基于独立分量分析算法研究儿童癫痫脑电的混沌动力学特征

首先采用独立分量分析(Independent component analysis,ICA)算法,将儿童癫痫信号从复杂的背景脑电(Electroencephalogram,EEG)中分离出来;然后采用了一维时间序列相空间重构技术和混沌的定量判据,对分离出来的独立分量信号进行了分析与计算.通过对生理和癫痫状态下独立分量信号的相图、功率谱、关联维数和Lyapunov指数的对比研究,得出如下结论:(1)EEG独立分量的相图、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指标衡量大脑的健康状态;(2)在正常的生理状态下EEG是混沌的,而在癫痫状态下则趋于有序。     

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.     

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.     

混沌的数值分析方法及其在生物医学工程中的应用

本对混沌的数值分析方法中的定量测量参数（功率谱、Ｌｙａｐｕｎｏｖ指数、分维数和测度熵等）的物理意义、定义及目前的研究状况进行了比较详细的介绍，并简介了Ｌｙａｐｕｎｏｖ指数、分维数和测度熵三的关系。最后介绍了混沌的数值分析在生物医学工程中的应用。     

混沌的数值分析方法及其在生物医学工程中的应用

本文对混沌的数值分析方法中的定量测量参数（功率谱、Ｌｙａｐｕｎｏｖ指数、分维数和测度熵等）的物理意义、定义及目前的研究状况进行了比较详细的介绍，并简介了Ｌｙａｐｕｎｏｖ指数、分维数和测度熵三者的关系。最后介绍了混沌的数值分析方法在生物医学工程中的应用。     

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。     

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。     

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。     

EEG信号的径向基函数神经网络预测

基于混沌动力学系统相空间的延迟坐标重构及人工神经网络的非线性特性。研究了采用基于自适应投影学习算法的径向基函数网络对实测的EEG信号进行预测。通过对径向基函数引入一宽度调节系数a，使网络的预测性能有较大提高。理论分析和研究结果表明：a的取值由EEG信号的关联维数D2决定，a在最佳区间内取值能够更有效地对EEG信号进行预测。     

Amplitude suppression and chaos control in epileptic EEG signals

In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected during epileptic seizure. Then we have proposed a measure of chaoticity for a chaotic signal, which is somewhat similar to measuring sensitive dependence on initial conditions by measuring Lyapunov exponent in a chaotic dynamical system. We have shown that with respect to this measure the amplitude suppression algorithm reduces chaoticity in a chaotic signal (EEG signal is chaotic). We have compared our measure with the estimated largest Lyapunov exponent measure by the largelyap function, which is similar to Wolf's algorithm. They fit closely for all but one of the cases. How the algorithm can help to improve patient specific dosage titration during vagus nerve stimulation therapy has been outlined.     

Assessment of Chaotic Parameters in Nonstationary Electrocardiograms by Use of Empirical Mode Decomposition

This study addressed the issue of assessing chaotic parameters from nonstationary electrocardiogram (ECG) signals. The empirical mode decomposition (EMD) was proposed as a method to extract intrinsic mode functions (IMFs) from ECG signals. Chaos analysis methods were then applied to the stationary IMFs without violating the underlying assumption of stationarity. Eight ECG data sets representing normal and various abnormal rhythms were obtained from the American Heart Associate Ventricular Arrhythmia database. The chaotic parameters including Lyapunov exponent, entropy, and correlation dimension were computed. The results consistently showed that the 10th IMF (IMF-10) was stationary and preserved sufficient nonlinearity of the ECG signals. Each IMF-10 from the data sets (n = 8) gave a positive dominate Lyapunov exponent (0.29-0.64, p < 0.0001), a positive entropy (0.039-0.061, p < 0.0001), and a noninteger correlation dimension (1.1-1.9). These were evidences of a chaotic dynamic system. We therefore concluded that the original ECG signals must also have chaotic properties. The chaotic parameters did not show significant differences among the eight data sets representing normal sinus rhythm and various abnormalities. This study has demonstrated an effective way to characterize nonlinearities in nonstationary ECG signals by combining the empirical mode decomposition and the chaos analysis methods.     

New approach to studies on ECG dynamics: Extraction and analyses of QRS complex irregularity time series

How to extract information intensively from ECGs for the diagnosis of cardiovascular diseases and assessment of heart function is a topical subject. Using a method based on the wavelet transform to calculate the irregularity of the QRS complex, which may relate to inotropy, the QRS complex irregularity time series is successfully extracted from original ECG signals. This provides a new approach to studies of ECG dynamics. With the help of non-linear dynamics theory, the QRS complex irregularity time series of eight subjects, from the MIT/BIH arrhythmia database are studied qualitatively and quantitatively, and the characteristics of ECG dynamics are analysed extensively. The power spectrum, phase portrait, correlation dimension, largest Lyapunov exponent, time-dependent divergence exponent and complexity measure all verify the fact that ECG dynamics are dominated by an underiying 5–6-dimensional non-linear chaotic system, whose complexity measure is about 0.7. The QRS complex irregularity time series contains abundant information about all parts of the heart and the regulation of the autonomic nervous system, and so further analyses are of great potential theoretical and clinical significance to patho-physiology studies and ambulatory monitoring.     

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.     

On the discrimination of patho-physiological states in epilepsy by means of dynamical measures

In the present paper a number of techniques were applied to determine the effects of epileptic seizure on spontaneous ongoing EEG. The idea is that seizure represents transitions of an epileptic brain from its normal (chaotic) state to an abnormal (more ordered) state. Some nonlinear measures including correlation dimension, maximum Lyapunov exponent and wavelet entropy and a graphical tool, named recurrence plot, as well as a novel technique that collects some statistics of the state space organization were used to characterize interictal, preictal and ictal states and derivate a phase transition. The novelty of this work includes of introducing new types of indicators base upon some nonlinear features besides of proposing a new feature of point distribution in phase space. Our results show that (1) these three states are separable in 3-D feature space of nonlinear measures with a gradual decrease of their quantity in seizure evolution, (2) strong rhythmicity, which manifests in recurrence plots and recurrence quantification analysis measures, appears in dynamic while having entered into seizure and (3) different volumes of state space are occupied during each phase of epileptic disorder.The significance of the work is that this information is a step into the detection of a preictal state and consequently is helpful in the prediction and control of epileptic seizures.     

Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration

The mammalian ventilatory behaviour exhibits nonlinear dynamics as reflected by certain nonlinearity or complexity indicators (e.g. correlation dimension, approximate entropy, Lyapunov exponents, etc.) but this is not sufficient to determine its possible chaotic nature. To address this, we applied the noise titration technique, previously shown to discern and quantify chaos in short and noisy time series, to ventilatory flow recordings obtained in quietly breathing normal humans. Nine subjects (8 men and 1 woman, 24-42 years) were studied during 15-min epochs of ventilatory steady-state (10.1+/-3.0 breaths/min, tidal volume 0.63+/-0.2 L). Noise titration applied to the unfiltered signals subsampled at 5 Hz detected nonlinearity in all cases (noise limit 20.2+/-12.5%). Noise limit values were weakly correlated to the correlation dimension and the largest Lyapunov exponent of the signals. This study shows that the noise titration approach evidences a chaotic dimension to the behavior of ventilatory flow over time in normal humans during tidal breathing.     

脑电信号的几个非线性动力学分析方法

作者讨论了目前常用的几个非线性动力学分析方法，包括Ｌｏｒｅｎｚ散点图，分维数、复杂度、ｙａｐｕｎｏｖ指数等，并将这些方法用于不同状态的脑电信号（睡眠、癫痫及正常状态）的分析。结果表明，利用这些方法确能将脑电信号的一些状态分开。