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。     

样本熵及在脑电癫痫检测中的应用

对癫痫进行检测和预报具有非常重要的临床意义。首先采用近似熵对癫痫患者的脑电信号进行分析，探索运用近似熵进行癫痫检测的可行性。针对近似熵存在的问题，选用一种与近似熵类似但精度更好的方法一样本熵，并同近似熵就在癫痫脑电信号中的应用进行了比较分析。结果显示癫痫发作时脑电信号的近似熵和样本熵均明显低于发作前和发作后。样本熵的变化幅度明显大于近似熵，样本熵的变化幅度相对于近似熵提高了约10％～25％。     

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。     

脑电同步性分析在癫痫机制研究中的应用

脑电相位同步性是研究癫痫超同步放电机制的方向之一。介绍了应用Hilbert变换提取脑电的瞬时相位的方法,分析脑电相位同步性的互相关法、互信息法和同步指数法,以及脑电信号的小波变换,综述了以上方法在癫痫发作的超同步放电机制研究中的应用。     

脑电同步性分析在癫痫机制研究中的应用

脑电相位同步性是研究癫痫超同步放电机制的方向之一。介绍了应用Hilbert变换提取脑电的瞬时相位的方法，分析脑电相位同步性的互相关法、互信息法和同步指数法，以及脑电信号的小波变换，综述了以上方法在癫痫发作的超同步放电机制研究中的应用。     

发作间期癫痫患者脑电非线性分析

目的：通过比较癫痫发作间期不同发作类型患者与正常对照组之间脑电非线性特点,探讨非线性脑电图在发作间期癫痫患者脑功能评价中的意义及应用价值。方法：对发作间期36例癫痫发作患者（其中2i例强直阵挛发作患者和15例复杂部分性发作）和32例健康对照组分别行安静闭眼、闭眼心算、安静睁眼3种脑功能活动状态下的脑电图检查,经专门软件采样处理得出相应的关联维数、近似熵并加以分析。结果：强直阵挛发作组不同脑功能活动状态下关联维数、近似熵与健康对照组相比在安静闭眼、闭眼心算、安静睁眼状态下均无明显差异,复杂部分性发作组与健康对照组相比在安静闭眼状态下于额、顶、颞叶相关导联的关联维数、近似熵下降,闭眼心算状态下近似熵在顶叶、颞叶导联下降。结论：脑电图之关联维数、近似熵有助于评价不同癫痫发作类型患者发作间期的脑功能状态。     

基于改进锁相位算法的脑区功能同步性研究

锁相位算法是分析脑区同步特性的有效方法。基于传统锁相位算法只针对脑电信号的相位分量进行分析,而不能有效地分析脑电信号的其他特征分量的问题,本文提出了一种改进锁相位算法。该算法首先基于经验模态分解获得固有模态函数,以此作为希尔伯特变换的输入求出所需瞬时幅值,计算锁幅值。基于此算法,不仅可以度量脑电信号采集位点之间的变化情况,而且可以度量各个位点自身的脑电信号瞬时振幅的变化情况,从而达到提取脑电信号同步特征的效果。本文采集了14名受试者在不同难度认知任务下的脑电信号,并基于改进锁相位算法,分析大脑在完成认知任务过程中各脑区之间的同步性。结果表明,大脑幅值同步程度与认知活动之间呈负相关,且大脑中央区和顶叶最为敏感。基于改进锁相位算法进行的同步性量化,能够真实地反映数据之间的生物信息,为大脑同步性研究提供了一种新的方法,为更好地探索脑区的相关性与同步性提供新思路。     

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。     

基于小波多尺度分析和极限学习机的癫痫脑电分类算法

癫痫脑电的自动分类对于癫痫的诊断和治疗具有重要意义。本文提出了一种基于小波多尺度分析和极限学习机的癫痫脑电分类方法。首先,利用小波多尺度分析对原始脑电信号进行多尺度分解,提取出不同频段的脑电信号。然后采用Hurst指数和样本熵两种非线性方法对原始脑电信号和小波多尺度分解得到的不同频段脑电信号进行特征提取。最后,将得到的特征向量输入到极限学习机中,实现癫痫脑电分类的目的。本文采用的方法在区分癫痫发作期和发作间期时取得了99.5%的分类准确率。结果表明,本方法在癫痫的诊断和治疗中具有很好的应用前景。     

基于离散S变换和排列熵的癫痫脑电识别

脑电图(EEG)分析对癫痫疾病的诊断具有重要的参考价值,对癫痫脑电信号的自动分类可以及时对患者的情况作出判断,在临床上有很重要的意义。为解决脑电信号采用单一特征识别率不高的问题,同时也为避免小波基函数的选取对分类结果的影响,本文提出了一种基于S变换和排列熵(PE)的癫痫脑电信号自动判别方法,首先将原始脑电信号进行离散S变换,再对变换后脑电信号各节律的系数分别求其波动指数,并与脑电信号的排列熵值共同组成特征向量送入Real Ada Boost分类器进行癫痫各时期的判别。本研究采用德国波恩大学癫痫研究中心数据库,对正常人清醒睁眼,癫痫患者发病间歇期致痫灶内及发作期3组脑电信号数据进行方法有效性检验。研究结果表明,各节律的波动指数可有效表征正常、癫痫发作间期和癫痫发作期脑电信号,且多种特征的识别率明显优于单一特征,平均识别率可达到98.13%,相比于仅提取时频特征或非线性特征,识别率分别提高了1.2%和8.1%以上,优于文献中报道的多种方法。因此,本方法在癫痫疾病的诊断方面有较好的应用前景。     

小波熵在脑电信号分析中的应用

小波熵是一个衡量非线性信号多尺度动力学行为有序、无序程度的量化指标,其可提供信号非线性动力学过程复杂程度的信息.近年来,小波熵在脑电信号中的研究日益受到关注,国内外学者用小波熵研究脑电信号、诱发电位、事件相关电位等的复杂程度,进一步揭示了大脑电活动的动力学机制.其主要应用于大脑感知、认知活动的研究,癫痫脑电信号的动态观测,睡眠、网络成瘾、头外伤后脑神经的康复等几个方面.小波熵不仅可以显示受到刺激后脑电信号频率上同步化的动态演变过程,而且可以有效区分癫痫发作前状态和癫痫发作状态,从而加深了对脑动力学机制的理解,成为认知功能研究的一种新的方法,显示了在脑电信号分析中良好的应用前景.     

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 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.     

Visually evoked phase synchronization changes of alpha rhythm in migraine: correlations with clinical features.

OBJECTIVE: This study aimed to compute phase synchronization of the alpha band from a multichannel electroencephalogram (EEG) recorded under repetitive flash stimulation from migraine patients without aura. This allowed examination of ongoing EEG activity during visual stimulation in the pain-free phase of migraine. METHODS: Flash stimuli at frequencies of 3, 6, 9, 12, 15, 18, 21, 24, and 27 Hz were delivered to 15 migraine patients without aura and 15 controls, with the EEG recorded from 18 scalp electrodes, referred to the linked earlobes. The EEG signals were filtered in the alpha (7.5-13 Hz) band. For all stimulus frequencies that we evaluated, the phase synchronization index was based on the Hilbert transformation. RESULTS: Phase synchronization separated the patients and controls for the 9, 24 and 27 Hz stimulus frequencies; hyper phase synchronization was observed in patients, whereas healthy subjects were characterized by a reduced phase synchronization. These differences were found in all regions of the scalp. CONCLUSIONS: During migraine, the brain synchronizes to the idling rhythm of the visual areas under certain photic stimulations; in normal subjects however, brain regions involved in the processing of sensory information demonstrate desynchronized activity. Hypersynchronization of the alpha rhythm may suggest a state of cortical hypoexcitability during the interictal phase of migraine. SIGNIFICANCE: The employment of non-linear EEG analysis may identify subtle functional changes in the migraine brain.     

基于脑电非线性特征和AdaBoost算法的诱导期麻醉深度检测

提出一种结合自适应增强学习AdaBoost算法和脑电非线性特征的麻醉深度评估方法,通过提取脑电信号中的4种非线性特征（KC复杂度、小波熵、排序熵、模糊熵）作为输入,以双谱指数作为参考输出,将诱导期麻醉深度分为清醒、轻度麻醉、中度麻醉。使用9例全麻患者的诱导期脑电信号对该方法进行评估,3种不同麻醉状态分类准确度为86.69%,Kappa系数为0.837,表明该方法可以较好地区分诱导期3种不同麻醉水平,为麻醉深度监测提供新思路。     

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。     

震颤信号分析的研究现状及展望

震颤是人身体某一个或多个功能区肌肉的节律性、不自主振动,是运动神经元异常同步化的结果。用信号处理的方法检测分析震颤患者加速度(accelerometer,ACC)、表面肌电(electromyography,EMG)、脑电(electroencephalography,EEG)信号对震颤临床诊断、等级评定、疾病早期发现等方面具有重要意义。介绍了时域分析、频域分析、人工神经网络、高阶谱、近似熵、模糊、浑沌、判别分析等方法在震颤信号研究中的应用情况,最后展望了震颤信号分析的应用前景。     

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.     

Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.     

基于小波包变换的癫痫脑电棘波检测

为了能够较好地实现癫痫患者脑电的棘波检测,提出一种将棘波物理特征（幅度、频率）和小波包变换结合的算法,用于癫痫患者脑电信号的棘波检测。首先利用小波包变换对癫痫脑电信号进行小波包分解,将脑电波频率（0~30 Hz）划分为3层;其次根据脑电波的频率范围重构第三层节点频率S（3, 0）（0~10.85 Hz）、S（3, 1）（10.85~21.7 Hz）、S（3, 2）（21.7~32.55 Hz）的脑电信号;最后取棘波的幅度作为检测阈值分别提取癫痫患者健康期、癫痫发作间期及癫痫发作期的棘波。实验结果证明,当数据的采样频率为173.61 Hz、信号长度为23.6 s时,该算法能够提取不同癫痫患者在不同时期的棘波信号,该算法棘波的误检率为12.02%、漏检率为11.70%。因此,本文所采用的算法在癫痫棘波检测中具有良好的效果。