大鼠癫痫模型脑电信息的非线性分析

为了探索应用非线性动力学理论进行癫痫疾病预报的新方法，采用非线性动力学指标：近似熵和相关维对大鼠癫痫发作过程的整个脑电信号进行分析研究。结果显示癫痫发作时，脑电信号的近似熵和相关维明显低于发作前和发作后，这表明了癫痫发作过程脑电信号复杂度的变化规律；而癫痫发作前又是一特殊的阶段，其脑电信号的复杂度已开始降低，但发作症状尚未出现。因此运用非线性动力学方法对癫痫发作至少在短期内可预报。     

大鼠癫痫模型脑电信息的不稳定周期轨道分析

为了进一步探索应用非线性动力学理论对癫痫脑电信息进行分析。在采用非线性动力学指标:近似熵和相关维对大鼠癫痫发作过程的整个脑电信号进行分析研究的基础上,运用新的用于神经元系统的复杂性行为研究的非线性动力学方法——不稳定周期轨道,分析研究癫痫不同发作时期脑电信号的变化规律。结果显示癫痫发作时,脑电信号中存在具有统计显著性的周期1和周期2轨道,而在癫痫发作前期仅存在具有统计显著性的周期1轨道。从而进一步验证了癫痫发作整个过程,脑电信号复杂度的变化规律。     

儿童失神癫痫发作期脑电信号子波熵分析

本研究采用脑电信号的整体子波熵和分尺度子波熵研究脑电信号的信息复杂性,探索儿童失神癫痫(CAE)发作的动力学机制。研究采集儿童失神癫痫患者及正常对照的脑电信号;采用连续子波变换提取脑电信号的时频特征;采用子波功率谱分析提取分尺度功率谱特征;根据分尺度功率谱计算整体子波熵和分尺度子波熵,分析整体子波熵和分尺度子波熵随CAE发作的时间演变过程,并与正常对照进行比较。结果显示:CAE患者发作期脑电信号的整体子波熵显著低于正常对照组,也低于发作间期。CAE发作时第12尺度(对应中心频率3 Hz)的分尺度子波熵显著高于正常对照,α频带(中心频率10 Hz)脑电节律的子波熵明显低于正常对照。脑电信号整体子波熵可以反映脑电信号的复杂程度,CAE发作时脑电信号的信息复杂度明显降低。子波熵降低有可能成为癫痫发作的特征神经电生理参数,为癫痫发作的神经调控技术的研究提供依据。     

脑电信号分析的实用符号动力学方法研究

符号动力学分析是脑电分析的一个新的研究方向,符号熵可以较好地反映非线性信号的复杂程度,具有简单、稳定的特点。本文提出了一种新的符号化方法——差分符号化,即在观测数据的切空间中进行符号动力学分析,并进一步比较了不同符号化参数对符号熵的影响。通过对不同生理状态下的脑电信号数据的对比分析表明,应用此方法可以显著地区分出正常与癫痫及睁眼与闭眼等病理及生理脑电信号的复杂度变化情况,对建立客观的脑电信号评价标准、准确进行定量分析具有重要的意义。     

子波变换在癫痫脑电信号检测和分析中的应用

介绍了一种新近发展的非线性、多尺度及多分辨信号的分析方法——子波分析在癫痫脑电信号检测和分析中的应用。重点介绍子波分析在脑电信号去噪、自动检测及癫痫发作过程的多尺度特征分析,癫痫发作动力学研究,以及癫痫发作预报等方面的应用。为癫痫脑电信号的临床应用及发病机理的研究提供一种新的方法。     

基于脑电样本熵和小波熵的麻醉深度监测

目的:通过研究全麻手术病人的脑电信号特征,从分类准确率、算法难易程度、计算时间等方面讨论样本熵和小波熵算法在麻醉深度监测中的应用。方法:基于脑电信号的非线性和不稳定性,采用两种非线性动力学分析方法(样本熵和小波熵)对30例全麻手术病人的脑电信号进行特征提取,并对每位病人清醒状态、轻度麻醉状态和中度麻醉状态下的脑电信号的样本熵和小波熵进行差异分析。结果:不同麻醉状态下的脑电信号的样本熵和小波熵均有明显差异。相同脑电信号的样本熵的变化阈值较小波熵的变化阈值大。结论:样本熵和小波熵算法均可以作为麻醉深度监测的有效指标。从分类准确率、算法难易程度和计算时间等方面考虑,使用样本熵算法的效果优于小波熵算法。     

不同思维状态下脑电近似熵的变化规律

背景:近似熵是一种描述信号复杂性和规律性的非线性动力学方法,只需较少数据就能度量信号的复杂性。目的:探讨不同思维状态下脑电近似熵的变化规律,以及近似熵在认知过程中的作用。方法:用近似熵对20名健康成年人在安静闭眼、安静睁眼、闭眼记忆、闭眼心算和图片识别5种状态下的脑电数据进行分析。结果与结论:近似熵值在闭眼计算和闭眼记忆思维状态高于安静闭眼状态,在图片识别状态下高于安静睁眼状态(P0.01);近似熵在安静闭眼和安静睁眼状态下各导联处于较低水平,在闭眼心算和闭眼记忆思维状态下各导联处明显增加。说明不同思维状态和不同导联部位对近似熵均有影响;近似熵在认知作业过程下较安静状态增高,并且不同思维状态下大脑功能活动的复杂性不同。因此脑电近似熵分析适用于认知过程脑功能活动变化规律研究,有助于了解大脑的工作机制。     

基于近似熵的癫痫发作预测研究

临床采用诱发方法检测获得的失神发作患者EEG信号,研究其发作前脑电信号的动力学变化的规律,寻找预测癫痫失神发作一般规律和方法.我们选择合适的电极对,使用非线性动力学的方法,采用复杂度变化度量的近似熵指标,通过闪光刺激癫痫患者获得的EEG信号进行动力学特征研究,根据EEG信号表现出的同步情况实现对癫痫发作的预测.结果表明...     

基于小波变换和Teager能量算子的癫痫脑电自动分类

提出一种利用小波变换和能量算子对EEG进行预处理提取癫痫特征信号,进行近似熵估计,对脑电信号进行分类的新方法。首先利用小波分析将EEG信号进行4层分解分成多个子频带,对频率接近棘波的第1,2层小波系数计算非线性能量算子,再对能量算子进行近似熵估计,最后用SVM对EEG信号进行分类。结果表明,该方法对癫痫发作期EEG和正常的EEG分类效果比较理想。     

癫痫脑电的双谱特性研究

双谱分析对于分析处理非高斯、非线性随机信号具有明显优点.脑电信号被认为具有非高斯、非线性的特性.本文对不同发作阶段癫痫患者的脑电信号进行双谱估计,进而研究不同生理条件下脑电的双谱特性.结果表明,不同发作阶段时癫痫脑电信号的高斯偏离程度明显不同,其中双相干系数能够区分不同发作阶段脑电的信号特征,有望成为临床监护和预报癫痫发作的一个指标.     

Entropy changes in brain function.

The traditional way of analyzing brain electrical activity, on the basis of electroencephalography (EEG) records, relies mainly on visual inspection and years of training. Although it is quite useful, of course, one has to acknowledge its subjective nature that hardly allows for a systematic protocol. In the present work quantifiers based on information theory and wavelet transform are reviewed. The "relative wavelet energy" provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The "normalized total wavelet entropy" carries information about the degree of order-disorder associated with a multi-frequency signal response. Their application in the analysis and quantification of short duration EEG signals (event-related potentials) and epileptic EEG records are summarized.     

Application of wavelet-based similarity analysis to epileptic seizures prediction

Epileptic seizures prediction is an interesting issue in epileptology, since it can promise a novel approach to control seizures and understand the mechanism of epileptic seizures. In this paper, we describe a new method, called wavelet-based nonlinear similarity index (WNSI), to predict epileptic seizures using EEG recordings in real time. This method combines wavelet techniques and nonlinear dynamics. The test results of EEG recordings of rats and humans show that WNSI can track the hidden dynamical changes of brain electrical activity. Particularly, we found that it can obtain the best performance of seizure prediction at the beta (10-30 Hz) frequency band of EEG signals. A possible reason is suggested from the functional connectivity of the brain. In terms of this study, it is recommended that wavelet technique is very useful to improve the performance of epileptic seizures prediction.     

A comparison analysis of embedding dimensions between normal and epileptic EEG time series

The embedding dimensions of normal and epileptic electroencephalogram (EEG) time series are analyzed by two different methods, Cao's method and differential entropy method. The results of the two methods indicate consistently that the embedding dimensions of EEG signals during seizure will change and become different from that of normal EEG signals, and the embedding dimensions will vary intensively during seizure, whereas the embedding dimensions of normal EEG signals basically maintains stability. The embedding dimension results also reflect the variation of freedom degree of the human brain nonlinear dynamic system (NDS) during seizure. And based on the results of Cao's method, it is also found that normal EEG signals are of some degree of randomness, whereas epileptic EEG signals have determinism.     

Epileptic seizure detection using DWT-based approximate entropy,Shannon entropy and support vector machine: a case study

In this work, we have used a time–frequency domain analysis method called discrete wavelet transform (DWT) technique. This method stand out compared to other proposed methods because of its algorithmic elegance and accuracy. A wavelet is a mathematical function based on time-frequency analysis in signal processing. It is useful particularly because it allows a weak signal to be recovered from a noisy signal without much distortion. A wavelet analysis works by analysing the image and converting it to mathematical function which is decoded by the receiver. Furthermore, we have used Shannon entropy and approximate entropy (ApEn) for extracting the complexities associated with electroencephalographic (EEG) signals. The ApEn is a suitable feature to characterise the EEGs because its value drops suddenly due to excessive synchronous discharge of neurons in the brain during epileptic activity in this study. EEG signals are decomposed into six EEG sub-bands namely D1–D5 and A5 using DWT technique. Non-linear features such as ApEn and Shannon entropy are calculated from these sub-bands and support vector machine classifiers are used for classification purpose. This scheme is tested using EEG data recorded from five healthy subjects and five epileptic patients during the inter-ictal and ictal periods. The data are acquired from University of Bonn, Germany. The proposed method is evaluated through 15 classification problems, and obtained high classification accuracy of 100% for two cases and it indicates the good classifying performance of the proposed method.     

A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram

We describe a strategy to automatically identify epileptiform activity in 18-channel human electroencephalogram (EEG) based on a multi-resolution, multi-level analysis. The signal on each channel is decomposed into six sub-bands using discrete wavelet transform. Adaptive threshold is applied on sub-bands 4 and 5. The spike portion of EEG signal is then extracted from the raw data and energy of the signal for locating the exact location of epileptic foci is determined. The key points of this process are identification of a suitable wavelet for decomposition of EEG signals, recognition of a proper resolution level, and computation of an appropriate dynamic threshold.     

基于C-FFuzzyEn的神经电生理信号同步性分析

基于传统互模糊熵,结合分数阶微积分提出分数阶互模糊熵（C-FFuzzyEn）,并基于该算法分析混沌耦合系统的同步性,进行健康对照者和癫痫患者不同脑区脑电信号的耦合性对比。结果表明,与传统互模糊熵相比,C-FFuzzyEn提高了不同耦合度模型的区分能力;与健康对照者相比,癫痫患者在癫痫发作时不同通道脑电信号之间C-FFuzzyEn较小,与癫痫发作时各神经元同步放电现象相吻合;相比互模糊熵,C-FFuzzyEn区分健康对照者与癫痫患者脑区之间脑电信号同步性的效果更好。C-FFuzzyEn可应用于脑电信号等神经电生理信号的同步性分析。     

Phase Spectral Analysis of EEG Signals

A new method of phase spectral analysis of EEG is proposed for the comparative analysis of phase spectra between normal EEG and epileptic EEG signals based on the wavelet decomposition technique. By using multiscale wavelet decomposition, the original EEGs are mapped to an orthogonal wavelet space, such that the variations of phase can be observed at multiscale. It is found that the phase (and phase difference) spectra of normal EEGs are distinct from that of epileptic EEGs. That is the variations of phase (and phase difference) of normal EEGs have a distinct periodic pattern with the electrical activity proceeds in the brain, but do not the epileptic EEGs. For epileptic EEGs, only at those transient points, the phase variations are obvious. In order to verify these results with the observational data, the phase variations of EEGs in principal component space are observed and found that, the features of phase spectra is in correspondence with that the wavelet space. These results make it possible to view the behavior of EEG rhythms as a dynamic spectrum.