基于高阶复杂性测度的心率变异信号分析

心率变异性反映了交感神经和迷走神经对心血管系统的综合调节作用,是评价心血管系统功能的重要指标。复杂度是描述时间信号序列信息量的一个重要参数,传统算法中的过分粗略化会丢失大量的有用信息,而高阶复杂度的引入可较大程度地避免这一问题。在利用Lorenz模型数据仿真的基础上,分别对25例正常人样本和25例充血性心力衰竭病人样本的心率变异信号的1～15阶Kolmogorov复杂度进行了计算,通过对比分析后确定,5阶Kolmogorov复杂度在临床医学上可以为分析心率变异信号获得更为理想的效果。     

子宫收缩对胎儿心率变异性非线性的影响

目的对不同子宫收缩状态下,分析胎儿心率变异性的非线性混沌强度。方法采用非线性滴定方法,计算胎儿心率变异性的噪声极限,即非线性混沌强度;另用近似熵方法计算宫缩状态下胎儿心率变异性的复杂性。结果统计结果表明宫缩频率越高,计算所得到的噪声极限越大。说明随着外加刺激的增强,胎儿心率变异性的非线性混沌强度是增大的。但是近似熵方法不能反映相似的结果,宫缩反而使胎儿心率变异性的复杂性降低。     

基于多尺度的小鼠ECG信号复杂性与特征频率研究

已有的生理信号非线性分析仅研究了信号在单一采样频率下复杂度的差异.分析认为,可通过一个频率尺度因子寻找与生命活动密切相关的多重分形特性谱参数,该参数对生理、病理活动状态具有敏感性.通过小鼠药物实验模拟不同的生理和病理条件,研究并分析了健康小鼠与不同加药组小鼠心电图（electrocardiogram,ECG）信号的质量指数谱曲率随尺度因子分布,确定该信号复杂度最强、同时对疾病最敏感的特征频率范围,并得出心跳频率、心跳动力学复杂度以及ECG信号最敏感频率范围的内在联系.结果表明,在某一尺度因子范围内,小鼠ECG信号的质量指数谱曲率绝对值最大,并且这个最大值所在的尺度因子（或频率）范围不随计算的数据长度和最大粗粒化尺度因子的变化而改变.小鼠心率与其心跳动力学非线性复杂度之间并无直接联系,只与能够表达该动力学复杂度的ECG信号最敏感的频率范围有关.与心跳动力学复杂度有直接联系的是心脏健康状况,这两者在一定尺度因子范围内正相关.随着小鼠心跳频率升高,该ECG信号的敏感频率范围段也随之向高端移动.     

呼吸模式对心血管变异性非线性特性的影响

目的研究不同呼吸模式对心血管调节系统的影响。方法对16名健康的大学生采集心电、血压和呼吸信号,采用频谱分析方法和基于Volterra—Wiener级数的非线性方法分析自主呼吸、控制呼吸和屏气对心血管调节的影响。结果与自主呼吸模式相比,10次／分钟的控制呼吸使心率变异性的非线性特性定性和定量上均显著降低,而自主呼吸节律的控制呼吸的非线性特性定性上无显著差异,但定量上仍然显著降低。这些结果提示自主呼吸是保持心肺耦合的最优呼吸模式。     

心率变异性信号的分析处理:现状与展望

一、概述 从1903年Einthoven第一次记录下人体的心电图至今已近一百年。这期间心电信号的分析处理取得了长足进展,已由对常规心电图的自动分析发展到体表多导心电图的描记及其逆向求解以及心电图中微小电位变化(如希氏束图、晚电位、高频变化等)的分析。近年来心率变异性Heart Rate Variability(HRV)成为心电信号处理中又一个前沿热点。 HRV是指连续心跳间瞬时心率的微小涨落或逐拍心跳R-R间期的微小涨落。人们发现,健康人的心率即使在静息状态下也非恒定不变,而是有起伏的。心率变异过小反而     

心血管变异性的中枢调节数学模型

通过建立心血管变异性的数学模型,讨论心血管中枢对心血管调节的作用,血液血动力学公式、心交感和心迷走对心率的控制,压力感受器反射以及心血管中枢的活动性构成闭环的拍－拍心血管变异性数学模型。获得如下结果;模型仿真了,1）心血管变异性的三个主要的频率成分;2）传出神经活动也具有与心血管变异性相同的频谱特性;3）压力反射的S形曲线及其受心血管中枢的影响;4）心血管变异的昼夜节律现象。本模型成功地仿真了心血管变异性的主要特征,尤其提示了心血管中枢的活动对心血管变异性和压力反射敏感性有极大的影响。     

心率变异性与冠心病、年龄的关系-附170例临床样本观察

目的:探讨心率变异性(HRV)与年龄的关系及冠心病患者心率变异性(HRV)各时域指标的差异变化.方法:采用24小时动态心电图监测,对经冠状动脉造影检查明确诊断的133例冠心病患者和37例非冠心病患者进行HRV时域分析(SDNN,SDANN,RMSSD,pNN50,SDNNindex),并将结果做以对比,分析关系.结果:冠心病患者HRV时域指标(SDANN,RMSSD,pNN50)低于非冠心病组,具有统计学意义(P＜0.05),且在非冠心病组冠心病患者组中,老年组HRV低于中年组,具有统计学意义(P＜0.05).结论:冠心病血管病变与迷走神经功能能受损相关,且随着年龄的增长,迷走神经功能下降.     

有氧锻炼对心脏自主神经调节的影响：心率变异性分析结果

为阐明有氧锻炼对心脏自主神经调节功能的影响,以及这种变化与卧位／坐位下体负压（ＬＢＮＰ）作用下的心率调节及立位耐力之间是否有一定联系,用频域、动态谱及非线性指标较全面分析了大学生有氧锻炼６个月前、后心率变异性（ＨＲＶ）的变化。结果表明：常规ＡＲ谱分析的批处理结果只能代表一段时间内ＨＲＶ信号的平均统计特性,方差大,得不出有显著意义的结果。而时变ＡＲ谱则可反映ＬＢＮＰ作用下心迷走撤除及交感激活的动态过程。非线性的β估计得不出有显著意义的结果;但ＡｐＥｎ分析则可敏感地检测出有氧锻炼关联的心率动力学细微变化,且初步揭示ΔＡｐＥｎ与立位耐力变化（ΔＤＮＰ）间显著相关。以上对阐明有氧锻炼对心率动力学调节的影响以及改进ＨＲＶ信号分析工作均有一定意义。     

非线性动力学在脑电信号分析中的应用

EEG是由大脑产生的非线性时间序列,体现出混沌行为。近年来迅速发展的非线性动力学理论为脑电信号分析开创了一个新的领域。本文综述了近年来非线性动力学在脑电信号研究中（睡眠阶段,麻醉深度,认知过程,精神分裂,痴呆及癫痫）的进展,以期对脑神经动力学有更好的理解。     

高血压病不同级别心功不全的自主神经功能变化及相关性研究

目的通过动态心电监护下心率变异性(HRV)分析自主神经功能对高血压病不同级别心功能不全的HRV变化。方法应用动态心电图和无创性心功能测定高血压病不同级别心功能不全的心率变异性的结果进行比较。结果发现高血压病不同级别心功能不全的患者随心功能损害的加重心率变异时域参数SDNN、SDRNN、RMSSD、PNN50和频域参数TP、VLF、LF、HF、LF/HF均明显降低,且有统计学差异。结论自主神经功能损害在高血压病不同级别的心功能不全患者起着重要的作用。     

Aging and the complexity of cardiovascular dynamics.

Biomedical signals often vary in a complex and irregular manner. Analysis of variability in such signals generally does not address directly their complexity, and so may miss potentially useful information. We analyze the complexity of heart rate and beat-to-beat blood pressure using two methods motivated by nonlinear dynamics (chaos theory). A comparison of a group of healthy elderly subjects with healthy young adults indicates that the complexity of cardiovascular dynamics is reduced with aging. This suggests that complexity of variability may be a useful physiological marker.     

Analysis of the nonlinear heart rate dynamics by two-contour mathematical model

Using a two-contour mathematical model, changes in the degree of heart rate variability induced by an increased extracardial impulsation in the sinoatrial node have been studied. The model is based on quantitative characteristics of impulse conduction in the cardiac conduction system. A mathematical and computer modeling revealed the following three regimes of heart rate variability: linear dynamics, the 1st-degree chaos, and the 2nd-degree chaos. Transitions between these regimes have been studied. A comparative analysis of the one- and two-contour models of heart rate regulation has been performed.     

Age dependency and correlation of heart rate variability,blood pressure variability and baroreflex sensitivity

Simultaneous analysis of heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) with different types of measures may provide non-duplicative information about autonomic cardiovascular regulation. Therefore, a multiple signal analysis of cardiovascular time series will enhance the physiological understanding of neuro cardiovascular regulation with deconditioning in bedrest or related gravitational physiological studies. It has been shown that age is an important determinant of HRV and BRS in healthy subjects. Whereas in the case of BPV, the effect of aging seems to depend upon the activity status of the subjects. In view of the facts that most of the previous works were dealing with only the variability of one kind of cardiovascular parameters in one study with conventional time-domain and/or frequency-domain analysis, we therefore designed the present work to compare the HRV, BPV and BRS between young and middle-aged male healthy subjects in one study with the same subjects using various techniques, including the approximate entropy (ApEn) measurement, a statistic quantifying HRV "complexity" derived from non-linear dynamics.     

Estimation of Instantaneous Complex Dynamics through Lyapunov Exponents: A Study on Heartbeat Dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the Hénon map and Rössler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations.     

Analysis of stability of different regimes of heart rate dynamics by computer modeling

Computer modeling revealed the following three regimes of heart rate dynamics: linear dynamics, “1st degree chaos,” and “2nd degree chaos.” This study investigated a stability of these regimes with respect to changes in initial conditions. The results show that the greatest stability is notable for the linear regime. For this regime small errors in values of initial conditions can not sharply change the initial dynamics of RR intervals. Both nonlinear regimes of heart rate dynamics are unstable, and a degree of instability of regime “2nd degree chaos” is higher in comparison with regime “1st degree chaos.” The results of computer modeling are in agreement with experimental data pointing to the existence of a relationship between the degree of heart rate irregularity and cardiac electrical stability.     

Autonomic cardiac control in animal models of cardiovascular diseases. I. Methods of variability analysis.

Analysis of heart rate variability (HRV) and blood pressure variability (BPV) and baroreceptor sensitivity (BRS) has become a proven tool in clinical cardiovascular diagnostics and risk stratification. In the present work, traditional and new methodological approaches for analysis of HRV, BPV, and BRS data are summarized. HRV, BPV, and BRS parameters were obtained from animal studies designed to study pathogenetic mechanisms of distinct cardiovascular diseases. Different non-linear approaches for HRV and BPV analysis are presented here, in particular measures of complexity based on symbolic dynamics. The dual sequence method (DSM) was employed for BRS analysis. In comparison to the classical measure of BRS using the average slope [ms/mm Hg], DSM offers additional information about the time-variant coupling between BPV and HRV. Since cardiovascular regulation shares common features among different species, data on HRV and BPV, as well as BRS, in animal models might be useful for understanding the pathogenetic mechanisms of cardiovascular diseases in humans and in the development of new diagnostic approaches.     

健康人心率变异性中的不稳定周期轨道

为刻划心脏节律存在的确定性动力学特征,运用不稳定周期轨道分析方法对健康青年人的RR间期时间序列数据进行分析。研究结果揭示健康人心脏节律中存在显著的不稳定周期轨道及不稳定周期轨道分级（周期1、周期2,周期3,周期4）现象,表明健康青年人心脏节律的动力学特性中包含着显著的确定性行为。通过跟踪不周期轨道随时间的演变,迹表明心脏节律的变化中存在着因有的非平稳性。     

Nonlinear dynamics of heart rate variability during experimental hemorrhage in ketamine-anesthetized rats

Indexes of heart rate variability (HRV) based on linear stochastic models are independent risk factors for arrhythmic death (AD). An index based on a nonlinear deterministic model, a reduction in the point correlation dimension (PD2i), has been shown in both animal and human studies to have a higher sensitivity and specificity for predicting AD. Dimensional reduction subsequent to transient ischemia was examined previously in a simple model system, the intrinsic nervous system of the isolated rabbit heart. The present study presents a new model system in which the higher cerebral centers are blocked chemically (ketamine inhibition of N-methyl-D-aspartate receptors) and the system is perturbed over a longer 15-min interval by continuous hemorrhage. The hypothesis tested was that dimensional reduction would again be evoked, but in association with a more complex relationship between the system variables. The hypothesis was supported, and we interpret the greater response complexity to result from the larger autonomic superstructure attached to the heart. The complexities observed in the nonlinear heartbeat dynamics constitute a new genre of autonomic response, one clearly distinct from a hardwired reflex or a cerebrally determined defensive reaction.     

Dynamics extraction in multivariate biomedical time series

A nonlinear analysis of the underlying dynamics of a biomedical time series is proposed by means of a multi-dimensional testing of nonlinear Markovian hypotheses in the observed time series. The observed dynamics of the original N-dimensional biomedical time series is tested against a hierarchy of null hypotheses corresponding to N-dimensional nonlinear Markov processes of increasing order, whose conditional probability densities are estimated using neural networks. For each of the N time series, a measure based on higher order cumulants quantifies the independence between the past of the N-dimensional time series, and its value r steps ahead. This cumulant-based measure is used as a discriminating statistic for testing the null hypotheses. Experiments performed on artificial and real world examples, including autoregressive models, noisy chaos, and nonchaotic nonlinear processes, show the effectiveness of the proposed approach in modeling multivariate systems, predicting multidimensional time series, and characterizing the structure of biological systems. Electroencephalogram (EEG) time series and heart rate variability trends are tested as biomedical signal examples. Received: 2 July 1997 / Accepted in revised form: 26 March 1998