短时非线性方法用于心率变异性分析

非线性分析为揭示心率变异性(HRV)的生理和病理变化提供了新的视角,不仅可反映更多关于心脏自主神经对于心率动力学调节的信息,而且与传统的时域和频域分析形成互补。特别是短时非线性分析契合了心率动力学的非线性和非平稳特征。文中就符号动力学、去趋势波动分析中的短时分形尺度指数α1,与复杂度相关的近似熵和样本熵,以及定量递归分析等方法,分别介绍了它们在短时RR间期序列分析中的应用。同时从短时非线性分析与长时序列结合,以及基于多分析域指标的判别分析这两方面,说明了短时非线性方法在HRV分析中的应用拓展。最后,就这些方法应用于临床需要解决的问题进行了阐述。     

基于联合熵的室性心动过速与室颤识别

对心电信号进行分析,是医学临床上检测和诊断心脏功能的重要手段。室性心动过速(VT)和心室纤颤(VF)是威胁人类生命的严重的心脏疾病。本文应用联合熵方法分析正常心跳信号(NSR)、VT和VF信号的动力学复杂性信息。将动力学符号统计理论以及替代数据的概念融入其中,通过计算原始时间序列和其替代时间序列之间的联合熵值,来量化序列的动力学复杂性。经过对实际心率数据的计算机分析,证明了联合熵方法的合理性。根据联合熵值的不同对NSR、VT和VF信号进行区分,取得了令人满意的效果。     

心率变异信号的非均匀采样问题研究

心率变异性研究是目前的一个研究热点,心率变异性研究的主要对象-R-R间期时间序列是一个非均匀采样序列,这种非均匀性导致了许多问题,本文提出了一种基于三次样条的序列均匀重采样算法。并构造了一种新的更直观的R-R间期信号模型。实验结果表明新算法确实可以改善谱估计的结果和波形的形态,消除二次伪谐波峰,并在大多数情况下减少偏差。更重要的是,该算法使得R-R间期序列的物理意义更明确,从而使不同信号间的比较和运算成为可能。     

基于短时心率变异性信号两种熵测度的过速型室性心律失常的预测分析

目的:过速型室性心律失常［持续性室性心动过速或心室纤颤（VT/VF）］是心脏猝死的主要诱因,测试VT/VF发生前心率变异性信号是否有明显改变可作为VT和VF发生的提前预报信号。方法:以78名患者体内心脏复律除颤器记录的VT/VF事件发生前心率变异性信号（VT/VF序列）和来自同一患者的正常窦性节律（CON序列）组成的135个样本对作为实验序列。通过预处理消除实验序列的伪差、异位心搏等干扰,采用两种基于熵的非线性复杂度测度——样本熵和逐点多尺度熵（PPMSE）,分析VT和VF发生前十几分钟的VT/VF序列,以及心率增加和减小的VT/VF序列复杂性,并采用PPMSE方法讨论了接近VT/VF发生时VT/VF序列复杂性变化。结果:与正常对照组CON序列相比,在一定匹配容差内,VT/VF发生前心率变异性信号的样本熵明显减小（r<0.25×SD, P<0.000 5）,心率增加的VT/VF序列减小更显著（r<0.3×SD, P<0.000 1）;VT/VF序列的PPMSE在越接近VT/VF发生时刻减小越显著,提取的CI指数存在显著差异（如1~30尺度,N=986、500、250时,P=1.5×10-2、P=4.3×10-3、P=1.3×10-5）,心率增加的VT/VF序列区分性能更好。结论:过速型心律失常的自然发作并不是突发现象,在其发作前或许存在某种生理预兆,两种熵测度可能是短时预报恶性室性心律失常事件的有效非线性参数。     

基于短时窗口熵的VF/VT ECG信息研究

目的：对心室纤颤和室性心动过速ECG信号的信息熵进行分析。材料与方法：VF和VT数据取自www．physionet．org的MIT数据库。利用一个固定大小的时间窗口，对信号序列进行滑动，同时求出各窗口内的熵，从而得到熵的时间演化图。结果：VT信号熵比VF信号和正常的心电信号的熵更规则一些，可预测性更高。结论：短时窗口熵能够敏感地反映信号的局部变化，VF/VT ECG信号可以通过短时窗口熵来识别。     

正常人深呼吸对心率变异性的影响

心率变异作为一种间接评价心脏自主神经功能的分析方法,受到多种因素的影响,呼吸便是其中一个重要因素。文章通过心电图RR间期(RRI)序列,就呼吸对心率变异性的影响进行了时域、频域和非线性动力学分析。统计发现,深呼吸时RR平均间期变小,间期标准差(SDNN)变大,功率谱中低频成分(LF)增加,平衡比(LF／HF)变大,复杂度减小。结果表明,深呼吸可以使交感神经兴奋性增加,加快心跳频率,影响自主神经平衡,而RRI序列复杂度的减小可能与此时呼吸成为自主神经系统的主要调制因素有关。     

青年与老年昼夜心率信号多尺度化的基本尺度熵分析

目的:本研究采用多尺度化的基本尺度熵方法分析青年组与老年组,白天和黑夜的心率变异性信号。 方法:从PhysioBank数据库中,分别提取出青年人和老年人的心率变异性信号,利用多尺度化的基本尺度熵方法对数据进行比较分析。 结果:通过计算心率变异性信号的熵值,发现在白天清醒状态下的熵值比睡眠状态下的熵值高,但是昼夜间变化相似;老年组的多尺度化的基本尺度熵相对于青年组的熵值有所偏离,但大致走势一样,可见老年组的生理状态偏离了青年组的最佳生理状态。 结论:通过熵值的分析,揭示出清醒状态下,心脏系统的自适应性和稳定性较睡眠状态要强;青年与老年的熵值具有昼夜节律相关性;多尺度化的基本尺度熵方法可以用于区别青年与老年,白天与黑夜的心率变异性信号。     

二进制序列在心率变异性分析中的应用及推广

目的：探究二进制序列在心率变异性分析中的应用。方法：提出了一种将RR间期序列转化成二进制序列的方法一阈值法，并利用这种方法分别将年轻组和老年组各20名健康受试者RR间期的原始序列转化成为二进制序列，计算了这两组健康受试者RR间期的原始序列和转化成二进制序列的近似熵与样本熵。结果：通过计算，得到转化后两组二进制序列的近似熵分别为0．5923±0．1071、0．3270±0．2057，样本熵分别为0．5315±0．1528、0．2238±0．1993。对这两个指标分别进行t检验表明：年轻组的近似熵与样本熵均明显大于老年组（P〈0．001，P〈0．001），经与原始序列的t检验结果对照发现，二进制序列的t检验效果更好．结论：经过讨论，本文介绍的经阈值法转化为二进制序列在心率变异性分析中有较明显的优点。     

心率变异性的检测与分析

心率变异性(HRV)是逐次心跳R-R间期之间存在的微小差异,其蕴涵着心血管神经及体液调节的大量信息,心率变异性的检测与分析是近年来心电信号处理领域的研究热点,其对于心血管系统疾病的无创检测具有重要的临床意义。本文介绍了心率变异性的原理、研究现状与临床意义,描述了用于计算心率变异性的各种QRS复合波检测方法,综述了心率变异性的时域分析、频域分析、时频分析和非线性动力学分析方法,并总结展望了今后的研究方向。     

一种基于近似熵特征分类的冠心病诊断方法

对104例冠心病人心电Holter信号进行心率变异性的分析,计算RR间期序列的近似熵指标的24h分段变化趋势图,并于健康对照组作比较,验证了近似熵这个心率变异性非线性参数的意义.通过LDA(线性鉴别分析)的模式识别方法对病人及健康人的24h变化趋势图进行模式识别和分类,平均正确分类率达99.03%.分类的结果表明,冠心病患者与健康人相比在白天,尤其在早上6点到中午12点间,近似熵指标的降低更明显,利用此时间段作分类正确率更高.     

运用排列熵方法分析冥想训练对心率变异性的影响

背景：冥想训练有利于保持身体与心理健康,训练过程中通过自主神经调整训练心率是一个可能的因素。两者之间关系的研究有助于加深对于心血管系统调节以及健康训练的理解。 目的：从复杂性分析的角度考察冥想训练过程中心率调整的规律。 方法：心率变异信号取自PhysioNet中冥想训练心率数据库,应用排列熵方法对中国气功以及瑜伽训练前和训练中数据分别进行分析,考察两种训练对于心率复杂性的影响。 结果与结论：冥想训练中心率变异序列的排列熵值均显著小于训练前的熵值,表明中国气功以及瑜伽均有降低心率复杂性的效果。而前期研究使用排列熵对于年轻、年老、心力衰竭患者的研究表明衰老和疾病会使排列熵值增加。因此,冥想训练通过自主神经对心率的调整可能是其帮助人体保持健康的一个重要因素。     

短时高频心电图的基本尺度熵分析

采用心电信号对心脏活动进行检测和分析是医学临床实践中心脏功能检测和诊断的最重要的方法和手段.本研究提出一种基本尺度熵方法来探讨时间序列的复杂性.该方法不受波形模式的影响,可以用来分析任意波形模式的信号,并且在叠加白噪声后依然显示方法的有效性.对于低维的混沌Lorentz序列,基本尺度熵值可以敏感的捕捉到动力学系统的复杂性的变化.将该方法用于心电图信号的分析,可有效地从短时序列中区分出不同的生理、病理信号.该方法简单、运算快速、抗干扰,能够给临床应用提供方便.     

多尺度交叉熵及其在血压与肾交感神经活动耦合模式中的应用

多尺度熵反映了单个时间序列在多个尺度上的复杂性,本研究提出了多尺度交叉熵的概念和算法,并探索两个时间序列在多个尺度上的耦合行为.首先,将其应用到一个混沌动力学系统的分析中,即Henon-Henon映射.然后,应用多尺度交叉熵分析清醒和麻醉两种状态下血压与肾交感神经活动的非线性耦合模式.结果表明,肾神经活动和血压之间耦合可能具有分形动力学的特征,其复杂度在麻醉后显著降低,而且两种状态下的耦合模式具有明显的差异.     

Self-affine fractal variability of human heartbeat interval dynamics in health and disease

The complexity of the cardiac rhythm is demonstrated to exhibit self-affine multifractal variability. The dynamics of heartbeat interval time series was analyzed by application of the multifractal formalism based on the Cramèr theory of large deviations. The continuous multifractal large deviation spectrum uncovers the nonlinear fractal properties in the dynamics of heart rate and presents a useful diagnostic framework for discrimination and classification of patients with cardiac disease, e.g., congestive heart failure. The characteristic multifractal pattern in heart transplant recipients or chronic heart disease highlights the importance of neuroautonomic control mechanisms regulating the fractal dynamics of the cardiac rhythm.Dedicated to Paolo Cerretelli on the occasion of his 70th birthday anniversary.     

Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy

Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales ‘t’. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entropy to the HRV time series of certain pathologies than that of healthy subjects, and to their corresponding randomized surrogate time series. This incorrect assessment of signal complexity may be due to the fact that these techniques suffer from the following limitations: (1) threshold value ‘r’ is updated as a function of long-term standard deviation and hence unable to explore the short-term variability as well as substantial variability inherited in beat-to-beat fluctuations of long-term HRV time series. (2) In RMSE, entropy values assigned to different filtered scaled time series are the result of changes in variance, but do not completely reflect the real structural organization inherited in original time series. In the present work, we propose an improved RMSE (I-RMSE) technique by introducing a new procedure to set the threshold value by taking into account the period-to-period variability inherited in a signal and evaluated it on simulated and real HRV database. The proposed I-RMSE assigns higher entropy to the age-matched healthy subjects than that of patients suffering from atrial fibrillation, congestive heart failure, sudden cardiac death and diabetes mellitus, for the entire time scales. The results strongly support the reduction in complexity of HRV time series in female group, old-aged, patients suffering from severe cardiovascular and non-cardiovascular diseases, and in their corresponding surrogate time series.     

Respiration and heart rate complexity: Effects of age and gender assessed by band-limited transfer entropy

Aging and disease are accompanied with a reduction of complex variability in the temporal patterns of heart rate. This reduction has been attributed to a break down of the underlying regulatory feedback mechanisms that maintain a homeodynamic state. Previous work has established the utility of entropy as an index of disorder, for quantification of changes in heart rate complexity. However, questions remain regarding the origin of heart rate complexity and the mechanisms involved in its reduction with aging and disease. In this work we use a newly developed technique based on the concept of band-limited transfer entropy to assess the aging-related changes in contribution of respiration and blood pressure to entropy of heart rate at different frequency bands. Noninvasive measurements of heart beat interval, respiration, and systolic blood pressure were recorded from 20 young (21–34 years) and 20 older (68–85 years) healthy adults. Band-limited transfer entropy analysis revealed a reduction in high-frequency contribution of respiration to heart rate complexity (p < 0.001) with normal aging, particularly in men. These results have the potential for dissecting the relative contributions of respiration and blood pressure-related reflexes to heart rate complexity and their degeneration with normal aging.     

Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series

The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respiration variability series measured from healthy humans in the resting supine position and in the upright position after head-up tilt.     

Nonlinear Analysis of Physiological Time Series

The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In this paper we use the nonlinear detection method to detect the nonlinear deterministic component in the physiological time series by a single variable series and two variables series respectively, and use the conditional information entropy to analyze the correlation between the heart rate, the respiration and the blood oxygen concentration. The conclusions are that there is the nonlinear deterministic component in the heart rate data and respiration data, and the heart rate and the respiration are two variables originating from the same underlying dynamics.