婴儿痉挛症脑干信息传导障碍的混沌学研究

婴儿痉挛症（Infantile Spasms,IS）是婴幼儿难治性癫痫。IS的发病机理至今也不清楚,目前比较一致的假说是：脑干是IS的责任结构,但该假说不能用神经生理学的传统方法来证实。从信息学的角度,婴儿弃挛症的发病机制可能是源自脑干神经信息传递障碍的脑干功能失调,为了证实这一论点,用可以反遇脑干传导功能的听觉诱发响应的相关维数,来评估脑干神经信息传导不同的功能状态,经过对IS患者、颞叶癫痫患者和健康人三个组的结果比较分析,表明IS患者脑干听觉诱发响应的相关维数明显低下,而颞叶癫痫患者该相关维数在颞叶病灶处最低。以上结果,证实了IS患者脑干神经信息传导功能障碍的存在。     

基于小波变换的婴儿痉挛症脑干听觉诱发电位复杂性分析

采用多尺度小波变换计算脑干听觉诱发电位近似熵的方法,对比婴儿痉挛症患儿与正常幼儿的近似熵值,按照脑干听觉诱发电位成份波对应的解剖位置,分段、分尺度计算并统计近似熵值,从神经信息传递角度探讨阻碍婴儿痉挛症患儿智能发育的原因。采集12例正常儿童和13例婴儿痉挛症患儿的脑干听觉诱发电位,将它们进行60尺度小波分解,分段、分尺度计算各尺度近似熵值。发现婴儿痉挛症组患儿脑干听觉诱发电位中代表脑干活动的3~7ms段的分尺度近似熵明显高于正常组(P<0.01),小尺度上表现尤为显著。结果表明婴儿痉挛症患儿脑干传导通路不畅通,其中的随机成份增多,阻碍信息在脑干的传递,进而影响患儿大脑皮层的发育。     

基于小波变换的婴儿痉挛症脑干听觉诱发电位近似熵分析

采用多尺度小波变换计算脑干听觉诱发电位近似熵的方法,对比婴儿痉挛症患儿与正常幼儿的近似熵值,按照脑干听觉诱发电位成份波对应的解剖位置,分段、分尺度计算并统计近似熵值,从神经信息传递角度探讨阻碍婴儿痉挛症患儿智能发育的原因.采集12例正常儿童和13例婴儿痉挛症患儿的脑干听觉诱发电位,将它们进行60尺度小波分解,分段、分尺度计算各尺度近似熵值.发现婴儿痉挛症组患儿脑干听觉诱发电位中代表脑干活动的3～7 ms段的分尺度近似熵明显高于正常组(P＜0.01),小尺度上表现尤为显著.结果表明婴儿痉挛症患儿脑干传导通路不畅通,其中的随机成份增多,阻碍信息在脑干的传递,进而影响患儿大脑皮层的发育.     

哀牢山区哈尼梯田的分形特征

以哀牢山区元阳县哈尼梯田为研究对象,以快鸟影像为原始数据,经矢量化得到小流域梯田田块分布图,运用Arcview计算得到梯田边缘的计盒维数和信息维数。结果表明:(1)各地貌单元尺度上的盒子边长ε与其对应的非空盒子数N(ε)在双对数坐标下呈显著线性相关,体现出标度不变性的特征,证明梯田边缘是分形体;(2)梯田形状具有多重自相似结构,且不同尺度梯田形状的自相似程度受下覆地形限制,其自相似结构单元是田块;(3)在研究范围最大的小流域尺度,信息维数最大,梯田边缘的复杂性最高,而在面积较小的坡面尺度上,信息维数值较小,梯田边缘则较简单、结构较清晰。另外,作为刻画格局与过程独立于尺度的分形维数,其应用还存在诸多难题。     

耳声发射信号的小波分析及应用

为了利用瞬态诱发耳声发射信号,对耳蜗性和蜗后性两种感音神经性耳聋进行诊断和定位,提出了一种基于小波变换的方法,用于对感音神经性耳聋病变进行诊断和定位。首先,获得听力正常耳信号的小波变换的对数均方根－频率曲线范围,及在对侧刺激声作用下,信号受抑制的小波变换的对数均方根－频率曲线范围;然后根据某一受试耳有无对侧刺激声作用下的瞬态诱发耳声发射信号,得出其小波变换的对数均方根曲线及受抑制的对数均方根曲线。最后,检测受试耳的两条曲线是否分别在正常值范围内,由此对受试耳的耳蜗及内侧橄榄耳蜗系统功能进行检测,同时根据每一条曲线将蜗性及蜗后病变定位到某一细致的频带。此方法与目前临床上采用的耳声发射结合ABR和纯音测听的诊断方法能很好地吻合。     

急性低氧时豚鼠声信号传入的变化

本工作的目的是研究急性高原低氧对豚鼠听觉传入功能的影响。２０只豚鼠分为４组,分别接受模拟海拔高度为５０００、６０００、７０００和８００ｏｍ的急性低氧暴露,于不同时间测定听觉脑干诱发反应各波的潜伏期、峰间期、振幅和ＩＩＩ波的电反应阈（听阈）。结果如下：当升至预定的海拔高度和在低氧维持阶段,６０００ｍ以上组各波潜伏期延长,其中ＩＩ波变化更明显;回到海平面高度后逐渐恢复,１ｈ内基本可恢复正常;潜伏期变化的程度与海拔高度有关。在低氧阶段各波振幅普遍下降,回到海平面高度后逐渐回升,但恢复至正常所需时间较长。在升至预定的海拔高度和其后低氧维持阶段,各组动物的听阈均上升,脱离低氧后即可迅速恢复,１ｈ内可恢复正常。结果表明,急性高原低氧使听觉脑干传导时间延长,传入神经元兴奋性降低,听敏度下降,因而可以推测它能影响声信号的传入。     

认知障碍患者theta节律脑功能网络连接异常

为了深入观察不同认知功能老年人的大脑功能连接特性,选取了20名老年人参与京剧体验任务,并记录了他们在听京剧前后闭眼静息状态下的脑电信号。根据被试简易精神状态量表得分将其分为高分组与低分组,通过分别计算不同脑电频带下的相位同步指数,构建了两组被试的大脑功能网络;然后,进一步计算了网络连接参数聚类系数和特征路径长度,并从不同角度分析了不同被试的大脑功能连接特性。结果发现:在theta频带下,低分组老年人的全局相位同步指数低于高分组;在京剧体验之后,高分组老年人theta频带的全局相位同步指数有显著提高,而低分组在各频段都没发现京剧效应;在固定阈值的前提下,低分组老年人在较大阈值范围内的聚类系数低于高分组,而特征路径长度高于高分组,表现出小世界特性相对缺失;进一步地,在保证不同被试脑网络边数相同的条件下(即采用相同的度),对两组被试的脑网络参数进行了统计分析,未发现组间差异。综上,较低认知能力的老年人表现出更少的脑网络连接边数。该研究表明,认知障碍老年人脑功能网络相对于正常人有所破损,京剧可以增强老年人的大脑功能连接特性。     

基于盒维数的心音信号分形特征研究

在传统盒维数的基础上,从尺度变化的角度,提出一种计算心音信号时域波形分形维数的新的二进盒维数算法,并给出了算法思想和估算方法;然后用该方法对正常心音和几种典型的病态心音的分形维数进行计算,并对其分形特征进行了研究．研究结果表明：心音信号具有明显的分形特征,分形维数能够反映心音信号的复杂程度,并且能够明显地区分正常心音和病态心音．     

脑干内的混沌

对脑干听觉诱发电位进行了非线性动力学分析,计算了关联维数Ｄ２和最大Ｌｙａｐｕｎｏｖ特征指数,确认ＢＡＥＰ是一个来源于低维混沌动力系统的时间序列,从而证明人类脑干存在着混沌现象,利用满足最小互信息标准的最佳延迟重构了二维混沌吸引子。     

认知功能研究中神经振荡交叉节律耦合应用研究进展

神经振荡交叉节律耦合(cross-frequency coupling,CFC)指不同神经元集群振荡节律之间的交叉调制作用,反映了大脑在不同时空尺度的局部场电位、脑电等神经电生理活动信息的传递与交流机制,是认知神经功能研究的重要工具。本文简要介绍了CFC基本现象与类型,并综述了在动物与人类认知功能研究的典型应用,归纳了现存的主要问题并展望其未来研究动向,以期为促进相关研究与应用提供新思路。     

用子波变换的能量最大准则分析婴儿痉挛症脑干诱发电位

借鉴流体力学中用子波变换识别湍流相干结构的能量最大准则,解释婴儿痉挛症发病的三联征,尤其想说明智能迟滞的原因.从电生理的角度来说明,为什么脑干是婴儿痉挛症的责任病灶——这个生化及病理已经做出的推测.研究发现婴儿痉挛症患儿的脑干通道对外界刺激的反应能力和信息的传递能力与正常儿童有明显差异,借用能量最大准则在湍流中的解释,认为是因为信息传导阻滞引起患儿智能发育的迟滞,并试着提出评价最理想治疗效果的标准.     

Infantile spasms in children with Down syndrome

Down syndrome (DS) is the most common genetic cause of mental retardation. It is estimated that 5-13% of persons affected by DS have seizures. Infantile spasms are the most common type of seizures and usually are well controlled with steroids and antiepileptic drugs. We present 11 children at the age of 3 years and 4 months to 10 years and 7 months with DS and infantile spasms, treated at Children's Hospital Zagreb from January 2000 until July 2009. Infantile spasms began at the age of 5 to 10.5 months in 10 children, in one child at the age of 16 months. Only one child had perinatal risk factors for the development of IS. Changes in EEG correlated to hypsarrhythmia. Infantile spasms were treated initially with antiepileptic drugs, most often with valproic acid. Treatment was inefficient in 10/11 patients. After application of ACTH, infantile spasms stopped between 7 and 15 days in 6 patients, until 28th day in 4 patients. Hypsarrhythmia vanished in all children. During follow-up period (2 years and 7 months to 9 years and 5 months) none of the children developed another type of seizures. No major epileptogenic changes were registered in EEG. Antiepileptic therapy was discontinued in 4 children (aged 4 years and 2 months to 5 years). In this group is the boy who died of heart failure. Infantile spasms associated with DS are categorized into symptomatic group. The existence of cerebral pathology and delayed psycho-motor development precedes occurrence of seizures. It is possible to achieve good control of seizures and disappearance of hypsarrhythmia with application of ACTH and antiepileptic drugs.     

Reduced ACTH, while normal beta-endorphin CSF levels in early epileptic encephalopathies

Since ACTH and the opioids display opposite effects on experimentally-induced seizures, cerebrospinal fluid (CSF) levels of ACTH and beta-endorphin (beta-EP) were measured in 6 children (4-8 months) affected by infantile spasms with hypsarhythmia, an idiopathic early onset encephalopathy, and in 8 age-matched controls. beta-EP levels in the patients (76.3 +/- 14.7 fmol/ml, M +/- SD) did not differ from those in controls (109.8 +/- 42.7) while babies with epileptic encephalopathy showed reduced ACTH levels in the CSF (3.8 +/- 1.5) as compared to controls (9.0 +/- 3.7, p less than 0.01). This resulted in an increased beta-EP/ACTH ratio. Another patient previously treated with ACTH showed a normal CSF level of ACTH (9.0) with a normal beta-EP/ACTH ratio while in clinical and EEG remission. These results are consistent with the hypothesis that some infantile seizures unrelated to brain injuries could originate from an ACTH deficiency at central level and/or an imbalance of neuropeptidergic pathways.     

脑电信号的高阶奇异谱分析

奇异谱分析是脑电信号分析的一种新方法,脑电信号的奇异谱可以反映脑电的特征,它有助于研究大脑的动力学行为。奇异谱分析方法是基于二阶统计的方法,反映的是信号时间上和空间上的一种线性相关关系。而脑电信号属于非线性信号,其内在的非线性关系很难通过奇异谱得到真实的反映,从而会丢失某些有用的信息。提出一种新的基于高阶统计的脑电奇异谱分析方法,并将其运用于正常脑电和癫痫患者的脑电分析中。大量的实测信号样本仿真实验结果表明,正常脑电和癫痫脑电的奇异谱有明显的不同。此外,基于高阶统计的奇异谱和基于二阶统计的奇异谱相比更能反映出信号的细节。     

睡眠剥夺对脑电活动相位相干性的影响研究

将小波变换和相位相干分析应用到事件相关电位实验的脑电信号中。在正常状态和一夜睡眠剥夺状态下提取12名受试者的视觉ERP,进行30～60Hz的小波变换,以此计算前额叶区域的导联内相位相干,以及枕叶和前额叶之间的相位相干性。发现睡眠剥夺引起前额叶的导联内相位相干活动减少和延迟,表明大脑维持完成任务的能力下降;枕叶与前额叶之间的gamma波段相位相干活动减少,表明功能区域之间的电活动传递效应减弱。基于小波变换的相位相干分析可以得到脑电的同步活动,为更好地理解睡眠的机制和评价睡眠剥夺对认知的影响提供了一条思路。     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.