新生儿睡眠期的脑电非线性分析方法

目的脑电（electroencephalogram,EEG）是新生儿脑功能监护中重要的生理信号,近年研究发现基于非线性动力学的复杂度分析能够客观反映大脑成熟度、睡眠周期和惊厥状态等。方法本文针对神经系统发育正常的早产新生儿组和足月新生儿组,采用近似熵（approximate entropy,ApEn）和样本熵（sample entropy,SampEn）两种非线性参数,对新生儿在安静睡眠期（quiet sleep,QS）和活动睡眠期（active sleep,AS）的脑电信号进行分析。结果神经系统发育正常的新生儿中,AS期的ApEn和SampEn均高于QS期,且具有显著性差异;随着受孕后年龄（postmenstrual age,PMA）的增大,新生儿QS期的ApEn和SampEn的值均随之增加,且波动逐渐减弱,而AS期的ApEn和SampEn的值并无显著变化;绝大多数新生儿在AS期与QS期的SampEn之差高于ApEn之差。结论AS期新生儿EEG的复杂度大于QS期的复杂度;随着PMA的增大,新生儿EEG的复杂度提高,脑功能发育趋于成熟;ApEn与SampEn在表现新生儿脑电信号复杂度上趋势一致,但SampEn在区分AS与QS方面更具优势。     

基于无线体域网和复合生理信号近似熵的驾驶疲劳研究

为有效合理地评价驾驶员在驾驶过程中的疲劳程度,通过无线体域网(WBAN)采集12名被试者的3种生理信号(脑电信号、肌电信号、呼吸信号),提取并分析这些生理信号的近似熵在驾驶过程中对疲劳程度的反映效果。试验结果表明,脑电、肌电、呼吸3种信号的近似熵均随时间的增加而逐渐下降,约90 min后下降程度变缓,表明进入比较疲劳状态。通过主成分分析可知,前两个主成分的贡献率分别为47.33%和40.26%,二者之和大于85%,其中脑电信号和肌电信号的近似熵所占权重较大。而且统计分析表明,脑电信号和肌电信号的近似熵显著性P＜0.05,表明脑电信号和肌电信号在表征正常与疲劳两种状态时差异明显。在此基础上,分析信号组合对驾驶疲劳的反映效果,结果表明在脑电肌电信号的近似熵组合时,正常和疲劳状态的概率分布具有明显的界限,可以有效反映驾驶过程中的疲劳状态。通过研究,获得最佳的反映疲劳驾驶的信号组合,可为更加准确地检测和避免疲劳驾驶提供理论依据。     

基于多模态特征组合的真实驾驶疲劳状态识别

对驾驶员在驾驶过程中的疲劳状态进行实时准确的预判,可减少由于驾驶疲劳引发的交通事故。首先,通过无线体域网采集了12名驾驶员在真实驾驶过程中的多模态特征,提取了脑电、肌电、呼吸等3种生理信号的特征参数近似熵,其中基于畸变能密度理论(DED)确定肌电信号的采集位置为颈6棘突旁开2 cm处的上斜方肌;然后,通过模糊C聚类方法分析了3种特征参数组合对疲劳状态的反映效果;最后,建立基于马氏距离理论的真实驾驶疲劳判别模型。研究结果表明,驾驶员颈6部位比颈7部位肌电信号的ApEn值显著下降(P<0.05),表明颈6处肌肉比颈7处肌肉对驾驶员的疲劳状态反映更为敏感,实际检测结果与畸变能密度理论计算结果一致,证明了该位置提取肌电信号的正确性和准确性;脑、肌、呼吸这3种生理信号的ApEn值均随驾驶时间的延长呈递减变化,驾驶约90 min时递减趋势变缓,表明驾驶员进入疲劳状态;通过模糊C聚类分析可知,当脑电与肌电ApEn组合时,清醒与疲劳的概率分布界限清晰,可有效反映驾驶疲劳状态;以脑电和肌电近似熵为自变量,基于马氏距离理论建立真实驾驶过程的疲劳判别模型,其测试集准确率达90.92%,表明该模型能够比...     

基于低频电刺激诱发表面肌电信号的肌肉疲劳度研究

目的 通过对肌肉疲劳过程中非诱发表面肌电(surface electromyography,sEMG)信号和诱发表面肌电信号的研究分析,寻找有效评价肌肉疲劳的分析测量方法.方法 对7名受试者进行自主运动和电刺激两种致肌疲劳的实验,并在两组实验中分别记录电刺激诱发与非诱发肌电信号,然后对每组信号进行傅里叶变换求取功率谱和近似熵.结果 随着疲劳的产生,两组实验诱发信号的频谱曲线左移效果优于非诱发信号,近似熵分析中电刺激组诱发信号出现先上升后下降的变化,自主运动组诱发信号则呈现单调递减的趋势.结论 低频电刺激诱发表面肌电信号更适于测量肌疲劳的动态变化.相对于传统功率谱,近似熵分析方法更适于处理电刺激诱发的表面肌电信号.     

严重意识障碍患者康复过程EEG非线性特征与CRS-R评分相关性分析与可视化表征

研究严重意识障碍患者意识康复过程脑电非线性特征与行为学意识量表CRS-R评分之间的相关性,并将非线性特征用于脑功能的可视化表征,为建立脑电非线性特征用于评估严重意识障碍患者意识康复过程提供电生理学依据。采集严重意识障碍患者(最小意识状态(MCS)10例和植物状态(VS)8例)意识康复过程中前后两次安静状态的脑电数据,计算近似熵、样本熵、排列熵和复杂度LZC等4种脑电非线性特征,对比分析非线性特征变化与CRS-R评分变化之间的相关性,并利用脑电地形图将近似熵非线性特征进行可视化表征。结果表明,MCS组和VS组CRS-R评分变化均为显著差异(P<0.01);MCS组的近似熵和复杂度LZC变化为显著差异(P<0.05),而VS组只有近似熵变化为显著差异(P<0.05);MCS组近似熵、复杂度LZC的变化与CRS-R评分变化的相关系数分别为0.851和0.693,呈显著正相关,VS组只有近似熵变化与CRS-R评分变化为显著正相关,相关系数为0.778;近似熵用于患者脑地形图可视化表征具有较好的效果。近似熵非线性特征用于评估严重意识障碍患者的意识康复过程具有一定的可行性。     

α频率光刺激脑电信号同步化的初步研究

研究α频率(8～13Hz)闪光刺激是否能引起人脑枕区同频率脑电信号的增加.采集15名正常志愿者在平静、光刺激状态下的脑电信号,利用脑地形图、小波分析和功率谱估计的方法对α频率光刺激前后的脑电信号进行处理,将归一化后的数据分为平静-激活组、男女对照组、两个不同电极位置组进行对比分析,对组间信号的差异进行了讨论,并对受试者精神状态的变化进行初步探讨.结果表明:周期性α频率光刺激能引起大脑枕区同频率脑电的显著增加.通过分析实验数据,初步得到了大脑在外部闪光刺激时脑电的变化规律,本实验结果对研究外部刺激对脑电的影响有一定的参考价值.     

醉酒者和正常人脑电的样本熵研究

为了探索应用非线性动力学理论研究醉酒者脑电信号的新方法,采用非线性动力学指标:样本熵对醉酒者和正常人的脑电信号进行分析和比较。试验随机选取了相同数目的醉酒者和正常人,运用样本熵算法对他们的脑电数据进行计算,得到样本熵值,然后对这些熵值进行分析。结果显示:醉酒者的样本熵普遍都大于正常人的样本熵,表明大脑在醉酒状态下复杂度会比正常状态要高。样本熵能够有效地区分醉酒者和正常人的脑电状态。     

α频率光刺激脑电信号同步化的初步研究

研究α频率（8-13Hz）闪光刺激是否能引起人脑枕区同频率脑电信号的增加。采集15名正常志愿者在平静、光刺激状态下的脑电信号,利用脑地形图、小波分析和功率谱估计的方法对α频率光刺激前后的脑电信号进行处理,将归一化后的数据分为平静-激活组、男女对照组、两个不同电极位置组进行对比分析,对组间信号的差异进行了讨论,并对受试者精神状态的变化进行初步探讨。结果表明：周期性α频率光刺激能引起大脑枕区同频率脑电的显著增加。通过分析实验数据,初步得到了大脑在外部闪光刺激时脑电的变化规律,本实验结果对研究外部刺激对脑电的影响有一定的参考价值。     

基于近似熵的思维脑电信号分类研究

采用近似熵(approximate entropy，Apen)作为脑电信号的特征参数对不同思维作业脑电信号进行了分类研究，并对近似熵算法中参数的选择以及互近似熵在思维脑电分类的应用进行了讨探。研究结果表明，近似熵特征在思维作业脑电信号的分类中取得了较好的应用效果。近似熵作为EEG的信号特征为提高思维作业脑电信号的分类正确率提供了一种新的途径，在基于思维作业BCI的应用中具有重要的实用价值。     

大鼠癫痫模型脑电信息的非线性分析

为了探索应用非线性动力学理论进行癫痫疾病预报的新方法，采用非线性动力学指标：近似熵和相关维对大鼠癫痫发作过程的整个脑电信号进行分析研究。结果显示癫痫发作时，脑电信号的近似熵和相关维明显低于发作前和发作后，这表明了癫痫发作过程脑电信号复杂度的变化规律；而癫痫发作前又是一特殊的阶段，其脑电信号的复杂度已开始降低，但发作症状尚未出现。因此运用非线性动力学方法对癫痫发作至少在短期内可预报。     

Fuzzy Approximate Entropy Analysis of Chaotic and Natural Complex Systems: Detecting Muscle Fatigue Using Electromyography Signals

In the present contribution, a complexity measure is proposed to assess surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the complexity of experimental data that is often corrupted with noise, short data length, and in many cases, has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an improved ApEn measure, i.e., fuzzy approximate entropy (fApEn), which utilizes the fuzzy membership function to define the vectors’ similarity. Tests were conducted on independent, identically distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, Rossler equation, and Henon map. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, and more robustness to noise when characterizing signals with different complexities. Performance analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the EMG signal, while the standard ApEn failed to detect this change. Moreover, fApEn of EMG demonstrated a better robustness to the length of the analysis window in comparison with the MNF of EMG. The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for muscle fatigue assessment and be applicable to other short noisy physiological signal analysis.     

近似熵理论及应用

目的:介绍近似熵分析生物信号的优点,探讨时间序列的参数对近似熵值的影响,阐述近似熵在医学的应用现状与前景.方法:根据定义和算法,介绍近似熵与关联维、分形维数、Lyaponov指数等非线性分析方法相比估计信号的复杂度所具有的优点;依据近似熵算法,本文编制程序定量地分析近似熵值与时间序列各参数的关系;人体脑电信号和心电信号等生物信号能反映出人体所处的状态,检测生物信号的近似熵值变化,定量的监测和检测某组织的状态.结果:近似熵具有计算所需数据短,抗噪能力好等优点,弥补了关联维等非线性分析方法的缺陷;近似熵与时间序列的幅值无关;在低频,近似熵值几乎是随频率增大而增大;当计算长度大于1000个点时,近似熵是稳定的,定量说明近似熵计算所需数据短;目前,近似熵在诊断、监测、麻醉学等医学领域广泛运用,并取得很好的成果.结论:近似熵一种新发展起来的定量估计信号复杂度的非线性分析方法,为诊断、监测一些疾病提供一种新的方法,具有广泛的应用前景.     

样本熵及在脑电癫痫检测中的应用

对癫痫进行检测和预报具有非常重要的临床意义。首先采用近似熵对癫痫患者的脑电信号进行分析，探索运用近似熵进行癫痫检测的可行性。针对近似熵存在的问题，选用一种与近似熵类似但精度更好的方法一样本熵，并同近似熵就在癫痫脑电信号中的应用进行了比较分析。结果显示癫痫发作时脑电信号的近似熵和样本熵均明显低于发作前和发作后。样本熵的变化幅度明显大于近似熵，样本熵的变化幅度相对于近似熵提高了约10％～25％。     

Complexity analysis from EEG data in congestive heart failure: A study via approximate entropy

Aim

Congestive heart failure (CHF) is a very complex clinical syndrome that may lead to ischemic cerebral hypoxia condition. The aim of the present study is to analyze the effects of CHF on brain activity through electroencephalographic (EEG) complexity measures, like approximate entropy (ApEn).

Methods

Twenty patients with CHF and 18 healthy elderly people were recruited. ApEn values were evaluated in the total spectrum (0.2–47 Hz) and main EEG frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–11 Hz), alpha 2 (11–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz), and gamma (30–45 Hz) to identify differences between CHF group and control. Moreover, a correlation analysis was performed between ApEn parameters and clinical data (i.e., B-type natriuretic peptides (BNP), New York Heart Association (NYHA), and systolic blood pressure (SBP)) within the CHF group.

Results

Statistical topographic maps showed statistically significant differences between the two groups in the total spectrum and theta frequency band. Within the CHF group, significant negative correlations were found between total ApEn and BNP in O2 channel and between theta ApEn and NYHA scores in Fp1, Fp2, and Fz channels; instead, a significant positive correlation was found between theta ApEn and SBP in C3 channel and a nearly significant positive correlation was obtained between theta ApEn and SBP in F4 channel.

Conclusion

EEG abnormalities in CHF are very similar to those observed in cognitive-impaired patients, suggesting analogies between the effects of neurodegeneration and brain chronic hypovolaemia due to heart disorder and underlying high brain sensitivity to CHF.     

胎儿心率变异性的近似熵分析在胎儿窘迫诊断中的应用

介绍一种近似熵分析方法,并将其应用于胎心率变异性的研究中,探讨近似熵分析结果与临床胎儿窘迫的相关性。对67例次胎心电子监护(CTG)结果经胎心率变异性的近似熵分析及时域分析。新生儿出生后记录Apgar评分,取脐动脉血行血气分析。结果显示胎心率变异性的近似熵值ApEn与脐血血气分析指标pH、Pco2、Po2、HCO3-、ABE、SO2及新生儿评分Apgar有显著相关性(r=0.51、-0.29、0.49、0.29、0.45、0.56、0.28,P值均<0.05);酸中毒组(pH≤7.2)胎心率变异性的ApEn值显著低于正常组(pH>7.2)(P<0.01),而常规时域分析指标平均胎心率(BHR)、胎心率标准差(SD)、胎心率变异系数(CV)则无显著性差别。提示胎心率变异性的近似熵值是一较敏感的反映胎儿缺氧酸中毒的指标,近似熵分析方法可为胎儿窘迫早期诊断的准确性提供新的思路。     

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.     

脑电近似熵分析对帕金森病患者认知功能障碍的评估作用

目的：观察帕金森病（PD）患者与对照者的脑电近似熵的差异,并探讨其对PD认知功能障碍的评估作用。方法：对31例PD患者和31例对照组进行简易精神状态检查量表（MMsE）测试、蒙特利尔认知评估量表（MoCA）测试、事件相关电位（ERP）检测P300波潜伏期（P3PL）,采集安静闭眼、闭眼心算、安静睁眼三种状态下的脑电信号,利用非线性参数近似熵对脑电数据进行分析。结果：①与对照组相比,PD组MMSE评分和MoCA评分均降低,P3PL延长,差异有统计学意义;②与对照组相比,在安静闭眼、闭眼心算、安静睁眼三种状态下,PD组的近似熵在大部分脑区降低,差异有统计学意义这些脑区在安静闭眼状态时是Fp1、Fp2、F4、O2、F8、T3、T4、T5、T6,在闭眼心算状态时是Fp1、F3、F4、C3、P3、O1、T3、T5、T6,在安静睁眼状态时除C4外的15个脑区;③与对照组相比,PD患者近似熵变化值于F3、C3、P3、O1降低,差异有统计学意义;④所有受试者安静闭眼、闭眼心算、安静睁眼三种状态下大部分脑区近似熵值与MMSE评分、MoCA评分呈正相关,与P3PL呈负相关。结论：脑电近似熵可以作为一项客观评定PD患者认知功能障碍的电生理指标。     

脑电生物反馈合并艾司西酞普兰治疗老年广泛性焦虑障碍的对照研究

目的:观察脑电生物反馈联合艾司西酞普兰治疗老年广泛性焦虑障碍的疗效和安全性。方法:选择45例老年广泛性焦虑障碍患者随机分为两组,研究组给予脑电生物反馈联合艾司西酞普兰治疗,对照组仅给予艾司西酞普兰治疗,均治疗8周。分别于治疗前及治疗后1,2,4,6,8周时应用汉密尔顿焦虑量表(HAMA)评估其疗效;应用副作用量表(TESS)评估其不良反应。结果:在治疗第1周、第2周两组HAMA总分比较有显著性差异(t=-3.40,-3.65;P0.05),治疗组分数低于对照组,在第4周到第8周两组HAMA两组总分比较无显著性差异,在治疗第1周、第2周研究组躯体性焦虑因子分明显低于对照组(t=-2.05,-2.72;P0.05),精神性焦虑因子两组比较无显著性差异(P0.05);研究组有效率为82.6%,对照组有效率为81.8%,两组有效率比较无显著性差异;两组间TESS评分比较无明显差异性。结论:脑电生物反馈联合艾司西酞普兰治疗广泛性焦虑障碍起效更快,尤其对躯体焦虑的改善更快,并且不增加不良反应。     

Complexity analysis of spontaneous EEG

The aim of the present paper is the assessment of the overall complexity of spontaneous and non-paroxysmal EEG signals obtained from three groups of human subjects, e.g., healthy, seizure and mania. Linear complexity measure suitable for multi-variate signals, along with nonlinear measures such as approximate entropy (ApEn) and Taken's estimator are considered. The degree of linear complexity is significantly reduced for the pathological groups compared with healthy group. The nonlinear measures of complexity are significantly decreased in the seizure group for most of the electrodes, whereas a distinct discrimination between the maniac and healthy groups based on these nonlinear measures is not evident.