癫痫病人脑电信号的奇异谱

癫痫是一种常见的神经系统疾患,其唯一客观证据为脑电图的癫痫样发放。在癫痫发作间期,仅有偶发的很难辨别的癫痫样放电,为了正确诊断癫痫病,往往需要医生长时间监测病人的脑电信号,在对脑电信号进行相空间重构,进而对其进行奇异系统分析,发现癫痫病人无论在癫痫发作前、发作中、发作后,其脑电信号的奇异谱曲线不存在噪声平台,明显区别于正常人。是否可以认为脑电信号的奇异谱正代表着大脑的一种基本状态,癫痫患者在未发作时,大脑的基本状态已经处于异常。无论如休,奇异系统分析方法使得可以利用很短的一段脑电数据诊断癫痫。无疑为癫痫病人的临床诊断提供了一条简单、有效的途径。     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

不同状态下脑电信号的双谱分析

根据脑电的非高斯随机特性,应用双谱技术分析脑电信号,引出脑电的参数化双谱估计,旨在克服脑电功率谱分析的缺陷。对四种不同脑功能状态（清醒闭眼、安神睁眼、快速心算、急促呼吸）的脑电进行双谱分析,并对对称脑电信号的互双谱作了初步的讨论。实验结果显示：所有脑电均出现明显的双谱结构,但不同生理状态下的脑电双谱结构存在明显的差异,结果表明双谱可能为研究脑电提供新的辅助信息。     

基于二阶C0复杂度的癫痫发作预测

癫痫发作的预测是近年来在临床医学和神经系统科学研究领域中备受关注的问题。如果癫痫发作能够被可靠地预测,则可以提前采取有效的临床预防措施,从而能较大程度地改善癫痫患者的生活质量。文章提出了一种基于二阶C0复杂度的预测算法用于预测癫痫发作。该算法通过分析癫痫患者颅内脑电信号的二阶C0复杂度,利用发作前期复杂度曲线的变化特征预测癫痫发作。作者运用该算法对21组癫痫病人87次发作的临床颅内脑电数据和4组大鼠4次发作的颅内脑电数据进行分析计算,预测准确率分别为94.3%和100%。实验结果表明该算法可以有效地预测癫痫发作,具有潜在的重要临床应用价值。     

一种改进的近似熵——样品熵及其在颞叶癫痫患者脑电信号分析中的应用

采用了近似熵(approximately entropy,ApEn)和它的改进算法,即样品熵(sample entropy,SampEn)分析了8位颞叶癫痫患者和10位健康人员的短程脑电信号。在计算过程中使用了两种滑动窗口和5个不同的过滤标准r。结果显示颞叶癫痫患者组脑电信号的熵值显著低于健康组,而且患者癫痫病灶所在的脑半球的复杂度远远小于非癫痫病灶的脑半球。小的滑动窗口能更多地反映与癫痫发作相关的细节。对于1秒的滑动窗口,过滤标准r不能小于时间序列标准差的0．15％;而对于4秒的滑动窗口,则过滤标准r不能小于时间序列标准差的10％。研究结果表明,在短程脑电信号的非线性分析中,样品熵是一种比近似熵更为可靠的非线性分析方法。颞叶癫痫患者脑电信号的熵值低于健康人员,这可能表明脑电活动的非线性程度的降低是由于神经信号在大脑内的传递受到了阻碍或者损坏,使得神经信号成了相对孤立的信息源。     

脑电信号的计算机频谱分析

由于脑电(EEG)信号的复杂性以及观察者在分析脑电变化时的主观性,使得有时脑电中有重要生理意义的信息被忽略掉,或者受到误解。因此,探讨脑电的精确而客观的分析法,如用计算机进行辅助分析,就有着重要的理论和实际意义。在计算机分析脑电信号的过程中,一般先要做脑电信号的频率域分析。其最常用的方法是将原始脑电信号先经模/数转换,而后对此离散时间序列信号作快速富里叶变换(FFT),计算其功率谱。但这种常规的计算方法有一定的     

快速独立分量分析及其在脑电信号处理中的应用

独立分量分析(IndependentComponentAnalysis,ICA)是一种基于信号统计特性的盲源分离方法,由于其分离的信号之间是互相独立的,所以在生物电信号去除干扰和伪迹、信号分离以及特征提取等方面有很大的潜在价值。本文提出了一种改进的快速ICA方法,提高了收敛速度。通过仿真,证明这种方法的优越性。最后利用该方法去除脑电中眼动伪迹,达到了较好的效果。     

基于大脑皮层信息传输的脑电信息图示方法

提出一种基于大脑皮层信息传输的脑电地形图示方法—脑电信息图（Ｂｒａｉｎ ＩｎｆｏｒｍａｔｉｏｎＭａｐｐｉｎｇ － ＢＩＭ） 。其原理是从不同导联电极上采集脑电信号经相空间重建构成头皮电位信息传输矩阵, 将各导联信息传输时间序列的信息传输量和复杂度数据绘制成头皮拓扑分布图, 以直观地反映脑电信息传输分布模式在不同时相中的变化进程。该方法不仅是从新的角度观察大脑功能变化, 而且可克服传统的脑电频谱分段地形图不能表达长程脑电模式变化的不足。对局限性癫痫病患者的试用表明,脑电信息图能较好地反映癫痫发作前后的信息传输动向和复杂度（Ｋｃ 、Ｃ１ 、Ｃ２） 的变化趋势。结果提示,脑电信息图（ＢＩＭ） 有可能成为一种新的观察大脑功能活动的图示诊断方法,值得进一步深入研究。     

脑电超慢涨落图技术在癫痫研究中的应用

目的：观察脑内多种神经递质对癫痫发作的影响。方法：以癫痫患者和ＳＤ大鼠为实验对象,用脑功能检测的最新脑电超电涨落图分析仪（ｅｎｃｅｐｈａｌｏｆｌｕｃｔｕｏｇｒａｍ ｔｅｃｈｎｏｌｏｇｙ,ＥＴ）长时程采集脑电信号,提取在脑电中载有脑神经递质调节系统的震荡信息（即Ｓ谱线）,分析癫痫发作时的脑神经递质的变化。结果：患儿癫痫发作时,Ｓ谱线中Ｓ２（谷氨酸）增高;Ｓ１（γ－氨基丁酸）降低,造成Ｓ１＜Ｓ２。Ｓ５     

一种独立分量分析的迭代算法和实验结果

介绍盲信源分离中一种独立分量分析方法,基于信息论原理,给出了一个衡量输出分量统计独立的目标函数。最优化该目标函数,得出一种用于独立分量分析的迭代算法。相对于其他大多数独立分量分析方法来说,该算法的优点在于迭代过程中不需要计算信号的高阶统计量,收敛速度快。通过脑电信号和其他信号的计算机仿真和实验结果表明了算法的有效性。     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

Generalized dimension of the intersection between EEGs

 The generalized dimension defined by [Mandelbrot (1995) J Fourier Anal Appl special J.P. Kahane issue: 409–432] was applied to studying the interrelationship between various parts of human cerebral cortex in different functional conditions. Taking EEG signals from different brain areas as different sets, the generalized dimensions of their intersections were calculated to describe the interrelationship between them. The results showed that the generalized dimensions of intersections in different brain states decreased according to the following order: rest with eyes open, closed, light sleep, and deep sleep. The generalized dimensions of intersections related to the left or right temporal lobe were higher than the others when the subjects was doing mental arithmetic, and there was a decrease when the subjects listened to soft classical music. In addition, it was found that there was a noticeable difference in singular spectra between epileptic patients and normal subjects, irrespective of whether the epileptic patient was experiencing a seizure or not. Received: 3 July 2000 / Accepted in revised form: 30 October 2000     

Generalized optimal spatial filtering using a kernel approach with application to EEG classification

Common spatial patterns (CSP) has been widely used for finding the linear spatial filters which are able to extract the discriminative brain activities between two different mental tasks. However, the CSP is difficult to capture the nonlinearly clustered structure from the non-stationary EEG signals. To relax the presumption of strictly linear patterns in the CSP, in this paper, a generalized CSP (GCSP) based on generalized singular value decomposition (GSVD) and kernel method is proposed. Our method is able to find the nonlinear spatial filters which are formulated in the feature space defined by a nonlinear mapping through kernel functions. Furthermore, in order to overcome the overfitting problem, the regularized GCSP is developed by adding the regularized parameters. The experimental results demonstrate that our method is an effective nonlinear spatial filtering method.     

癫痫发作间期alpha波的窄带相位同步分析

神经元同步化放电是癫痫发作的一个重要特征,作者提出并运用窄带相位同步技术对比分析了54个癫痫病人和10个正常成年人的脑电信号(EEG)数据.结果表明,相对于对照组,癫痫组的Alpha波的平均窄带相位同步值有显著下降(P=0.02058).为了更准确地刻画和衡量癫痫组和其对照组在同步模式上的差异,提出了一个新的具体的量化指标,即alpha波的窄带相位同步发散率.分析结果显示,对照组的窄带相位同步发散率明显低于癫痫组(P=0.003060),说明对照组的alpha波振子间互诱导强度更高.这可能反映了癫痫组窄带相位同步发散率的升高及所代表的alpha振子间互诱导强度的减弱与患者在癫痫发作间期的状态有密切的联系.     

Extraction of reproducible seizure patterns based on EEG scalp correlations

The objective of this work is to identify similarities in the spatio-temporal dynamics of epileptic seizures, record with scalp EEG. A comprehensive method is proposed and applied in EEG of the patients who suffer from temporal lobe epilepsy. The method is based on the computation of the time-varying degree of non linear correlation between scalp electrodes at seizure onset and during seizure spread, determined by a nonlinear regression analysis. The quantification and coding of these similarity relations allow the comparison between two epileptic networks. Results show that reproducible patterns may be extracted from different seizures of the same patient and confirm the existence of different subtypes of temporal lobe epilepsy.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

Measuring the directionality of coupling: phase versus state space dynamics and application to EEG time series

Measuring the directionality of coupling between dynamical systems is one of the challenging problems in nonlinear time series analysis. We investigate the relative merit of two approaches to assess directionality, one based on phase dynamics modeling and one based on state space topography. We analyze unidirectionally coupled model systems to investigate the ability of the two approaches to detect driver-responder relationships and discuss certain problems and pitfalls. In addition we apply both approaches to the intracranial electroencephalogram (EEG) recorded from one epilepsy patient during the seizure-free interval to demonstrate the general suitability of directionality measures to reflect the pathological interaction of the epileptic focus with other brain areas.     

Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Electroencephalogram (EEG) analysis remains problematic due to limited understanding of the signal origin, which leads to the difficulty of designing evaluation methods. In spite of these shortcomings, the EEG is a valuable tool in the evaluation of some neurological disorders as well as in the evaluation of overall cerebral activity. In most studies, which use quantitative EEG analysis, the properties of measured EEG are computed by applying power spectral density (PSD) estimation for selected representative EEG samples. The sample for which the PSD is calculated is assumed to be stationary. This work deals with a comparative study of the PSD obtained from normal, epileptic and alcoholic EEG signals. The power density spectra were calculated using fast Fourier transform (FFT) by Welch's method, auto regressive (AR) method by Yule–Walker and Burg's method. The results are tabulated for these different classes of EEG signals.