表现在小波的第3尺度分量上(频带范围是32~64 Hz,主要为γ频带范围),在这个尺度上正常组相关维数明显低于IS组(P<0.05)。相关维数的降低意味着大脑活动自由度的减少,表明大脑的各单元耦合加强。因为正常组脑干内信息传递通道完好,使得大脑各个单元之间的信息耦合较强; IS组则由于脑干功能的异常,存在神经信息传递障碍,进而影响到脑干及其与大脑各个局部之间的信息耦合。小波第3尺度处于较高频率范围(γ频带范围),而在大脑皮层上的基频信号与听觉调频信号经加工后所产生的神经信号正在这一频率范围,且这一信号与大脑高级认知功能密切相关。因此,IS患者γ频带细貌信号的相关维数高于正常值,能够解释IS认知功能发生障碍的原因。     

应用小波变换检测细胞显微荧光图像中的囊泡

文章提出了一种用小波变换来检测生物荧光图像中囊泡的方法。作者用à trous小波对图像进行小波变换,然后求出每层系数的中值绝对偏差σ,并用t=kσ/0.67作为阈值对每层系数进行门限滤波,然后通过提取小波变换系数来重构图像。通过设计实验与常用的“rolling ball”算法对比,发现小波变换算法在低信噪比的情况下,具有更好的灵敏度;对于形状大小不同的信号,具有更好的稳定性;而且对于信号的细节信息具有更好的保真性。     

Non-invasive diagnosis methods of coronary disease based on wavelet denoising and sound analyzing

The heart sound is the characteristic signal of cardiovascular health status. The objective of this project is to explore the correlation between Wavelet Transform and noise performance of heart sound and the adaptability of classifying heart sound using bispectrum estimation. Since the wavelet has multi-scale and multi-resolution characteristics, in this paper, the heart sound signal with different frequency ranges is decomposed through wavelet and displayed on different scales of the resolving wavelet result. According to distribution features of frequency of heart sound signals, the interference components in heart sound signal can be eliminated by selecting reconstruction coefficients. Comparing de-noising effects of four wavelets which are haar, db6, sym8 and coif6, the db6 wavelet has achieved an optimal denoising effect to heart sound signals. The de-noising result of contrasting different layers in the db6 wavelet shows that decomposing with five layers in db6 provide the optimal performance. In practice, the db6 wavelet also shows commendable denoising effects when applying to 51 clinical heart signals. Furthermore, through the clinic analyses of 29 normal signals from healthy people and 22 abnormal heart signals from coronary heart disease patients, this method can fairly distinguish abnormal signals from normal signals by applying bispectrum estimation to denoised signals via ARMA coefficients model.     

基于三轴加速度传感器的跌倒检测研究

本文基于MMA7260QT加速度传感器获取的人体运动加速度信号,采用人体加速度向量幅值(SVM)和人体加速度向量区域值(SMA)描述了老年人的运动状态,检测人体跌倒,具有良好的准确性和实时性。采用bior3.3小波分析,在轮廓的基础上,最大程度上保留了细节,有效的去除噪声对特征量的干扰。本文提出了人体跌倒检测算法,大大降低了误判率和漏判率。首先,检测人体SVM是否超过阈值进行第一级跌倒检测,区别出人体日常活动(ADL)和跌倒;其次在此基础上,检测第一级各个跌倒的SMA值,是否超过阈值,判断跌倒和疑似跌倒。当两次判断都检测到跌倒发生时,报警。     

Motor strategy patterns study of diabetic neuropathic individuals while walking. A wavelet approach

The aim of this study was to investigate muscle?s energy patterns and spectral properties of diabetic neuropathic individuals during gait cycle using wavelet approach. Twenty-one diabetic patients diagnosed with peripheral neuropathy, and 21 non-diabetic individuals were assessed during the whole gait cycle. Activation patterns of vastus lateralis, medial gastrocnemius and tibialis anterior were studied by means of bipolar surface EMG. The signal?s energy and frequency were compared between groups using t-test. The energy was compared in each frequency band (7–542 Hz) using ANOVAs for repeated measures for each group and each muscle. The diabetic individuals displayed lower energies in lower frequency bands for all muscles and higher energies in higher frequency bands for the extensors? muscles. They also showed lower total energy of gastrocnemius and a higher total energy of vastus, considering the whole gait cycle. The overall results suggest a change in the neuromuscular strategy of the main extensor muscles of the lower limb of diabetic patients to compensate the ankle extensor deficit to propel the body forward and accomplish the walking task.     

高维蛋白质波谱癌症数据特征提取

高维蛋白质波谱癌症数据分析,一直面临着高维数据的困扰。针对高维蛋白质波谱癌症数据在降维过程中的问题,提出基于小波分析技术和主成分分析技术的高维蛋白质波谱癌症数据特征提取的方法,并在特征提取之后,使用支持向量机进行分类。对8-7-02数据集进行2层小波分解时,分别使用db1、db3、db4、db6、db8、db10、haar小波基,并使用支持向量机进行分类,正确率分别达到98.18%、98.35%、98.04%、98.36%、97.89%、97.96%、98.20%。在进一步提高分类识别正确率的同时,提高了时间率。     

基因表达数据小波降噪与肿瘤识别

建立了基于小波降噪和支持向量机的结肠癌基因表达数据肿瘤识别模型.对试验数据进行小波分解,并利用交叉验证的方法计算试验样本的平均分类准确率,确定小波函数与小波分解层数;引入能量阈值方法对小波分解系数进行阈值处理,达到降噪的目的;提出了基因分类贡献率与主成分分析结合的方法,提取结肠癌样本数据特征;利用支持向量机强大的非线性映射能力,实现对结肠癌样本数据的非线性分类.为了减弱样本集的划分对分类准确率的影响,本文采取Jackknife检验方法对支持向量分类器的分类器检验,其分类准确率为96.77%.试验结果证明了该方法的有效性,该方法对结肠癌的识别具有一定的参考价值.     

On the synchrony of steady state visual evoked potentials and oscillatory burst events

In this paper, we investigate the large-scale synchrony of EEG oscillatory bursts, during stimulation by a flickering square of light. Whereas most studies focus on averaged raw EEG responses, this study considers oscillatory events within EEG of single trials, which leads to various new insights. We recorded EEG signals before, during and after stimulation by a flickering square of light in medium (16 Hz) and high frequency (32 Hz) ranges. Similar oscillatory bursts, to those observed in spontaneous EEG, can be found in single-trial synchrony of steady state visual evoked potentials (SSVEP). These bursts are extracted from the EEG of single trials using bump modeling. Stochastic event synchrony method is applied to those events, which quantifies synchronies of oscillatory bursts on a large-scale basis. Those oscillatory patterns have a significantly higher degree of co-occurrence during SSVEP, uncorrelated with ongoing signal synchrony. It means that EEG oscillatory patterns are presumably an outcome of brain activity, rather than a mere side effect of ongoing EEG. They undergo a consistent reorganization during visual stimulation, preferentially along the visual pathway, depending on magno or parvo stimulations. Flickering stimuli may induce some cognitive side-effects depending on the stimulation frequency.

基于双正交小波变换的宫缩信号消噪

小波分析的信号消噪方法是现代信号处理中的重要组成部分,小波基的不同选取将直接影响消噪的效果.本文在全局阈值的标准下,基于不同噪声水平,讨论了小波基的正交性和线性相位性对消噪结果的影响,提出了选取小波基的一般方法,最后利用双正交小波基在软阈值标准下实现了对宫缩信号的消噪处理,并取得了较好的效果.     

基于小波和BP网络的视觉诱发电位分类识别

提出了小波分解与BP网络相结合的方法来识别视觉诱发电位（Visual Evoked Potential,VEP）。先用小波分解对VEP进行特征提取和降维。然后用BP网络进行分类识别。