首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到19条相似文献,搜索用时 156 毫秒
1.
小波能量评价EEG的不同成分对癫痫发作预报的价值   总被引:4,自引:0,他引:4  
癫痫是一种严重危害人类健康的常见疾病,对癫痫发作进行预报具有重要的重要意义。通过对3例部分性继发全身性发作的癫痫病人在发作最长约30min的8导EEG进行小波分解,将EEG中的棘波、尖波成分与慢波成分分别突出到不同的尺度上,并计算相应尺度上这些成分的能量,考察这些不同成分在发作前的变化趋势。发现在发作前的若干分钟,8导EEG的慢波能量都有显著增大,而与棘波/尖波有关的快波能量基本上没有什么变化趋势,说明EEG慢波成分的增大对部分性继发全身性发作的预报具有重要价值,EEG的“慢波过大”可能是癫痫从发作间状态转变为发作的重要因素。  相似文献   

2.
小波神经网络在脑电信号数据压缩与棘波识别中的应用   总被引:1,自引:0,他引:1  
介绍了一种新的神经网络模型———小波神经网络,利用它并适当调节网络、小波基参数,实现了对脑电信号的压缩表达,较好的恢复了原有信号。另外,在其算法研究的基础上,提出了适应于非稳态和非线性信号处理的时频分析新方法。在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,与传统的短时傅立叶变换(STFT)和Wigner分布相比,此方法有更高的分辨率和自适应性,而且其时频能量分布没有交叉项干扰。  相似文献   

3.
海马CA3和内嗅皮层(entorhinal cortex,EC)网络是海马认知功能和颞叶癫痫研究的关键回路之一,尖波对海马网络theta(4~8 Hz)节律抑制作用的研究有利于揭示癫痫对认知功能影响的机制。以往,在网络层面上,该抑制作用常借助脑片来实现定量评估。本文旨在建立依赖于脑深度局部场电位评估癫痫尖波对theta节律抑制作用的方法。从4位术前处于快速眼球运动(rapid eyes movement,REM)睡眠下的颞叶癫痫患者皮质电极记录中择取发作间期有散发性尖波(sporadic spikes,SSs)脑电和两个相邻SSs间无尖波暂态期脑电,尖波分别只在CA3、只在EC、或在CA3和EC同步出现,应用Gabor小波和Hilbert变换计算尖波前后和无尖波暂态期theta能量,并计算无尖波暂态期theta节律的断裂程度。结果显示:(1)尖波可瞬时降低theta能量,CA3和EC同步尖波时下降最为剧烈,抑制作用最强;(2)无尖波暂态期theta能量下降,出现theta节律消失,造成节律断裂,表明抑制作用在持续,且断裂程度与尖波附近抑制作用一致;(3)3例患者无尖波暂态期theta能量水平降低程度与尖波附近抑制作用一致,而1例不一致。本文结果提示,SSs可对theta节律产生瞬时、直接的抑制作用,该抑制作用可在无尖波暂态期持续,并可由theta节律断裂程度反映。该工作首次应用局部场电位证明了癫痫尖波对theta节律抑制作用可通过无尖波时脑电节律的断裂程度来评估,为利用脑电衡量癫痫尖波抑制作用提供了量化分析方法。  相似文献   

4.
脑电信号数据压缩及棘波识别的小波神经网络方法   总被引:1,自引:0,他引:1  
本文在对小波神经网络及其算法研究的基础上,提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。实验结果显示,小波网络在大量压缩数据的同时,能够较好的恢复原有信号,另外,在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,说明此方法在电生理信号处理和时频分析方面有着光明的应用前景。  相似文献   

5.
介绍了用于肌肉动态收缩期间非平稳表面肌电信号的时频分析方法。用短时傅里叶变换、Wigner-Ville分布及Choi-Williams分布计算了表面肌电信号的时频分布,用于信号频率内容随时间演化的可视化观察。通过计算瞬时频谱参数,对肌肉疲劳的电表现进行量化描述。分析了反复性的膝关节弯曲和伸展运动期间从股外侧肌所记录的表面肌电信号。发现和在静态收缩过程中观察到的平均频率线性下降不同,在动态收缩期间瞬时平均频率的变化过程是非线性的并且更为复杂,且与运动的生物力学条件有关。研究表明将时频分析技术应用于动态收缩期间的表面肌电信号可以增加用传统的频谱分析技术不能得到的信息。  相似文献   

6.
在保持完整血液循环的鲫鱼眼杯标本上,应用Ag-AgCl电极记录视网膜电图(ERG),研究了急性低氧下不同适应状态ERG反应变化的情况,以期分析视锥与视杆通路对急性低氧的敏感性是否不同。结果表明:1.急性低氧对明视ERG-b波的影响要远远快于对暗视b波的影响,这说明视锥信号通路比视杆信号通路对缺氧敏感;2.在间视状态下,ERG的b波在低氧开始反几分钟内有一个明显的增大过程,而在明视或暗视中皆未观察到  相似文献   

7.
采用基于神经网络的算法预测了我们自行克隆的新的白血病相关蛋白EEN(extra elevennineteen, EEN)全长分子的二级结构,结果表明:EEN 蛋白可能有三个结构域,N 端由三段α螺旋和短β折叠组成,中间为四段α螺旋组成的四螺旋结构,C端为SH3结构域,类似于在受体酪氨酸激酶信号传导途径中起重要作用的SEM-5/GRB2 C端SH3结构域;利用同源蛋白结构模拟的方法,模拟了EEN SH3结构域的三维结构,结果表明:EEN SH3结构域与SEM-5/GRB2 SH3结构域具有相近的结构,构成脯氨酸结合区的氨基酸非常保守.上述结果提示:EEN 蛋白可能为新的信号蛋白,可能涉及新的信号传导途径或新的信号传导旁路,SH3结构域是其功能区域.  相似文献   

8.
目的:探究尖波间期和无尖波恢复期颞叶癫痫大鼠模型的海马CA1网络theta节律随癫痫发展进程的变化规律。方法:14只成年雄性Wistar大鼠(200~250 g)麻醉后开颅,在海马的背侧埋入一个双极性钢电极(直径小于1 mm),在颅骨上埋入三个不锈钢皮质电极,在左和右额皮质内分别植入两个电极,在小脑埋入参考电极,粘合颅骨,检测、记录大鼠脑电图;之后腹腔注射癫痫诱发药物,匹罗卡品氢氯化物(pilocarpine hydrochloride,310 mg/kg),30 min后给予大鼠莨菪碱 (scopolamine, 1 mg/kg);借助14只大鼠在嗅行(exploration)时脑深部记录(SEEG),应用Gabor小波时频能量分析估算断裂段数(以350 ms为1段),与总段数的比值定义为断裂比,用来衡量theta节律断裂程度,分别计算了癫痫发展进程的早期(D7)和晚期(D25)中两个邻近尖波之间时间段(定义为尖波间期)和随后无尖波恢复期theta节律断裂比,与注射前脑电图比较。结果:① 与对照脑电图比较,癫痫诱发大鼠的D7和D25的theta节律断裂比显著升高(P<0.05),并且D7远高于晚期D25;② 在无尖波恢复期,theta节律断裂比与尖波间期相当 (P<0.05)。结论:癫痫尖波直接导致了theta节律断裂,断裂程度将随癫痫发展进程而动态变化,早期伤害尤其严重。  相似文献   

9.
利用阴离子交换层析、凝胶过滤及阳离子交换层析三步方法, 从皖南尖吻蝮蛇毒中分离纯化到一个新的抗凝血因子ACFII(anticoagulation factorII) 。纯化的ACFII在PAGE、SDSPAGE和IEFPAGE图谱上均呈单一区带。ACFII由两条分子量为14.6 kD 的肽链通过二硫键连接在一起, 其等电点为7.0。ACFII具有显著的抗凝血活性, 在体外延长PPT 时间的最终浓度为0 .4 mg/L。ACFII不具有类凝血酶活性、磷脂酶A2 活性和纤溶活性,也没有出血活性和毒性, 是一种潜在的高效抗凝药物。  相似文献   

10.
中华竹鼠胃肠道内分泌细胞分布型的研究   总被引:5,自引:0,他引:5  
杨贵波  王平 《兽类学报》1996,16(4):303-308
本文用免疫组织化学方法(PAP法)对10种GIEC在中华竹鼠(Rhizomyssinensis)15个胃肠段中的分布作了观察和统计分析,以探讨中华竹鼠胃肠内分泌细胞与其特殊食性的适应关系.结果表明:中华竹鼠胃肠中可能至少有7种免疫反应活性内分泌细胞。与大熊猫相比,尽管都以竹类为主食,但由于取食行为不同,它们GIEC的分布型不尽相同.这些结果从一定程度上表明GIEC的分布不仅与食物组成相关。也可能与取食行为有关。  相似文献   

11.
基于经验模态分解(EMD)理论,提出一种左右手运动想象脑电信号分析方法。首先利用时间窗对脑电信号数据进行划分,对每段数据通过经验模态分解法将其分解为一组固有模态函数IMF,提取主要信号所在的IMF层去除信号中的噪声。对含有主要信号的几层IMF进行Hilbert变换,得到瞬时频率与对应的瞬时幅值。再提取左右手想象的特定频段mu节律和beta节律的能量信号作为特征,分别利用支持向量机(SVM)和Fisher进行了分类比较。对EMD和小波包在去噪和特征提取进行了比较。结果表明,EMD是一种很有效的去噪方法,经过EMD分解后提取的能量信号在区分左右手想象上更具有优势,识别率高。  相似文献   

12.
The recently described slow oscillations of amplitude of theta and alpha waves of the EEG (with a frequency below 0.08 Hz) in healthy subjects are attributed to the autonomic nervous system with control at the brain stem level. In the present pilot study, the slow brain rhythms were analyzed in a patient with Alzheimer's disease and were compared to a healthy subject. Dynamic analysis of the EEG was performed using time-frequency mapping which gives simultaneous time and frequency representation of the brain signal. This method comprises a transform of the filtered EEG signal into its analytic form and application of the Wigner distribution modified by time and frequency smoothing. It has been shown that the envelope of both theta and alpha activities oscillates at 0.04 Hz and 0.07 Hz in the healthy subject and at 0.03 Hz and 0.06 Hz in a patient with Alzheimer's disease. The amplitude of the slow oscillations of theta activity was substantially higher in the patient with Alzheimer's disease as compared with the healthy subject. It is being proposed that the increase of slow brain rhythms in the patient with Alzheimer's disease reflects an abnormal activity of the autonomic nervous system. However, the underlying pathophysiological mechanisms need to be further studied.  相似文献   

13.
Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony. Action Editor: Carson C. Chow  相似文献   

14.
Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.  相似文献   

15.
We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.  相似文献   

16.
Surface myoelectric signals often appear to carry more information than what is resolved in root mean square analysis of the progress curves or in its power spectrum. Time-frequency analysis of myoelectric signals has not yet led to satisfactory results in respect of separating simultaneous events in time and frequency. In this study a time-frequency analysis of the intensities in time series was developed. This intensity analysis uses a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal. Special procedures were developed to calculate intensity in such a way as to approximate the power of the signal in time. Applied to an EMG signal the intensity analysis was called a functional EMG analysis. The method resolves events within the EMG signal. The time when the events occur and their intensity and frequency distribution are well resolved in the intensity patterns extracted from the EMG signal. Averaging intensity patterns from multiple experiments resolve repeatable functional aspects of muscle activation. Various properties of the functional EMG analysis were shown and discussed using model EMG data and real EMG data.  相似文献   

17.
In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.  相似文献   

18.
The continuous wavelet transform was applied to the human EEG signals recorded in different states of brain activity. The dynamics of local maxima chains in the matrices of the continuous wavelet transform coefficients was studied. The typologization method was developed for local maxima chains to separate by their drift in the frequency space as well as by dynamics of their signal “energy.” The method proved to be highly informative. It was shown that it was highly sensitive to a selection of one of two responses to the test question. It is determined that local maxima chains in most cases are gradually increasing and decreasing in the frequency space and by changes in the values of their continuous wavelet transform coefficients. The functional asymmetry in local maxima chains types’ distribution is determined. The results obtained allow us to consider the types of the local maxima chains dynamics as a new phenomenon of EEG activity.  相似文献   

19.
《IRBM》2008,29(1):44-52
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.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号