HAI实验中EEG信号的非线性动力学研究

采用一维时间序列相空间重构技术和混沌的定量判据，对缺氧窒息而引起的中枢神经损伤（Hypoxic-Asphyxic Injury，HAl）实验中仔猪的脑电（Electroencephalogram，EEG）信号进行了分析与计算。通过对生理和损伤状态下仔猪EEG信号的相图、功率谱、关联维数和Lyapunov指数的对比研究，得出如下结论：（1）EEG的相图、功率谱、关联维数和Lyapunov指数反映了大脑的总体动态特征，它们可作为一种定量研究EEG的新方法进行脑损伤的早期诊断；（2）在正常的生理状态下EEG是混沌的，而在损伤状态下则趋于有序。     

EEG非线性特征参数的研究

脑电图(EEG)记录了神经元群的电活动,为脑信息处理特征的研究提供重要的信息.基于相空间重构思想的时间序列分维算法(G-P算法)提取EEG信号的特征参数,讨论了G-P算法的三个重要参数,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关联维数可以有效地区分大脑不同状态的特征,关联维数可以作为脑信息处理的非线性特征参数.     

癫痫脑电及节律波的非线性动力学特征研究

脑电图(EEG)是研究脑科学的重要工具,对EEG信号中隐藏的特征和信息进行深入研究,能更好地满足现在临床研究的需要。本文通过小波变换和非线性动力学两种分析方法,提取癫痫发作间期和发作期EEG信号及其节律波(δ波、θ波、α波和β波)的非线性特征,计算分析关联维数(CD)、Lyapunov指数、近似熵(ApEn)特征值在癫痫发作过程是否存在显著变化。研究结果表明,EEG信号及其节律波的非线性动力学特征在检测癫痫发作过程时可作为有效的鉴别统计量。     

基于非线性混沌的心律失常心音信号分析

本文基于非线性混沌理论,对正常及心律失常心音信号的关联维数及最大Lyapunov指数进行了计算和混沌特性分析,从而提出一种新的心律失常分析方法。对30例健康人和30例心律失常患者的分析结果显示,正常心音和心律失常心音的关联维数和最大Lyapunov指数具有显著性差异。由于心律失常心音信号时序上的不规则性,导致其可预测性下降,与正常心音信号相比,具有较高的复杂度,从而具有比正常心音更大的关联维数和最大Lyapunov指数值。故关联维数和最大Lyapunov指数可用于分析心律失常心音信号及其特征提取。     

心电动态生理及病理信息的非线性动力学研究

按照非线性理论,作者设计并实施了用于心脏系统非线性研究的深低温停循环(Profound Hypothermia and Circulatory Arrest,简称PHCA)实验.通过对PHCA实验中几个温度台阶上所采集的心电信号的功率谱分析和Lyapunov指数的计算,得出以下结论:(1)心电信号的功率谱和Lyapunov指数反映了心脏的总体动态特征,它们可用以估计心脏的健康状态;(2)在正常的生理状态下心脏的运动是混沌的,而在病理状态下则趋于有序.     

脑电高阶Lyapunov指数的估计及其仿真计算

本研究从一维时间序列中估算了相应混沌系统的前3阶最大Lyapunov指数λ1、λ2和λ3,以两个高维混沌模型:4×3 Lorenz方程高维混沌系统和HyperR ssler超混沌系统作为仿真系统,对算法的可行性和有效性进行了验证。然后,应用λ1、λ2和λ3对脑电的高维混沌特性进行了研究:从脑电中分别重构了对应的时间延迟吸引子,计算了癫痫脑电组和正常对照组脑电的1λ、λ2和3λ,应用Y-K公式给出的混沌系统分数维和Lyapunov指数谱jλ的关系,估计了癫痫脑电和正常对照脑电对应系统的分数维的范围,对癫痫脑电的高维混沌状况进行了分析。结果表明:癫痫脑电对应混沌吸引子的维数低于正常脑电(高维混沌)的维数;可以分属于高维混沌和低维混沌两类情况,因此研究这两类不同的情况,应采用高维和低维两种不同的混沌理论和方法。     

警戒作业中脑电功率谱的重心频率及监控应用

从频域探索脑电(EEG)信号的生理信息是研究大脑神经细胞电活动的重要手段,其中重心频率是频域信息的一个全局性参数。本研究运用性能甚佳的多窗谱方法(MTM)计算了警戒状态与警戒下降状态情形下受试者的EEG功率谱及其重心频率。计算结果表明,警戒状态下功率谱的重心频率高于警戒下降状态的重心频率,重心频率随着警戒下降而变小,其所对应的谱线位置发生左移。最后,通过设计算法获取了重心频率的监控曲线,并应用于警戒作业人员的在线监控。     

一种脑电信号相空间分析的新方法

提出一种新的脑电(EEG)信号相空间分析方法。通过计算相空间状态点间的欧氏距离,定义相对嵌入维数的态密度和态方差,并与反映非线性动力学系统混沌特征的关联维数作比较。对各种实测的脑电时间序列的计算结果表明,态密度和态方差不仅计算简单,计算结果一致可靠,而且和关联维数相比,更能有效地反映非线性动力学系统的某些特征。此外,计算了基于距离协方差的脑电信号的奇异谱,并对实验结果作了分析。     

阿尔茨海默病患者自发脑电信号子波熵的研究

子波熵是衡量信号复杂程度的指标,本文采用连续子变换的方法对轻、中、重度阿尔茨海默病(AD)患者及正常对照老年人的脑电(EEG)信号进行子波分析,根据子波系数计算EEG信号的子波功率谱分布,提取描述EEG信号复杂程度的定量指标——子波熵。对轻、中、重度AD患者和正常对照的自发状态下EEG信号的子波熵值进行比较,并将子波熵值与MMSE进行相关性分析。结果显示,轻、中、重度AD组和正常对照组之间EEG信号的子波熵存在显著差异(P0.01)。组间比较显示轻、中、重度AD患者EEG信号的子波熵均低于正常对照,差异具有统计学意义(P0.05)。这与AD患者EEG信号的功率谱分布单一有关。进一步研究表明EEG信号的子波熵与其MMSE评分均存在显著相关(r=0.601~0.799,P0.01)。子波熵可以作为描述EEG信号复杂程度的定量指标,子波熵值有可能成为AD诊断和病情评估的电生理指标。     

一种基于脑电信号的疲劳驾驶状态判断方法

通过研究疲劳驾驶时脑电信号的特征,提出了一种基于独立分量分析（independent component analysis,ICA）的脑波疲劳状态判断方法.利用模拟驾驶系统,采用NT-9200动态脑电仪采集驾驶员在清醒和疲劳状态下（连续驾驶4h以上）的脑电信号,对采集的多导信号进行独立分量分析,去除EEG信号中的眼电、肌电及工频等干扰,经过快速傅里叶变换（fast fourier transform,FFT）后计算出脑波中多种功率谱密度,求得疲劳指数F.实验结果表明,在疲劳状态下的疲劳指数F明显高于清醒状态下的F.本文提出的脑波疲劳状态判断方法可有效用以判断驾驶员的疲劳程度.     

Statistics over features: EEG signals analysis

This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.     

Chaotic behavior of the coronary circulation

The regulation of the coronary circulation is a complex paradigm in which many inputs that influence vasomotor tone have to be integrated to provide the coronary vasomotor adjustments to cardiac metabolism and to perfusion pressure. We hypothesized that the integration of many disparate signals that influence membrane potential of smooth muscle cells, calcium sensitivity of contractile filaments, receptor trafficking result in complex non-linear characteristics of coronary vasomotion. To test this hypothesis, we measured an index of vasomotion, flowmotion, the periodic fluctuations of flow that reflect dynamic changes in resistances in the microcirculation. Flowmotion was continuously measured in periods ranging from 15 to 40 min under baseline conditions, during antagonism of NO synthesis, and during combined purinergic and NOS antagonism in the beating heart of anesthetized open-chest dogs. Flowmotion was measured in arterioles ranging from 80 to 135 μm in diameter. The signals from the flowmotion measurements were used to derive quantitative indices of non-linear behavior: power spectra, chaotic attractors, correlation dimensions, and the sum of the Lyapunov exponents (Kolmogorov–Sinai entropy), which reflects the total chaos and unpredictability of flowmotion. Under basal conditions, the coronary circulation demonstrated chaotic non-linear behavior with a power spectra showing three principal frequencies in flowmotion. Blockade of nitric oxide synthase or antagonism of purinergic receptors did not affect the correlation dimensions, but significantly increased the Kolmogorov–Sinai entropy, altered the power spectra of flowmotion, and changed the nature of the chaotic attractor. These changes are consistent with the view that certain endogenous controls, nitric oxide and various purines (AMP, ADP, ATP, adenosine) make the coronary circulation more predictable, and that blockade of these controls makes the control of flow less predictable and more chaotic. Supported by NIH grant HL32788.     

Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound

Electroencephalography is an essential clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important element in the diagnosis of epilepsy. In this study, EEG signals recorded from 30 subjects were processed using autoregressive (AR) method and EEG power spectra were obtained. The parameters of autoregressive method were estimated by different methods such as Yule-Walker, covariance, modified covariance, Burg, least squares, and maximum likelihood estimation (MLE). EEG spectra were then used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complexes in patients with absence seizures. The variations in the shape of the EEG power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and determination of epileptic seizure. The Cramer-Rao bounds (CRB) were derived for the estimated AR parameters of the EEG signals and the performance evaluation of the estimation methods was performed using the CRB values. Finally, the optimal AR spectral estimation method for the EEG signals was selected according to the computed CRB values. According to the computed CRB values, the performance characteristics of the MLE AR method was found extremely valuable in EEG signal analysis.     

Study on Singularity of Chaotic Signal Based on Wavelet Transform

INTRODUCTION The significant information of a signal is often carried by singular characteristics or irregular struc-tures of the signal, for example, the most important information of ECG(electrocardiogram) or EEG isoften presented at the transient points of a signal, such as those points near peaks. The singular charac-teristics of these transient points are more obvious than the smooth parts of signals. Therefore,to studythe singularity of a signal is a meaningful work. Those analysi…     

动态分析急性心肌缺血模型中的非线性

通过分析心脏电信号 ,许多学者已经发现正常的心脏和病态的心脏都有显著而突出的动力学特征。在此基础上 ,建立了 6只兔子急性心肌缺血实验模型 ,计算其不同时间段的 12导同步 ECG,作出关联维数 -时间 (D2- T)曲线和最大 L yapunov- T曲线 ,结合电生理和解剖学知识 ,研究了实验结果 ,证实了心肌缺血的存在会导致ECG的最大 L yapunov指数和关联维数的下降。 ECG的最大 L yapunov指数和关联维数分别反映了心脏系统的混沌性和复杂性 ,两者间没有明确的对应关系。关联维数更多受到了局部心脏系统状态的影响。     

青少年肌阵挛癫痫发作间期EEG-fMRI的研究

采用同步脑电与功能磁共振(Simultaneous electroencephalography-correlated functional magnetic resonance imaging,EEG-fMRI)技术,研究青少年肌阵挛癫痫患者发作间期痫样放电时脑部血氧水平依赖(Blood oxygen level-dependent,BOLD)信号变化。结果发现:双侧大脑半球的激活及失活信号变化普遍对称且各自独立存在,信号由枕顶至额区逐渐减少。阳性激活区有:楔叶、岛叶、额中部内侧、小脑中线两侧及丘脑。阴性激活区有:双侧额前部、顶部及扣带后回。由此推断:以棘慢复合波为表现形式的同步的神经元活动可能反映了丘脑皮层BOLD信号的激活,而失活区域反映了异常放电时的脑功能的静息状态;这类激活在神经元的活动(EEG)与fMRI结果之间有很好的对应关系;EEG-fMRI是研究脑功能状态有效的方式。     

Effect of increased task procedures on correlation dimensions of the electroencephalogram

Changes in correlation dimensions of the electroencephalogram (EEG) were examined in three different tasks. These three tasks differed from each other with respect to the number of procedures. In the present experiment, left-hand movement and mental arithmetic were controlled, respectively, during an auditory linguistic task. Subjects were 13 healthy right-handed males. EEG signals from eight electrode sites were analyzed and the correlation dimensions were obtained. In addition, the relative power was obtained for the alpha band. An increase in the number of procedures yielded high dimensionality on the occipital EEG. In contrast, left-hand movement had no significant effect on EEG dimensions over the motor area. The relative power of the alpha band was seen to decrease in all channels as the number of procedures increased. The fact that changes in EEG dimensions did not necessarily exhibit a simple correspondence to changes in alpha wave activity was also discussed.     

冠心患者与健康人同步12导联ECG信号的李雅普诺夫指数谱比较研究

本研究首次计算了60名具有窦性心率的冠心患者(Coronary atrery disease:CAD)和60名健康老年人的同步12导联心电图信号的李雅普诺夫指数谱.发现对同一个人,从不同导联得出的Lyapunov指数是不同的,具有明显的空间分布特性.所有导联的ECG信号的最大Lyapunov指数L1均为正数,其余指数为负,心电信号表现出明显的混沌特征.同一导联相比较,冠心患者的最大Lyapunov指数L1低于健康正常人的最大Lyapunov指数L1,提示在心肌缺血的情况下,心电信号的混沌程度下降了,重构相空间中ECG信号的奇异吸引子的动力学复杂性降低了.结果表明,在估算Lyapunov指数时,有必要指明导联的位置.在Lyapunov指数谱中,最大Lyapunov指数可以将冠心患者与健康正常人区分开来,在心脏疾病诊断中具有潜在的应用价值.     

Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients

The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) with composite features (wavelet coefficients and Lyapunov exponents) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the classification of the ECG signals. Decision making was performed in two stages: computing composite features which were then input into the classifiers and classification using the classifiers trained with the Levenberg-Marquardt algorithm. The research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the ECG signals and the RNN trained on these features achieved high classification accuracies.