Chaos, Fractal and Recurrence Quantification Analysis of Surface Electromyography in Muscular Dystrophy

We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component. We have estimated the correlation dimension (fractal measure), the largest Lyapunov exponent, the LZ complexity and the %Rec and %Det of the RQA demonstrating that such indexes are able to detect the presence of repetitive hidden patterns in sEMG which, in turn, senses the level of MU synchronization within the muscle. The results give also an interesting methodological indication in the sense that it evidences the manner in which nonlinear methods and RQA must be arranged and applied in clinical routine in order to obtain results of clinical interest. We have studied the muscular dystrophy and evidence that the continuous regime of chaotic transitions that we have in muscular mechanisms may benefit in this pathology by the use of the NPT treatment that we have considered in detail in our previous publications.

Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert Huang transform

Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.

Analysis of Key Features of Non-Linear Behavior Using Recurrence Plots. Case Study: Urban Pollution at Mexico City

The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type non-linear problem. Two different approaches have been used to tackle this problem. The first approach is to show results according to monitoring network. The second approach is to show the results by particle type. This analysis shows the feasibility of using Recurrence Analysis for pollution monitoring and control.

Characterization of various structures in gas-solid fluidized beds by recurrence quantification analysis

Gas-solid fluidized beds are widely considered as nonlinear and chaotic dynamic systems. Pressure fluc- tuations were measured in a fluidized bed of 0.15 m in diameter and were analyzed using multiple approaches： discrete Fourier transform （DFT）, discrete wavelet transform （DWT）, and nonlinear recur- rence quantification analysis （RQA）. Three different methods proposed that the complex dynamics of a fluidized bed system can be presented as macro, meso and micro structures. It was found from DFT and DWT that a minimum in wide band energy with an increase in the velocity corresponds to the transition between macro structures and finer structures of the fluidization system. Corresponding transition veloc- ity occurs at gas velocities of 0.3, 0.5 and 0.6 m]s for sands with mean diameters of 150, 280 and 490/~m, respectively. DFT, DWT, and RQA could determine frequency range of0-3.125 Hz for macro, 3. ! 25-50 Hz for meso, and 50-200 Hz for micro structures. The RQA showed that the micro structures have the least periodicity and consequently their determinism and laminarity are the lowest. The results show that a combination of DFT, DWT, and RQA can be used as an effective approach to characterize multi-scale flow behavior in gas-solid fluidized beds.

Recurrence quantification analysis on pulse morphological changes in patients with coronary heart disease

OBJECTIVE:To show that the pulse diagnosis used in Traditional Chinese Medicine,combined with nonlinear dynamic analysis,can help identify cardiovascular diseases.METHODS:Recurrence quantification analysis(RQA) was used to study pulse morphological changes in 37 inpatients with coronary heart disease(CHD) and 37 normal subjects(controls).An independent sample t-test detected significant differences in RQA measures of their pulses.A support vector machine(SVM) classified the groups according to their RQA measures.Classic time-domain parameters were used for comparison.RESULTS:RQA measures can be divided into two groups.One group of measures [ecurrence rate(RR),determinism(DEL),average diagonal line length(L),maximum length of diagonal structures(Lmax),Shannon entropy of the frequency distribution of diagonal line lengths(ENTR),laminarity(LAM),average length of vertical structures(TT),maximum length of vertical structures(Vmax)] showed significantly higher values for patients with CHD than for normal subjects(P<0.05).The other measures(RR_std,L_std,Lmax_std,TT_std,Vmax_std) showed significantly lower values for the CHD group than for normal subjects(P<0.05).SVM classification accuracy was higher with RQA measures:With RQA(16 parameters) accuracy was at 88.21%,and with RQA(12 parameters) accuracy was at 84.11%.In contrast,with classic time-domain(15 parameters) accuracy was 75.73%,and with time-domain(7 parameters) accuracy was 74.70%.CONCLUSION:Nonlinear dynamic methods such as RQA can be used to study functional and structural changes in the pulse noninvasively.Pulse signals of individuals with CHD have greater regularity,determinism,and stability than normal subjects,and their pulse morphology displays less variability.RQA can distinguish the CHD pulse from the healthy pulse with an accuracy of 88.21%,thereby providing an early diagnosis of cardiovascular diseases such as CHD.

基于RQA与GG聚类的滚动轴承故障识别

提出递归定量分析与GG聚类相结合的滚动轴承故障识别方法。利用能够表征信号发散程度的RQA参数——确定率和分层率组成轴承故障识别的特征向量,结合GG模糊聚类实现滚动轴承故障模式识别。对实际故障数据进行分析,结果表明,该方法不仅能够识别滚动轴承的不同程度损伤,而且能够实现不同部位的轴承故障诊断。研究结果为滚动轴承故障识别提供了一种高效、直观的新方法。

基于RQA与SVM的声发射信号检测识别方法

针对裂纹声发射信号检测问题,提出基于递归定量分析与支持向量机相结合的新型检测方法。利用小波阈值去噪原理,对采集的声发射信号进行去噪,将递归定量分析引入声发射信号检测,提取递归定量分析的量化特征参数,结合支持向量机对模拟裂纹声发射信号进行识别。并实验验证该方法的可行性。

RQA在肌电分析中的应用

介绍了递归图的生成方法和定量递归分析 (RQA)中递归点的百分数 ,确定性线段的百分数,线段分布香农熵等分析量的意义。应用RQA分析了肱二头肌及肱桡肌在不同负重下的肌电信号 ,发现肱二头肌肌电信号的递归点百分数均比肱肌高,有较强的周期性嵌入。与同一信号所作的FFT谱分析相比较 ,RQA法有较高的区分灵敏性 ,是肌电分析的一种新方法 。

基于微分熵与RQA的电能质量扰动分析

基于非线性混沌和相空间重构理论,将电能质量扰动信号序列重构到高维相空间,进行递归图(RP)分析。采用微分熵法对电能质量信号进行相空间重构,避免分别求取嵌入维数和延迟时间的不一致性;引入递归定量分析(RQA)进行扰动的定量分析,克服传统特征提取方法对过程平稳的严格要求。利用能够表征信号发散程度的RQA参数-确定率(DET)和分层率(LAM)组成电能扰动信号识别的特征向量,根据不同电能质量扰动信号各自的分布情况,来区分不同的电能质量扰动信号。通过对6种电能质量扰动信号进行实验分析,结果表明:该方法不仅能够很直观地识别电能质量扰动信号,还能利用RQA的特征量对信号进行具体的定量分析,为电能质量扰动分析提供了高效、直观的方法。

基于VMD-RQA的直线振动筛激振力不平衡故障诊断

针对直线振动筛早期激振力不平衡故障难以诊断问题,提出了一种基于变分模态分解(VMD)和递归量化分析(RQA)的故障诊断方法。对振动信号进行VMD分解,将直线振动筛基频信号分离,并得到被淹没的各阶高频分量;绘制不同信号分量的动力特性递归图,计算递归图的量化指标,组成故障信号的非线性、非平稳性评价特征向量,将高维特征向量输入机器学习分类器中进行识别诊断,并与传统的特征提取方法比较。试验结果表明:在直线振动筛激振力不平衡故障现场,该方法所提取的特征参数具有最高识别精度,综合识别率为99.13%;且应用于旋转机械滚动轴承实例数据,综合识别率为99.38%,说明该方法具有一定的通用性和工程应用价值。

基于CRP和RQA的变压器绕组压紧状态检测

为了掌握绕组压紧力状况,通过分析变压器表面振动变化情况,提出了基于交叉递归图(Cross Recurrence Plot,CRP)和递归定量分析(Recurrence Quantification Analysis,RQA)的绕组压紧状态检测方法。首先,从振动信号的递归特性出发,对多元和一元振动信号进行相空间重构。然后,分别采用CRP和RQA对相轨迹进行定性和定量分析,据此对绕组压紧状态进行检测。实验数据的分析结果表明,CRP中对角线结构的变化能定性反映出绕组压紧状态的变化,多元与一元振动信号的RQA度量能够分别从整体和局部角度对绕组压紧状态进行定量检测。研究结果为从非线性动力学角度监测绕组松动故障提供了理论依据。

基于RQA和V-VPMCD的滚动轴承故障识别方法

多变量预测模型模式识别(variable predictive model based class discriminate,简称VPMCD)利用样本特征值内在的相关性来建立特征学习模型,但是当训练样本较少时会导致模型预测不准确,因此提出了基于递归定量分析(recurrence quantification analysis,简称RQA)和投票法多变量预测模型模式识别(voted variable predictive model based class discriminate,简称V-VPMCD)的故障识别方法。该方法利用了递归定量分析对非线性、非平稳信号分析的鲁棒性和样本质量不高时处理的优势,以VPMCD作为分类方法,并用投票法优化了VPMCD方法,提升了算法的稳定性和识别率。对滚动轴承不同程度、不同类型故障的模式识别实验表明,该优化算法具有较高的识别准确率和稳定性。

用RQA法分析不同信噪比下的非线性系统

以 L orenz系统为例 ,运用重现图形和重现定量分析法分析了不同信噪比下噪声对连续系统混沌区非线性特征量的影响 .选取自相关函数第一次降到 C(0 ) / 2时所对应的 t作为延迟时间τ,运用错误近邻分析法确定序列的嵌入维 m,以重构相空间 .结果表明 :当信噪比大于 5时 ,序列存在的确定性规律 RPA仍能显示出来 .随着信噪比的降低 ,RQA的参量 :% recur,% determ ,ratio,entropy和 trends的绝对值呈下降的趋势 .当 SNR≥ 10 0 0时 。

基于定量递归分析(RQA)方法的肌电信号处理

简要介绍了定量递归分析(RecurrenceQuantificationAnalysis,RQA)方法在表面肌电信号(surfaceelectromyographic,EMG)分析中的应用进展情况。应用RQA方法对不同位置的肌电信号进行自发受力分析,得出随着力量的增加,确定性值也相应增大的结论,并且与传统的频域分析变量中值频率进行比较,得出了确定性值与中值频率的关系。对电刺激诱发表面肌电信号进行RQA分析,得出了随着刺激强度的增加,RQA参数%REC、%DET、ENTR、Trend都相应增大的结论。

基于JRP和RQA的电抗器绕组故障诊断方法

针对电抗器振动信号的非线性特征,提出了一种基于联合递归图和递归量化分析的信号处理方法,以提取电抗器振动信号的特征并进行故障诊断。

基于RQA方法的心肺系统非线性耦合测量

目的 :探讨重现量化分析 (RQA)方法评测心肺系统非线性耦合中的可行性。方法 :健康家兔 17只 ,采用 RQA方法 ,分析了 17只家兔在注射异丙肾上腺素前后呼吸 -心率和呼吸 -血压的耦合关系的变化。结果 :注射异丙肾上腺素后 ,重现百分比、决定性百分比和重现熵都发生了显著的变化 (与注射前比 ,均 P<0 .0 5 )。结论 :RQA方法可用于分析心肺系统的非线性耦合。

基于总变差降噪与RQA的单向阀故障诊断

针对单向阀振动信号易被噪声淹没和故障表征不明显的问题,提出了一种基于总变差降噪(TVD)和递归定量分析(RQA)的单向阀故障诊断方法。首先利用总变差降噪方法对振动信号进行降噪,提高信号的信噪比;然后对降噪后的信号绘制递归图,通过递归定量分析方法提取递归图中的非线性特征参数,并对所提取特征参数进行敏感度分析,找出敏感度较高的特征参数构成特征向量;最后将得到的特征向量输入加权K近邻分类器(WKNN)完成单向阀故障类型的识别。实验结果表明,该方法在降低背景噪声、表征故障信息和保证故障诊断准确性方面具有明显的效果,具有一定的工程应用价值。

基于CRP和RQA的高压并联电抗器振动信号分析

并联电抗器油箱表面的振动信号与电抗器绕组和铁心的状态密切相关。因此,可以通过振动信号来监测并联电抗器绕组和铁心状态。本文针对电抗器振动信号的非线性特征,提出了一种基于交叉递归图(CRP)与递归量化分析(RQA)相结合的信号处理方法,对铁心饼上表面振动信号与油箱表面多个测点信号的相关性进行研究。实验结果表明,该方法不仅能定性描述电抗器不同测点信号的相关性,还可利用提取的RQA参量对递归现象定量表述。研究结果从非线性动力学角度为高压并联电抗器振动敏感区域确定及振动测点选取提供了依据。

基于脑部电阻抗信息的非线性动力学脑卒中诊断方法

缺血性脑卒中因其较高的发病率和致残率严重威胁人们的健康.作为缺血性脑卒中病情的诊断手段之一,电阻抗层析成像(Electrical Impedance Tomography,EIT)具有便携、无创、连续监测、低成本的优点.然而,现有EIT研究较多关注单帧检测信息条件下病态逆问题的求解,缺乏对时间序列数据信息的深入分析.提出一种基于脑部电阻抗信息的非线性动力学脑卒中诊断方法,通过EIT连续监测假手术组大鼠和右侧大脑中动脉栓塞模型组大鼠脑部,获取其阻抗变化数据,并采用递归图和递归定量分析等非线性动力学方法对监测信息的非线性特征进行提取,进而获得电阻抗信息与脑卒中病情的内在关联.实验结果表明,所提方法可为缺血性脑卒中的诊断提供依据.

弧齿锥齿轮系统的非线性动力学特征分析

为了揭示弧齿锥齿轮系统在不同啮合频率、刚度系数和阻尼系数下的非线性动力学特征,建立了弧齿锥齿轮系统数学模型,计算其在不同参数下的时间序列,并利用相空间重构、递归图、递归量化分析等方法,分析系统在不同工况下的动力学特征,从而揭示弧齿锥齿轮系统的非线性动力学规律。结果表明,在较高刚度系数、较高阻尼系数且远离特定啮合频率的工况时,弧齿锥齿轮系统重构相空间的吸引子结构和递归图结构较简单,此时相图多为单一闭合曲线,递归图主要呈现与主对角线平行的斜直线,捕获时间和熵均较小,此时系统运动状况较简单,运动平稳性较强;而在相反的工况下,相空间的吸引子结构和递归图结构复杂,系统运动状况较复杂,呈现出混沌特性。