基于小波变换的混沌信号相空间重构研究

应用小波变换和非线性动力学方法研究了混沌信号在相空间中的行为,指出混沌时间序 列的小波变换实质上是在重构的相空间中,混沌吸引子向小波滤波器向量所张的空间中的投 影,与Packard等人提出的相空间重构方法本质上是一致的.实验结果表明,混沌信号经过 小波变换后,吸引子轨迹与原有轨迹具有相似的结构,同时,系统的关联维数、Kolmogorov 熵等非线性不变量仍然得到保留.这些结果表明,利用小波变换研究混沌信号是有效的.关键词：小波变换相空间重构混沌信号脑电信号     

基于LABVIEW的脑电信号虚拟采集系统设计

在神经科学研究领域,对大脑的观察主要来源于对脑电信号的收集与分析。当前对脑电信号收集的方法是通过专业脑电设备将信号收集保存,再由专业软件处理。由于这类仪器非常昂贵,系统体积也比较大,软件更新快,现在只能用在科学研究上,根本无法用于有规模的实验教学,更不可能一人一机。为此,提出了一种基于LABVIEW的脑电信号虚拟采集系统设计方法,使脑电收集与分析可以广泛地应用于教学。该方法首先对脑电信号虚拟采集系统的硬件进行构造,然后以硬件构造为依据,利用AR模型功率谱估计对脑电信号进行特征提取,在特征提取过程中,对模型类型与模型系数算法以及模型最佳阶数进行分析,最后通过将二阶低通滤波器与二阶高通滤波器进行串联,形成4阶Bessel带通滤波器,实现脑电信号的滤波,并以脑电信号传输电路的设计完成脑电信号虚拟采集系统的设计。实验结果证明,所提方法可以快速地对脑电信号虚拟采集系统进行设计,并为该领域的研究发展提供支撑。#$NL关键词：LABVIEW;脑电信号;虚拟采集系统;     

儿童额叶癫痫临床和长程视频脑电图特征

目的通过对46例儿童额叶癫痫患儿的临床表现、长程视频脑电图（VEEG）特点进行分析,提高对儿童额叶癫痫的认识。方法收集2008年12月至2014年8月儿科门诊及住院确诊的46例儿童额叶癫痫病例的资料,回顾性分析其临床表现、VEEG及神经影像学等特征。结果本组患儿发病年龄最小11个月,最大15岁。46例患儿共监测到临床发作258次,明确临床发作34例,14.7%仅于清醒期发作,61.8%仅在睡眠期发作,23.5%在清醒、睡眠中均有发作;临床发作形式包括额叶失神、局部阵挛发作、偏转性强直、姿势性强直、过度运动性自动症、口咽自动症、发声、发笑发作、临床下放电和自主神经性发作等。73.9%患儿记录到发作间期额叶为主的癫痫样放电,63.0%患儿记录到发作期额叶为主的癫痫样放电。结论儿童额叶癫痫临床发作频繁、短暂,以睡眠期发作为主,临床表现复杂多样,易漏诊;长程VEEG可监测到儿童额叶癫痫发作期临床及异常脑电图表现,可提供明确诊断率。     

银-氯化银柔性脑电电极的制备及其评价

本文采用丝网印刷技术制备了一种基于聚酰亚胺(PI)柔性塑料基片的Ag/AgCl脑电电极,并建立了一套系统评价柔性脑电电极性能的方法。评价方法主要包括扫描电镜(SEM)表征、电极电位/时间响应和稳定性测试、电化学阻抗测试、附着性测试。结果表明,该柔性电极表面为多孔结构,且与基底粘附性好;该电极呈现Ag/AgCl的电化学界面性质,其平衡电位为0.97±0.20mV,与Ag/AgCl粉末电极接近;且电极电位一致性和稳定性良好,最大极差电位不超过0.7mV,4h后电位漂移值在10μV/4min以内;经磨砂导电膏GT5处理后,电极-皮肤阻抗在5kΩ以内,满足脑电记录要求;相对于人体皮肤的高阻抗值,柔性电极-导电膏(GT20)的界面阻抗仅为166Ω·cm2。该评价方法系统、实用,可为制定相应国家标准提供技术参考。     

Classification of spontaneous EEG signals in migraine

We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.     

The Tsallis entropy of natural information

Estimating the information contained in natural data, such as electroencephalography data, is unusually difficult because the relationship between the physical data and the information that it encodes is unknown. This unknown relationship is often called the encoding problem. The present work provides a solution to this problem by deriving a method to estimate the Tsallis entropy in natural data. The method is based on two findings. The first finding is that the physical instantiation of any information event, that is, the physical occurrence of a symbol of information, must begin and end at a discontinuity or critical point (maximum, minimum, or saddle point) in the data. The second finding is that, in certain data types such as the encephalogram (EEG), the variance within of an EEG waveform event is directly proportional to its probability of occurrence.These two outcomes yield two results. The first is the easy binning of data into separate information events. The second is the ability to estimate probabilities in two ways: frequency counting and computing the variance within of an EEG waveform. These results are used to derive a linear estimator of the Tsallis entropy functional, allowing it to be estimated without deducing the encoding.This method for estimating the Tsallis entropy is first used to estimate the information in simple signals. The amount of information estimated is highly accurate. The method is then applied to two problems in electroencephalography. The first is distinguishing normal aging from very early Alzheimer's disease (mild cognitive impairment), and the second is medication monitoring of Alzheimer's disease treatment. The former is done with an accuracy of 92% and the latter with an accuracy of 91%. This detection accuracy is the highest published accuracy in the literature, which suggests that this method for Tsallis entropy estimation is both accurate and useful.     

改进的相对转移熵的癫痫脑电分析

脑电信号是由脑神经活动产生并且始终存在于中枢神经系统的自发性电位活动,是一种重要的生物电信号. 脑电信号是非常微弱的且是非线性的,脑电信号也具有时间不可逆性. 本文提出了一种新的基于正向序列转移概率与逆向序列转移概率的相对熵方法即相对转移熵方法,并应用此方法研究了正常脑电与癫痫脑电的不可逆性,实验结果显示癫痫患者的脑电信号的不可逆性明显小于正常人的脑电信号的不可逆性. 这说明改进的相对转移熵可以作为一个物理过程不可逆程度的度量参数,这使得应用脑电信号区分病人是否患有癫痫疾病具有积极指导意义.关键词：相对转移熵脑电信号符号化时间不可逆性     

基于改进的符号相对熵的脑电信号时间不可逆性研究

提出了一种新的使用过程的前向概率和后向概率计算符号相对熵, 并利用符号相对熵来估计熵产的方法. 该方法是基于熵增和过程不可逆特性关系的, 同时证明脑电信号具有时间不可逆特性, 而且该不可逆特性可以提供脑电信号的熵增信息. 最后应用该方法对青老年脑电信号进行数值计算及对比, 结果是老年人的平均能量损耗显著高于年轻人, 证明符号相对熵可以作为一个物理过程不可逆程度的度量参数, 这对脑电信号是否处于积极或平衡状态的诊断治疗具有积极的作用.