麻醉监测脑电分析的研究

采用自适应傅立叶估计的方法对麻醉的脑电特征进行自动监测和分析,以傅立叶系数表示和跟踪诱发脑电信号的特征,并用神经网络分类器对特征进行分类,从而估计出麻醉深度.仿真结果表明了该方法的有效性.     

基于嵌入式系统的诱发脑电信号采集仪器设计

本文在建立了一个脑电信号源模型的基础上,仔细分析了脑电信号放大器的干扰与噪声,并且提出了抑制干扰与噪声的一系列措施.然后根据脑电信号的特点着重讨论了前置放大器的设计,介绍了整个脑电信号采集系统的组成,以及软件设计方面的任务分析、数字滤波器以及关键的数据结构,最后讲解了诱发事件与诱发脑电信号的同步关系和数据的封装.     

基于小波分析的脑电信号处理

为去除脑电信号采集过程中存在的噪声信号,提出了基于小波阈值去噪的脑电信号去噪。以小波阈值降噪为基础,首先利用db4小波对脑电信号进行5尺度分解,然后采用软、硬阈值与小波重构的算法进行去噪。通过对MIT脑电数据库中的脑电信号进行仿真,结果表明,采用软阈值方法有效去除了噪声,提高了脑电信号的信噪比。     

基于关联维数的高压电机定子绕组局部放电信号模式识别

提出了利用分形理论对高压电机定子绕组局部放电信号进行处理的方法,得到局部放电信号的关联维数,并将其作为特征参量对几种典型的局部放电信号进行模式识别。局部放电信号是非线性、非平稳随机信号。因此采用非线性理论中的分形理论对其进行分析,即计算关联维数。考虑到相空间重构中嵌入维数和时间延迟对关联维数精度的影响,采用联合算法确定2个参数。仿真结果表明,关联维数用于局部放电信号模式识别是行之有效的。     

麻醉深度监测系统的优化算法研究

麻醉深度监测对提高麻醉质量,保障患者的手术期安全与康复具有极为重要的意义。系统地回顾了脑电信号分析算法,主要包括脑电双频指数、麻醉趋势指数、脑状态指数等多种不同参数的算法在麻醉深度监测中的应用研究及进展情况。首先比较了这些参数的脑电分析算法在临床应用中的优缺点,接着提出了具有优秀去噪能力的排序熵指数算法,并引入反向映射神经网络对脑电双频指数、麻醉趋势和排序熵指数进行校正优化,以便更精确地实现麻醉深度监护。最后展望了今后其应用于麻醉深度监测领域的发展前景。     

混沌信号与噪声

本文通过三种方法分别研究了Lorenz模型和高斯白噪声，获得了混沌信号和噪声在这些方法下的不同表现特征。提出了根据关联维数（correlationdimension）判断降噪效果的方法，并进一步探讨了采用主分量分析（principalcomponentanalysis）确定嵌入维数（embeddingdimension）和去除混沌信号中噪声的可行性。最后得出结论：主分量分析用于确定嵌入维数和降低混沌信号中的噪声是不合适的。     

基于听觉E RP功能脑网络特征和SVM的测谎方法研究

基于图论理论的脑网络分析方法近年来在认知脑科学研究中起到了非常重要的作用,而基于事件相关电位（Event-Related Potentials,ERP）的传统测谎方法一直都专注于对某一特定通道上的脑电信号进行分析,针对传统方法中使用少数通道并不能够全面的反映人在说谎状态下大脑整体认知功能特征的缺点,本文提出了基于脑网络特征的测谎方法,通过听觉刺激诱发事件相关电位ERP,记录脑区多通道脑电信号,通过讨论各导联之间的相位延迟指数来构建脑功能网络,计算7类脑网络特征参数作为判别指标。分析被试在说谎和无辜状态下的网络特征参数,使用支持向量机对实验数据进行分类判断,结果表明：本文提出的方法有较高的判别准确率,优于目前判别方法的平均值,证明了本方法的测谎有效性。     

边缘学科分支最新动向

TN一051 2004021276荃于状态空间的EEG信号分析/游荣义,徐慎初(厦门大学)11厦门大学学报.一2003,42(6)一736一740为录求脑电(Eleetroeneephalogram,EEG)分析的新途径,提出了一种基于状态空间的脑电分析的新方法.利用状态空间点间的欧氏距离来计算脑电状态空间的态密度和态方差.实验表明,态密度和态方差不仅计算简单,而且与脑电的关联维数和Lyapunov指数相比,更能有效地反映混沌系统非线性动力学的特征.此外,还计算了基于距离协方差的脑电信号的奇异谱,并对结果作了分析.图7参11(木)发现人脑16导EEG信号具有不同强度的分形特性且存在稳…     

脑电信号识别方法的研究

人类对大脑的探索已进入了数字化时代,随着脑信号检测技术的日益成熟以及人工智能算法的研究进展,脑信号的解读研究也展现出越来越多的成果.本文首先介绍当下大脑信号获取的医学方法,而后简述脑电信号的特征提取以及分类识别方法,接着列举脑电识别的前沿研究,最后对脑电信号识别的数据应用领域进行展望.     

医用电子技术与设备

0618940X线乳腺图像中微钙化检测的快速排除无病变区域法〔刊,英〕/贾新华//南京航空航天大学学报(英文版).—2006,23(1).—52-58(E)0618941脑信息处理动态特征研究〔刊,中〕/陈冬冰//北京理工大学学报.—2006,26(3).—221-225(L)为提取脑信息处理过程中的动态特征参数,提出运用基于相空间重构的思想的时间序列分维算法(G-P算法)。讨论了G-P算法的3个重要参数(即无标度域、嵌入维数和延时)的确定规则,记录大脑在不同状态下的EEG信号并计算其关联维数。实验结果表明,EEG关联维数能够反映脑信息处理过程中的神经元群活动状态,可作为脑信…     

A system for tracking changes in the mid-latency evoked potentialduring anesthesia

Describes a method to measure changes in the mid-latency auditory evoked potential (MLAEP) during anesthesia. It is claimed that the position of the Nb-trough of the MLAEP indicates the level of consciousness. The component shows graded changes corresponding to the dose of anesthetic and it exhibits stable reproducible properties between different subjects. The authors propose a system that reduces the disturbances in an averaged MLAEP using fewer realizations than in the standard averaging procedure. The resulting smoothing error is reduced if the number of stimulus is decreased. Unfortunately, the variance of the waveform estimate is, thereby, increased. An improved method must be used in order to estimate the Nb-trough within a prescribed time interval of one minute. The procedure is based on inherent properties of the MLAEP and the noise. A simulation and examples of the performance on real data recorded during surgery are shown     

基于时间自相关函数的诱发电位单通道单次提取方法

单通道信号分离一直是信号处理领域中的重要问题,当观测数据较少时,该问题尤其困难。本文提出一种以时间自相关函数作为目标函数的最优化分离方法。先使用小波模极大值法来估计出迭代初始值与源信号的时间自相关函数,然后得到的最优解就是对待分离信号的估计。实验结果表明,该方法能够较好地应用于诱发电位信号的单通道单次提取。      

Modeling and estimation of single evoked brain potential components

Presents a novel approach to solving the single-trial evoked-potential estimation problem. Recognizing that different components of an evoked potential complex may originate from different functional brain sites and can be distinguished according to their respective latencies and amplitudes, the authors propose an estimation approach based on identification of evoked potential components on a single-trial basis. The estimation process is performed in 2 stages: first, an average evoked potential is calculated and decomposed into a set of components, with each component serving as a subtemplate for the next stage; then, the single measurement is parametrically modeled by a superposition of an emulated ongoing electroencephalographic activity and a linear combination of latency and amplitude-corrected component templates. Once optimized, the model provides the 2 assumed signal contributions, namely the ongoing brain activity and the single evoked brain response. The estimator's performance is analyzed analytically and via simulation, verifying its capability to extract single components at low signal-to-noise ratios typical of evoked potential data. Finally, 2 applications are presented, demonstrating the improved analysis capabilities gained by using the proposed approach. The first application deals with movement related brain potentials, where a change of the single evoked response due to external loading is detected. The second application involves cognitive event-related brain potentials, where a dynamic change of 2 overlapping components throughout the experimental session is detected and tracked     

Depth of anesthesia estimation and control [using auditory evokedpotentials]

A fully automated system was developed for the depth of anesthesia estimation and control with the intravenous anesthetic, Propofol. The system determines the anesthesia depth by assessing the characteristics of the mid-latency auditory evoked potentials (MLAEP). The discrete time wavelet transformation was used for compacting the MLAEP which localizes the time and the frequency of the waveform. Feature reduction utilizing step discriminant analysis selected those wavelet coefficients which best distinguish the waveforms of those responders from the nonresponders. A total of four features chosen by such analysis coupled with the Propofol effect-site concentration were used to train a four-layer artificial neural network for classifying between the responders and the nonresponders. The Propofol is delivered by a mechanical syringe infusion pump controlled by Stanpump which also estimates the Propofol effect-site and plasma concentrations using a three-compartment pharmacokinetic model with the Tackley parameter set. In the animal experiments on dogs, the system achieved a 89.2% accuracy rate for classifying anesthesia depth. This result was further improved when running in real-time with a confidence level estimator which evaluates the reliability of each neural network output. The anesthesia level is adjusted by scheduled incrementation and a fuzzy-logic based controller which assesses the mean arterial pressure and/or the heart rate for decrementation as necessary. Various safety mechanisms are implemented to safeguard the patient from erratic controller actions caused by external disturbances. This system completed with a friendly interface has shown satisfactory performance in estimating and controlling the depth of anesthesia     

基于同步压缩小波变换的主信号抑制技术

在辐射源个体识别(SEI)技术中,能量较高的主信号往往导致微弱个体特征稳定性降低,进而影响最终的个体识别效果。为了解决该问题并提升辐射源个体识别性能,该文提出基于同步压缩小波变换的主信号抑制技术。首先,利用静态小波变换完成对带噪信号的去噪预处理;然后,利用同步压缩小波变换完成对主信号的检测和抑制,并以均方根误差和皮尔逊相关系数为数值指标,验证算法的有效性;最后,在主信号抑制的基础上,利用分形理论中盒维数完成对信号的特征提取,并利用单核支持向量机验证个体识别性能。实验结果表明,与主信号抑制之前相比,主信号抑制算法下个体识别率提升了10%左右,验证了同步压缩小波变换的主信号抑制算法对辐射源个体识别率提升的有效性。     

铁路信号的激光器驱动电源集中监控

为了提高铁路激光器驱动电源监控信号自动识别能力,实现铁路信号的激光器驱动电源集中监控,提出一种基于铁路信号激光器驱动电源集中监控方法。采用传感器进行铁路信号电源集中监控信息采集,对采集的信号模型采用空间阵列分布式设计方法进行空间信源模型构建,对采集的铁路信号采用相干检测法进行滤波检测,提取铁路信号功率谱密度特征,采用小波分析方法进行铁路激光器驱动电源监控信号的多元尺度分解,实现铁路激光器驱动电源信号的自动检测和集中监控。仿真结果表明,采用该方法进行的铁路信号激光器驱动电源集中监控能力较强,误码率低于10-1,下降幅度较大且迅速,信号传输延迟缩短了30%,提高了信号的自动检测和识别能力。     

基于相似度的雷达目标识别灰关联分析算法

针对参数重叠的雷达目标识别算法准确率较低、对复杂体制雷达目标识别效果较差的问题,提出了基于相似度的灰关联分析算法,实现对复杂体制雷达目标的融合识别.该算法首先提取各参数间的相似度矩阵,然后运用灰关联分析得出相应的灰关联系数,同时根据各参数匹配程度确定其权重值,与灰关联系数组合获得待识别信号与库信号的灰关联度,根据灰关联度完成目标识别.仿真实验结果表明,该算法能很好地降低参数重叠给识别带来的影响,提高识别正确率,快速实现复杂体制雷达目标的识别.     

基于谱相关的CBOC信号参数盲估计

针对低信噪比下组合二进制偏移载波(CBOC)调制信号的参数盲估计问题,提出了利用谱相关对CBOC信号进行参数估计方法。首先给出了CBOC信号模型,然后根据CBOC信号的数据通道和导频通道之间有良好的正交性特点,详细推导出其谱相关函数可以化简为两个BOC信号谱相关函数的叠加,最后根据CBOC信号循环频率截面的特点进行峰值检索后,实现对伪码速率、载频速率和副载波速率的盲估计。推导结果和计算机仿真分析表明,该方法可以实现在低信噪比下对伪码速率、载频速率和副载波速率的有效估计。     

Automatic detection of intraoperative neurological injury

Neurological injuries occurring during high-risk surgical procedures can be detected by monitoring intraoperative evoked potential signals. In this communication, an automatic injury detection algorithm is proposed in which the EP signal is modeled as a pole-zero filter and then the model parameters are applied as inputs to a classifier type neural network. A recognition rate of 96% is achieved using an experimental model of brain injury.