癫痫脑电信号的奇异性研究

基于小波变换模极大值在多尺度上的变化,研究了癫痫脑电的奇异性,并用Lipschitz指数来表征.提出了一种高阶统计的方法来研究癫痫脑电的高阶奇异谱特征,并和健康脑电进行比较.实验结果表明,癫痫脑电的Lipschitz指数和高阶奇异谱与健康脑电相比存在明显的差异,说明该方法对研究脑电是有效的.     

Approximate Entropy Analysis of Electroencephalogram

Based on the time-delayed embedding method of phase space reconstruction, a new method to compute the approximate entropy(ApEn) of electroencephalogram (EEG) is proposed. The computational results show that there are significant differences between epileptic EEG and normal EEG in the approximate entropy with the variance of embedding dimension. This conclusion is helpful to analyze the dynamical behavior of different EEGs by entropy.     

The Spatial Equivalence Between Wavelet Decomposition and Phase Space Embedding of EEG

Using both the wavelet decomposition and the phase space embedding, the phase trajectories of electroencephalogram (EEG) is described. It is illustrated based on the present work,that is,the wavelet decomposition of EEG is essentially a projection of EEG chaotic attractor onto the wavelet space opened by wavelet filter vectors, which is in correspondence with the phase space embedding of the same EEG. In other words, wavelet decomposition and phase space embedding are equivalent in methodology. Our experimental results show that in both the wavelet space and the embedded space the structure of phase trajectory of EEG is similar to each other. These results demonstrate that wavelet decomposition is effective on characterizing EEG time series.     

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。     

Independent Source Separation of Multichannel Electroencephalogram Based on Neural Network

A neural network method for independent source separation （ISS） of multichannel electroencephalogram （EEG） is proposed in this paper. Using the denoising function of wavelet multiscale decomposition, the high-frequency noises are removed from the original （raw） EEGs. Then the multichannel EEGs are treated as the weighted mixtures and the expression of weight vector is obtained by seeking the local extrema of the fourthorder cumulants （i. e. kurtosis coefficients） of the mixtures. After these process steps, the weighted mixtures are used as the input of neural network, so the independent source of EEGs can be separated one by one. The experimental results show that our method is effective for ISS of multichannel EEGs.     

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。     

基于小波变换的脑电图癫痫波形检测

脑电图中癫痫波形的自动检测与分类是临床上很有意义的工作。我们根据脑电图中的癫痫特征波形，利用小波变换的时频局部化特性，给出了一种高效的癫痫波表的自动检测方法，构造了一个连续的癫痫波检测系统。通过检测不同尺度上的局部极大值，确定出对应的脑电图中的锐变点位置，并由此检测出脑电图中的癫痫波，从初步临床试验的结果来看，系统具有检测精度高，可连续作业等优点，获得了较好的效果。     

Estimation of obscure ictal epileptic activity in scalp EEG

Summary We have produced a method to estimate ictal localized epileptic activity hidden among the background in scalp EEGs. When the visually completely different waveforms of the epileptic and background activities are nearly orthogonal, epileptic activity may be approximately extracted from the EEG data matrix by singular value decomposition with subsequent orthogonal rotation to match the distribution of one component with that of the epileptic source. A simulation study was carried out using a matrix mimicking the scalp EEG with an inconspicuous ictal epileptic activity from a dipole source. This hidden epileptic activity was approximately recovered by matching the dipole of interest with the epileptic dipole, even when the simulated waveforms of the epileptic and background activities were not exactly orthogonal. High linear correlation between these two types of waveforms hampered the recovery of the epileptic activity. In another simulation study employing two epileptic dipoles producing activities with the same waveform and a brief time lag, it was indicated that the temporal relationship between the epileptic activities could be also estimated using the cross-correlation function. In the preliminary clinical application of this method to the ictal EEGs of complex partial seizures, rhythmic activities with seemingly epileptic waveforms were estimated at the dipoles which were located in the vicinity of cortical lesions revealed by neuroimaging studies. These activities were indicated to appear before any change in the scalp EEG. We hope for the clinical application of this method for noninvasive estimation of inconspicuous ictal epileptic activity.The authors thank Prof. Peter K.H. Wong of the Department of Paediatrics, University of British Columbia, Canada, and Prof. Yutaka Tanaka and Mr. Kim Hyun Bin of the Department of Environmental and Mathematical Sciences, Faculty of Environmental Science and Technology, Okayama University, Japan, for their technical suggestions. This study was aided by a grant from the Japan Epilepsy Research Foundation.     

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.     

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.     

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.     

多分辨率小波信号分解用于大鼠睡眠纺锤波的分析

本研究首先设计了慢波睡眠期脑电信号的合成仿真信号 ,对小波基函数进行了选择 ,结果证明Coiflet 5阶小波变换对大鼠慢波睡眠期EEG信号具有较好的分解结果。据此 ,应用多分辨率小波分析法设计了提取睡眠纺锤波的算法 ,并利用该算法对安定用药后和睡眠剥夺后大鼠慢波睡眠期纺锤波的持续时间和能量变化进行了分析 ,结果表明 :安定具有延长慢波睡眠期纺锤波持续时间的作用 ,而睡眠剥夺可以增加慢波睡眠期纺锤波的能量。这些结果说明 ,小波分析算法可以提供功率谱分析无法表现的时频信息。     

Phase relationships of alpha rhythm in man

Stationary phase relationships of human scalp EEGs in the frequency range of the alpha rhythm were examined by the method of cross-spectral analysis. EEGs were recorded from midsaggital points equally spaced on the scalp in normal and blind adults. Cross-spectral analysis was applied to one- or three-minute records and to successive q0-second records. The generalized component, which usually formed a dominant peak in the EEG spectra, showed a gradual phase advance toward the frontal region but seldom reached 180 degrees, and the phase shift at intermediate points was not linearly related to the distance between them. A parallel relation was confirmed between the increase in the inter-regional phase difference and the decrease in the coherence value. A variety of phase differences was noticed for the more localized subordinate component, and the angles of this component ranged more broadly than with the generalized dominant component. Phase relations of the alpha rhythm in the blind were similar to those of the localized component in normal subjects. Thus, in respect to inter-regional relationships in the antero-posterior direction, the alpha rhythms were classified into at least two types, one closely related to the visual function and the other depending upon some functions other than visual. Effectiveness of the cross-spectral analysis on phase relations of the EEG was discussed in comparison with other methods.     

基于匹配追踪的癫痫脑电信号的时频分析

目的 鉴于匹配追踪算法具有良好的参数化描述特性,应用匹配追踪算法研究癫痫脑电的时频分布特征.方法 通过仿真算例,将匹配追踪算法与短时傅里叶变换、Wigner-Ville分布结果进行比较,验证该方法的频率分辨率高及参数化表征的优越性;应用上述3种方法对癫痫脑电和正常脑电进行时频分析,研究癫痫异常放电在时频平面的表现.结果 仿真结果表明基于匹配追踪算法能得到较好的时频分布;对癫痫脑电和正常脑电进行时频分析,癫痫脑电和正常脑电在时频平面上存在明显的差异.结论 基于匹配追踪的时频分析方法,能够更好地揭示脑电类非平稳信号的特征.     

癫痫手术治疗的皮层电图监测

目的：探讨皮层电图（ＥＣｏＧ）监测对切除癫痫病灶的应用价值及适应症。方法：对１０４例难治性癫痫患者使用盘状电极行ＥＣｏＧ监测下切除癫痫灶进行研究。结果：术前脑电图（ＥＥＧ）检查８６例异常（８２７％）。皮层电图１０２例异常（９８％）。术后随访３个月至４年,有８８例（８４６％）临床发作已控制,１６例发作次数明显减少。复查常规ＥＥＧ有痫样放电６例,慢波局限性改变５例,其余ＥＥＧ均为正常。术前术后ＥＥＧ阳性率相差显著。结论：在ＥＣｏＧ监测下切除癫痫灶具有一定的临床价值     

Wavelet-based feature extraction for classification of epileptic seizure EEG signal

Electroencephalogram (EEG) signal-processing techniques are the prominent role in the detection and prediction of epileptic seizures. The detection of epileptic activity is cumbersome and needs a detailed analysis of the EEG data. Therefore, an efficient method for classifying EEG data is required. In this work, a constructive pattern recognition strategy for analysing EEG data as normal and epileptic seizure has been proposed. With this strategy, the signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to reduce the dimensionality of EEG data. These reduced features were used as input to Naïve Bayes and K-Nearest Neighbour Classifier to classify normal or epileptic seizure signal. The performance of classifier was evaluated in terms of accuracy, sensitivity and specificity. The experimental results show that PCA with Naïve Bayes classifier provides 98.6% accuracy and LDA with Naïve Bayes classifier attains improved result of 99.8% accuracy. Also, the result shows that PCA, LDA with K-NN achieves 98.5% and 100% accuracy. This evaluation is used to propose a reliable, practical epilepsy detection method to enhance the patient’s care and quality of life.     

司法鉴定为癫癎性精神障碍者的脑电图分析

目的：探讨经司法精神病鉴定为癫癎性精神病障碍者的脑电图（ＥＥＧ） 改变特点,及与精神障碍表现、临床发作类型、案件类型的关系。方法：对５２ 例经司法精神病鉴定为癫癎性精神障碍患者的ＥＥＧ 资料进行回顾性分析。结果：常规ＥＥＧ 异常率为５８ ％ 。广泛异常２２ 例,其中轻度１１ 例,中度９例,重度２ 例;局灶性异常８ 例。智能障碍、人格改变并智能障碍异常率均较高,且大多为广泛异常。伤害罪、抢劫罪、纵火罪、杀人罪及被奸淫案异常率比其他案件类型高。结论：癫癎性精神障碍ＥＥＧ 有其特征性改变,与其病因、发作频次、精神障碍表现和临床发作类型有关,但与案件类型无关。     

用相空间内邻近轨线伸展的相关性研究脑电的混沌特性

本文的研究针对目前脑电的非线性动力学研究中一个尚未定论的关键问题：脑电究竟是混沌信号还是无序的随机信号？文中介绍了作者的研究成果：用相空间中一对邻近轨线伸展的伸关性来鉴别混沌和无序的随机运动,并应用这种新的混沌行为分析方法证明了脑电的混沌特性,作为比较和验证,本文也介绍了用该方法对加上不同水平噪声和Lorenz系统仿真的研究结果。     

Circadian variation of EEG power spectra in NREM and REM sleep in humans: Dissociation from body temperature

In humans, EEG power spectra in REM and NREM sleep, as well as characteristics of sleep spindles such as their duration, amplitude, frequency and incidence, vary with circadian phase. Recently it has been hypothesized that circadian variations in EEG spectra in humans are caused by variations in brain or body temperature and may not represent phenomena relevant to sleep regulatory processes. To test this directly, a further analysis of EEG power spectra - collected in a forced desynchrony protocol in which sleep episodes were scheduled to a 28-h period while the rhythms of body temperature and plasma melatonin were oscillating at their near 24-h period - was carried out. EEG power spectra were computed for NREM and REM sleep occurring between 90-120 and 270-300 degrees of the circadian melatonin rhythm, i.e. just after the clearance of melatonin from plasma in the 'morning' and just after the 'evening' increase in melatonin secretion. Average body temperatures during scheduled sleep at these two circadian phases were identical (36.72 degrees C). Despite identical body temperatures, the power spectra in NREM sleep were very different at these two circadian phases. EEG activity in the low frequency spindle range was significantly and markedly enhanced after the evening increase in plasma melatonin as compared to the morning phase. For REM sleep, significant differences in power spectra during these two circadian phases, in particular in the alpha range, were also observed. The results confirm that EEG power spectra in NREM and REM sleep vary with circadian phase, suggesting that the direct contribution of temperature to the circadian variation in EEG power spectra is absent or only minor, and are at variance with the hypothesis that circadian variations in EEG power spectra are caused by variations in temperature.     

基于小波包分析的意识任务特征提取与分类

将基于小波包变换的多尺度分析方法应用于自发脑电 (EEG)的特征提取。在对 3种意识任务的脑电信号进行多级小波包分解的基础上 ,将不同尺度空间的能量信号作为特征值 ,组成不同意识任务的特征向量 ,并利用径向基函数神经网络进行分类测试。结果表明 ,小波包变换方法的分类正确率高于自回归模型方法。小波包分析方法可以作为不同意识任务脑电信号特征提取的一种新方法 ,具有较强的稳定性