改进G-P算法与睡眠脑电的关联维

脑电的非线性参数能有效表达大脑的不同生理状态,不同睡眠期脑电的关联维数不同.但用G- P算法求关联维数存在抗干扰能力较差、可靠性不稳定、运算量巨大等缺点.先对相空间进行奇异谱分析,进而对原始相空间进行旋转,使其成为正交的等效空间,然后再使用G- P算法.改进后的算法能有效地抑制噪声干扰,降低相空间规模,减少运算复杂性,在睡眠脑电的关联维数计算上效果良好.     

睡眠脑电的研究

睡眠脑电是研究睡眠的重要且有力的工具。本文在阐述睡眠与脑电关系的基础上，着重论述了非线性方法：人工神经网络、混沌与分形及复杂性在睡眠脑电研究中的应用。     

生物医学信号的高密度表达法及其在睡眠脑电分析 …

在对心电、脑电、肌电等生物医学信号的长时间监测过程中，会获得大量的数据。目前对这些数据采用二维波形法显示，即使是高分辨率的屏幕也只能显示３Ｋ数据。本文首次提出了时域彩色地图技术，应用本文提出的方法可以提高信息表达密度５２０倍，使用户对全部数据有一个整体的观察，易于识别数据中隐藏的信息。本文以睡眠脑电波的处理为例详细讨论了该方法。实践表明可把信息分析时间从原来的２天缩短到１０分钟。     

应用近似熵对睡眠脑电进行分期的研究

主要介绍了一种新的度量序列复杂性的统计方法－近似熵（ＡｐＥｎ）并将其于睡眠脑电的研究，提取出各睡眠期的近似熵特征，实验结果显示，从清醒期到ＮＲＥＭ期的Ⅲ，Ⅳ期，近似熵由最大依次减小，到ＲＥＭ期又回以接近，Ｉ，Ⅱ期，根据这一特征对睡眠脑电进行分期实验，得到良好的效果，说明近似熵是一种值得重视的，很有前景的复杂度度量方法。     

基于复杂性测度的睡眠脑电分期处理方法研究

提高临床脑部及神经系统疾病的早期诊断水平,及时予以控制和治疗,是降低脑疾病对人类危害最有效的途径。探讨复杂性测度在睡眠脑电分期中的应用,主要利用加窗的Lempel-Ziv复杂度处理算法对经采用小波变换滤波算法滤除生理干扰后的睡眠脑电信号进行分期处理,并与没有加窗的Lempel-Ziv复杂度处理算法的仿真处理结果进行比较。结果表明：加窗的Lempel-Ziv复杂度算法能更好地将睡眠脑电不同状态分开,在一定程度上减少由脑电的非平稳性带来的计算上的片面性,同时兼顾各期睡眠脑电状态的不均匀性,在很大程度上满足临床的应用要求。     

睡眠脑电的自回归模型阶数特性

传统睡眠脑电 (Sleep EEG)研究从信号的时域和频域的特征分析睡眠过程 ,通常根据功率谱观察信号中特定节律的出现和频带的分布。而功率谱估计中基于参数模型的方法得到广泛应用 ,但建模时通常只能根据经验选择一个固定较低的阶数。本文讨论了自回归模型阶数 (Autoregressive m odel order,ARMO)估计准则的一些最新进展 ,并且统计了一段睡眠过程中 EEG的阶数分布。结果显示 EEG的 ARMO分布集中在差别很大的几个区间 ,可以用来表示睡眠 EEG分期内微结构和过渡过程 ,并在一定程度上提供 EEG的特征和组成成分的信息     

用奇异值分解改善脑电偶极子探测

脑电偶极子探测是研究脑电的重要方法之一。众所周知,偶极子探测的数学表达式为: V=A(Y)·X (1) S(Y)=V~T〔I-A(A~TA)~(-1)A~T〕V (2) 转移矩阵A与偶极子位置和方向参数矢量Y的函数关系,由人脑偶极子正向简化模型给出。     

抑郁症、原发性失眠睡眠脑电生理特征的对照研究

目的:探讨抑郁症、原发性失眠睡眠脑电生理特征的异同点。方法:对25例抑郁症、15例原发性失眠和14例正常人进行多导睡眠的对照研究。结果:抑郁症和原发性失眠在睡眠潜伏期(SL)、睡后觉醒次数(AN)、觉醒总时间(AT)、觉睡比(AT/TST%)方面均较正常对照组长(或高)(P0.01),在睡眠总时间(TST)、睡眠效率(SE)、睡眠维持率(SM)S1、S2时间方面均较正常对照组短(或低)(P0.05);抑郁症在REM活动度(RA)、REM活动强度(RI)、REM活动密度(RD)、第一个REM时间(FRT)、REM出现次数(RSN)等方面均较原发性失眠长(或高)(P0.01);原发性失眠在REM时间(RT)、REM百分比(RT%)方面差异非常显著低于抑郁症(P0.01),在REM潜伏期(RL)、睡后觉醒次数(AN)显著高于抑郁症(P0.05)。结论:抑郁症和原发性失眠患者均存在一定特征性的睡眠异常,抑郁症与原发性失眠的快波睡眠特征有差异。     

重度阻塞性睡眠呼吸暂停患者脑电能量密度分布及变化规律分析

目的 阻塞性睡眠呼吸暂停(obstructive sleep apnea,OSA)是一种在临床有较高病发率的睡眠障碍,其病发机制为上气道在睡眠期间反复阻塞,会引发低氧血症、频繁低通气、睡眠唤醒、呼吸暂停等病理生理改变。多导睡眠监测(polysomography, PSG)是临床上诊断OSA的金标准,可以监测患者睡眠期间的脑电波变化。本文通过分析重度OSA男性患者睡眠规律和脑电(electroencephalogram, EEG)能量密度分布及变化规律,探究重度OSA患者的睡眠脑电变化。方法 选取30名男性重度OSA患者和30名男性正常对照组,对60名受试者进行整夜(8 h左右)的多导睡眠监测,对得到的睡眠特征和脑电参数进行整理,提取睡眠与脑电特征。对比睡眠监测得到的睡眠特征和其前额区的脑电能量密度。结果 在睡眠特征方面,重度OSA患者睡眠期时间为370.20 min,睡眠效率平均为86.90%,非快速眼动1期平均占比19.90%,非快速眼动2期平均占比42.55%,对比正常人睡眠时间、睡眠效率以及非快速眼动1期、2期占比明显降低。在脑电特征方面,重度OSA患者整夜睡眠θ波的绝对能量密度...     

基于各物理因素对睡眠剥夺者脑电的影响

目的:研究不同的物理因素对被睡眠剥夺的受试者脑电的影响.方法:将24名成年健康男性随机分为四组,一个对照组和三个实验组,对所有受试者进行48 h睡眠剥夺实验,其中三个实验组在实验过程中分别不断接受音乐、磁场和局部高压氧干预措施,每个6 h提取一次受试者的脑电波数据.对睡眠脑电数据首先应用小波变换,将脑电信号中的噪声进行消噪处理,然后再利用近似熵和复杂度将消噪后的脑电信号进行特征提取和分析.结果:对于睡眠剥夺的不同时间段,其脑电近似熵的平均值随着脑疲劳程度的增加而降低.实验的前半部分受试者的脑电波的复杂度和近似熵没有统计学差异,但是实验的后半部分受试者的脑电波的复杂度和近似熵有了明显的区别.结论:不同的物理因素可以影响受试者的状态,从而影响受试者的脑电波.脑电近似熵随着脑疲劳程度的增加而降低,有望成为衡量脑疲劳程度的指标.     

Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis

Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.     

Phase Spectral Analysis of EEG Signals

A new method of phase spectral analysis of EEG is proposed for the comparative analysis of phase spectra between normal EEG and epileptic EEG signals based on the wavelet decomposition technique. By using multiscale wavelet decomposition, the original EEGs are mapped to an orthogonal wavelet space, such that the variations of phase can be observed at multiscale. It is found that the phase (and phase difference) spectra of normal EEGs are distinct from that of epileptic EEGs. That is the variations of phase (and phase difference) of normal EEGs have a distinct periodic pattern with the electrical activity proceeds in the brain, but do not the epileptic EEGs. For epileptic EEGs, only at those transient points, the phase variations are obvious. In order to verify these results with the observational data, the phase variations of EEGs in principal component space are observed and found that, the features of phase spectra is in correspondence with that the wavelet space. These results make it possible to view the behavior of EEG rhythms as a dynamic spectrum.     

时窗复杂度序列在睡眠脑电分期中的应用

运用时窗复杂度序列来分析睡眠脑电，减少了非平稳性及状态空间的不均匀性造成的脑状态信息的丢失，在一定程度上克服了复杂度自身的局限，有助于不同睡眠期状态特征的提取。另外采用独立分量分析（ICA），小波变换等方法对脑电进行预处理，实验表明它们能有效地去除脑电中的一些生理干扰，有利于提高复杂度算法在睡眠分期应用中的精确度。     

EOG and Delta Rhythmicity in Human Sleep EEG

The use of the electroencephalogram (EEG) is accepted as the only objective method of studying sleep. However, EEG sleep study may not be feasible, although it would be highly desirable, in some non-laboratory operational environments due to difficulties in EEG recordings. Results of the present study showed that the ultradian delta rhythmicity of sleep as defined by EEG could be obtained from electro-oculograms (EOGs) alone.     

利用小波级数检测睡眠EEG中的K-复合波

本文提出了一种利用小波级数检测睡眠脑电中Ｋ－复合波的方法。这种方法使用Ｄａｕｂｅｃｈｉｅｓ正交小波基,把ＥＥＧ信号分解成４个尺度上的小波级数,利用最大尺度的信号逼近检测Ｋ－复合波中的大慢波脉冲,然后,利用信号细节检测紧跟大慢波脉冲之后的梭形波。     

睡眠EEG的多尺度信息熵分析

本文提出一种基于连续小波变换的睡眠EEG分析方法。该方法使用Morlet小波计算EEG信息的小波变换系数,通过计算EEG信号在多个尺度上小波系数的熵分析睡眠EEG。结果表明：浅睡阶段EEG信号的多尺度熵的变化模式与深睡阶段的多尺度熵的变化模式不同,REM睡眠期间EEG信号的多尺度熵的变化与深睡阶段类似,使用多尺度熵可以区分REM睡眠和浅睡时EEG之间的差别。     

Sleep and EEG profile in neonatal hippocampal lesion model of schizophrenia

Sleep architecture, EEG power pattern and locomotor activity were investigated in a putative animal model of schizophrenia. The model was prepared by excitotoxic damage of the ventral hippocampus on postnatal day 7 (PD 7), after which locomotor activity and electroencephalographic (EEG) sleep profile were compared between lesioned and sham operated animals respectively, at prepuberty (postnatal day PD 35) and postpuberty (PD 56). An enhancement of locomotor activity was observed in lesioned adult PD 56, but not in juvenile PD 35 rats. Spontaneous EEG/EMG recordings during 24 h showed no major differences between both groups at PD 35 and at PD 56. However, quantitative analysis of the EEG revealed an enhancement of power in delta (delta), theta (theta) and alpha (alpha) activities in lesioned animals at PD 35 during wakefulness in both light and dark phases. At PD 56, the power in the delta and theta bands was increased during the light and dark periods in both wakefulness and non-REM sleep. These findings suggest that ventral hippocampus lesion is not associated with disturbance of sleep architecture in rats, while consistent changes were observed in the dynamic of EEG slow wave frequency domain. Thus, the data indicate that neonatal lesion of ventral hippocampus did not mimic sleep abnormalities observed in schizophrenia, however this rodent model may model some EEG features seen in schizophrenia such as a frontally pronounced slowing of the slow EEG activity in delta and theta frequency bands.     

Variations in Period-Analysed EEG Asymmetry in REM and NREM Sleep

Monopolar EEG was recorded from lateral frontal and parietal sites with linked ear reference during sleep in 24 adults. Electrode placement followed the 10-20 International system. EEG was quantified using digital period analyses. The absolute difference in interhemispheric EEG parameters was compared for Stage 2, REM, and a slow wave sleep measure. The absolute difference measures reflect the degree of symmetry or asymmetry, regardless of the hemisphere of origin. Theta and delta activity in slow wave sleep was more asymmetrical than in either Stage 2 or REM. REM sleep was associated with the smallest asymmetries. These results do not support a right hemisphere REM, left hemisphere NREM relationship. Rather they suggest that REM sleep is associated with relative hemispheric symmetry whereas asymmetries are most prominent in slow wave sleep. Stage 2 sleep was significantly less asymmetrical than slow wave on a number of theta and delta measures. The significant differences between slow wave and Stage 2 sleep may denote functional differences within NREM sleep stages.