基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别

为了提高动作表面肌电信号的识别率,提出一种将最大李雅普诺夫指数和多尺度分析结合的方法。从非线性和非平稳的角度出发,引入多尺度最大李雅普诺夫指数特征,并应用到人体前臂6类动作表面肌电信号的模式识别中。首先利用希尔伯特-黄变换,对原始信号进行经验模态分解,即多尺度分解;然后利用非线性时间序列分析方法,计算多尺度最大李雅普诺夫指数;最后将多尺度最大李雅普诺夫指数作为特征向量,输入支持向量机进行识别。平均识别率达到97.5%,比利用原始信号的最大李雅普诺夫指数进行识别时提高了3.9%。结果表明,利用多尺度最大李雅普诺夫指数对动作表面肌电信号进行模式识别效果良好。     

心率信号的混沌分析

非线性动力学研究方法之一的混沌分析方法在许多科学领域都得到了应用，研究表明，心率信号是混沌信号，本文介绍了判断信号混沌特征的方法，如相平面图，延时图，功率谱等菜法；李雅普诺夫指数，分维数，混沌度等数值法。介绍了心率信号混沌分析在心血管疾病的诊断预测及植物神经系统功能测试方面的应用，最后还介绍了作者的部分工作。     

冠心患者与健康人同步12导联ECG信号的李雅普诺夫指数谱比较研究

本研究首次计算了60名具有窦性心率的冠心患者(Coronary atrery disease:CAD)和60名健康老年人的同步12导联心电图信号的李雅普诺夫指数谱.发现对同一个人,从不同导联得出的Lyapunov指数是不同的,具有明显的空间分布特性.所有导联的ECG信号的最大Lyapunov指数L1均为正数,其余指数为负,心电信号表现出明显的混沌特征.同一导联相比较,冠心患者的最大Lyapunov指数L1低于健康正常人的最大Lyapunov指数L1,提示在心肌缺血的情况下,心电信号的混沌程度下降了,重构相空间中ECG信号的奇异吸引子的动力学复杂性降低了.结果表明,在估算Lyapunov指数时,有必要指明导联的位置.在Lyapunov指数谱中,最大Lyapunov指数可以将冠心患者与健康正常人区分开来,在心脏疾病诊断中具有潜在的应用价值.     

正常人心动周期信号的混沌特征随年龄的变化

心动周期信号（ＨＰＳ）具有混沌特征，含有重要的生理、病理信息。我们利用自行研制的计算机化心动周期信号混沌分析系统，研究了２７１例正常人心动周期信号的混沌特征参数如相对分散度（ＨＲＤ）、李雅普诺夫数（ＨＬＥ）和分维数（ＨＦＤ），发现随着年龄的增加，ＨＲＤ、ＨＬＥ、ＨＦＤ逐步降低，即心血管动力学的复杂性逐步降低     

基于最大Lyapunov指数的心音信号混沌特性分析

目的寻求无创伤的且能自适应信号变化的方法区分正常和异常的心音信号,为临床诊断提供更简捷的参考方法。方法本文以心音信号非线性时间序列最大Lyapunov指数为主线,根据心音信号不同阶段特性的统一性,提出了对信号分阶段进行研究的方法。首先对7种具有代表性的正常和异常心音信号的S1、S2心音分别分3阶段进行相空间重构,然后结合各阶段求得的相空间重构参数计算对应的最大Lyapunov指数,最后对正常、异常心音信号最大Lyapunov指数均值进行比较分析。结果正常S1心音信号的最大Lyapunov指数均值0．1450,远大于异常S1心音信号,正常S2心音信号的最大Lyapunov指数均值也比异常s2心音信号大很多。结论心音信号中确实存在混沌现象,且正常（健康）心脏运动到S1和S2阶段的混沌程度要比异常（病态）时高。     

孕大鼠和胎鼠心脏系统混沌特征分析

应用计算机化大鼠心电信号采集和处理系统,引导妊娠第21dSD大鼠和胚胎宫内心电,记录母胎鼠心动周期信号混沌图形参数和数值参数,探讨正常妊娠大鼠母胎心脏系统混沌特征,分析母胎鼠自主神经系统发育的差异性。结果显示,(1)正常胎鼠与孕鼠比较,频率和电压较低,差异均有显著性(P<0.01);(2)正常胎鼠和孕鼠心动周期信号功率谱具有类似于人的心动周期信号功率谱三峰特征;正常胎鼠和孕鼠比较,第二峰、第三峰明显较低;(3)正常胎鼠与孕鼠比较,相对分散度、李雅普诺夫指数和分维数均显著较低,差异均有显著性(P<0.01)。本研究表明,正常胎鼠心动周期信号变化的复杂性低于孕鼠,胎鼠心脏混沌程度低于孕鼠,胎鼠自主神经系统发育尚不成熟。     

基于非线性混沌分析的急性低血压预测

重症监护室中急性低血压发生的预测研究是临床医学的重点与难点。本文应用非线性混沌分析方法,对MIMICⅡ临床记录中患者的平均动脉压时间序列信号进行分析,构建患者的李雅普诺夫指数变化曲线。研究发现患者在急性低血压症状发生前一般会出现明显的指数曲线突变情况,这为急性低血压的有效预测提供了直观的依据,为急性低血压的理论研究与应用提供了一条可参考的思路。     

基于HHT边际谱熵和能量谱熵的心率变异信号的分析方法

基于希尔伯特-黄变换(HHT)理论,依据广义信息熵的概念,提出基于HHT边际谱熵和能量谱熵的概念和熵分析方法。对常规信号和混沌时间序列信号进行复杂性研究,结果表明本方法在刻画信号复杂度变化、抗脉冲干扰方面优于Lempel-Ziv复杂度和功率谱熵方法。将其应用于MIT-BIH标准数据库的实际心率变异(HRV)信号分析,结果显示HHT边际谱熵和能量谱熵能从HRV信号中敏感地检测出生理和病理状态的变化,统计学分析优于传统的功率谱熵方法,为临床HRV信号及其他复杂生理信号的分析提供一种有效的分析方法。     

基于复杂度的心率变异性分析研究进展

一、引言 心率变异性（heart rate variability,HRV）分析是一种敏感的无创伤性的评价心脏自主神经系统（autonomic nervous system,ANS）功能的定量方法。以往HRV分析多用标准差、直方图及频谱法等线性分析方法,虽可反映总体的心率变异度,但掩盖了瞬时心搏变化。1984年Ritzenberg等从狗的心电图中首次发现心脏搏动具有混沌现象,此后,许多研究者,对这方面进行了研究HRV信号被普遍认为是混沌或含有混沌成分的信号,     

基于多尺度时间不可逆的窦性和房性心律失常分析

时间不可逆性是非平衡系统的一个基本性质,量度心电(ECG)信号在不同心脏生理病理状态下的时间不可逆指数的变化趋势具有重要的意义。本文利用多尺度时间不可逆方法,对MIT-BIH标准数据库中的正常窦性心律、房性期前收缩(也称为房性早搏)和窦性心动过缓的心率变异(HRV)信号进行了分析和检测。研究表明,正常窦性心律、房性早搏和窦性心动过缓的不可逆指数呈现下降趋势,该结果对辅助临床诊断具有提示作用。     

Nonlinear analysis of heart rate variability of healthy subjects and patients with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is one of the causes of mortality worldwide with an increasing prevalence. Heart rate variability (HRV) reflects the regulation mechanism of the cardiac activity by the autonomic nervous system. The assessment of HRV by using nonlinear methods is more sensitive for the detection of complexity when compared to linear methods. This study aims to get information about the autonomic dysfunction occurred in patients with COPD by analysing the complexity of HRV. Electrocardiogram signals recorded from healthy subjects, patients with moderate COPD and severe COPD (eight subjects per group) were analysed. The HRV signals were acquired from ECG signals. Signals were reconstructed in the phase space and largest Lyapunov exponent (LLE), correlation dimension, Hurst exponent and approximate entropy (ApEn) values were calculated. It has seen that for the patients with COPD LLE, correlation dimension, Hurst exponent and ApEn values were less than control group. According to this, HRV complexity decreases in the presence of COPD. However, there is no significant difference between COPD groups and the severity of COPD has no effect on the chaoticity of the system. The results revealed that autonomic dysfunction occurred in patients with COPD is associated with reduced HRV complexity.     

Breathing frequency bias in fractal analysis of heart rate variability

Detrended Fluctuation Analysis (DFA) is an algorithm widely used to determine fractal long-range correlations in physiological signals. Its application to heart rate variability (HRV) has proven useful in distinguishing healthy subjects from patients with cardiovascular disease. In this study we examined the effect of respiratory sinus arrhythmia (RSA) on the performance of DFA applied to HRV. Predictions based on a mathematical model were compared with those obtained from a sample of 14 normal subjects at three breathing frequencies: 0.1 Hz, 0.2 Hz and 0.25 Hz. Results revealed that: (1) the periodical properties of RSA produce a change of the correlation exponent in HRV at a scale corresponding to the respiratory period, (2) the short-term DFA exponent is significantly reduced when breathing frequency rises from 0.1 Hz to 0.2 Hz. These findings raise important methodological questions regarding the application of fractal measures to short-term HRV.     

Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures

Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.     

Transtibial Amputee Joint Motion has Increased Attractor Divergence During Walking Compared to Non-Amputee Gait

The amputation and subsequent prosthetic rehabilitation of a lower leg affects gait. Dynamical systems theory would predict the use of a prosthetic device should alter the functional attractor dynamics to which the system self-organizes. Therefore, the purpose of this study was to compare the largest Lyapunov exponent (a nonlinear tool for assessing attractor dynamics) for amputee gait compared to healthy non-amputee individuals. Fourteen unilateral, transtibial amputees and fourteen healthy, non-amputee individuals ambulated on a treadmill at preferred, self-selected walking speed. Our results showed that the sound hip (p = 0.013), sound knee (p = 0.05), and prosthetic ankle (p = 0.023) have significantly greater largest Lyapunov exponents than healthy non-amputees. Furthermore, the prosthetic ankle has a significantly greater (p = 0.0.17) largest Lyapunov exponent than the sound leg ankle. These findings indicate attractor states for amputee gait with increased divergence. The increased attractor divergence seems to coincide with decreased ability for motor control between the natural rhythms of the individual and those of the prosthetic device. Future work should consider the impact of different prostheses and rehabilitation on the attractor dynamics.     

A new algorithm developed based on a mixture of spectral and nonlinear techniques for the analysis of heart rate variability

In this paper, an algorithm based on a joint use of spectral and nonlinear techniques for heart rate variability (HRV) analysis is proposed. First, the measured RR data are passed into a trimmed moving average (TMA)-based filtering system to generate a lower frequency (LF) time series and a higher frequency (HF) one that approximately reflect the sympathetic and vagal activities, respectively. Since the Lyapunov exponent can be used to characterize the level of chaos in complex physiological systems, the largest Lyapunov exponents corresponding to the complex sympathetic and vagal systems are then estimated from the LF and HF time series, respectively, using an existing algorithm. Numerical results of a postural maneuver experiment indicate that both characteristic exponents or their combinations might serve as a set of innovative and robust indicators for HRV analysis, even under the contamination of sparse impulses due to aberrant beats in the RR data.     

Spontaneous contractions of isolated rat portal vein under temperature perturbations

We studied nonlinear dynamics underlying spontaneous rhythmical contractions of isolated rat portal vein. The signals were acquired at four different temperatures important in isolated blood vessels preparations: 4, 22, 37 and 40°C. To characterize the system’s nonlinearity, we calculated the largest Lyapunov exponent, sample entropy and scaling exponents. Evidence for nonlinearity was provided by analysis of surrogate data generated from the phase-randomized Fourier transform of the original sequences. Positive values of the largest Lyapunov exponent were obtained for the time series recorded under applied conditions, indicating that the system preserves its chaotic deterministic nature even far from the physiological temperature range. Scaling exponents revealed three distinctive regions with different correlation properties. The calculated measures that characterize the time series obtained at 4°C were significantly different from those derived from data obtained at higher temperatures. System’s dynamics becomes more complex or less predictable as temperature approaches physiological value. The computation of the largest Lyapunov exponent, sample entropy and correlation measures gave an insight into the complex dynamics of the isolated blood vessels rhythmicity. We identified different modes of rhythmical contractions of isolated rat portal vein which could improve understanding of possible control mechanisms in vivo.     

冠心病患者心率变异性分析

目的研究冠心病患者心率变异（HRV）的变化规律及临床意义。方法选择50例无心律失常冠心病患者（冠心病组）、30例伴心律失常冠心病患者（心率失常组）与52例正常成人自愿者（正常组）进行24h动态心电图HRV指标比较研究。结果与正常组比较,冠心病患者SDNN、SDANN、RMSSD、PNN50和HF指标均降低,LF指标升高,具有显著差异。伴心律失常与无心律失常冠心病患者比较,HRV指标异常变化趋于恶化。结论冠心病患者心脏自主神经调节功能受到损害,迷走神经活性减弱,交感神经活动占优势。     

A new algorithm developed based on a mixture of spectral and nonlinear techniques for the analysis of heart rate variability

In this paper, an algorithm based on a joint use of spectral and nonlinear techniques for heart rate variability (HRV) analysis is proposed. First, the measured RR data are passed into a trimmed moving average (TMA)-based filtering system to generate a lower frequency (LF) time series and a higher frequency (HF) one that approximately reflect the sympathetic and vagal activities, respectively. Since the Lyapunov exponent can be used to characterize the level of chaos in complex physiological systems, the largest Lyapunov exponents corresponding to the complex sympathetic and vagal systems are then estimated from the LF and HF time series, respectively, using an existing algorithm. Numerical results of a postural maneuver experiment indicate that both characteristic exponents or their combinations might serve as a set of innovative and robust indicators for HRV analysis, even under the contamination of sparse impulses due to aberrant beats in the RR data.     

HRV信号关联维计算中参数选取的研究

健康和有疾病的心率变异性（HRV）参数有明显差异,计算关联维是识别这种差异的一种重要手段。用传统的G—P算法计算关联维时,嵌入维数m、延迟时间τ、及序列长度N等参数的选取会对最终计算结果有很大影响。本文从理论和实验方面论述了如何选取这些参数以获得正确的结果,并且将其应用于正常组和心率不齐疾病组进行对照,结果显示关联维可以有效地表征由于疾病对于心脏节律造成的影响。