心率与血压的变异性：分析方法,生理意义及其应用

本文回顾了关心回顾变异性及血压变异性的最新进展。在分析方法方面介绍了单一生理变量多变量系统的线性分析技术及其主要结果。对ＨＲＶ／ＢＰＶ谱的生理意义及其应用问题,也进行了回顾了评述。     

Technique for the quantification of transient quadratic phase couplings between heart rate components.

A technique for the time-variant analysis of quadratic phase coupling (QPC) in heart rate data is introduced and tested in 6 human neonates during quiet sleep. The set up of the approach is based up on the assumption that QPCs in the heart rate variability (HRV) are related to amplitude modulation effects. The application of the biamplitude deals with the detection of the coupling pattern and the bicoherence is used for the statistical quantification of coupling. By means of the results of bispectral analysis the time-variant processing has been adapted. The frequency-selective complex demodulation of the HRV leads to the envelope of the respiratory sinus arrhythmia (RSA), this has been used as one input for a time-variant coherence analysis. The other input is the low-pass filtered 10-second-rhythm of the HRV. A time-continuous quantification of the QPC, caused by amplitude modulation (10-second-rhythm modulates the RSA), is possible using this approach. According to our observed results in neonatal HRV both a phase co-ordination between the 10-second-rhythm and RSA as well as a non-linear coupling (amplitude modulation) between these HRV components can be seen.     

Fractal dimension in health and heart failure.

BACKGROUND: Non-linear analysis of heart rate variability (HRV) can give additional information about autonomic control of the heart rate. This study applied the fractal dimension (FD) in a congestive heart failure (CHF) population. METHODS: FD and HRV were evaluated in a healthy population (n=21) and an end-stage heart failure population (n=21) using 1-h segments during the day and night from Holter recordings. RESULTS: CHF patients presented a loss of circadian variation in both FD and conventional time- and frequency-domain HRV indices. FD was higher in the CHF population both during the day and night. In the CHF population the correlation between FD and high-frequency power of HRV was lost. CONCLUSION: Day-night variations of heart rate fluctuations are lost in heart failure. Changes in FD reflecting physiological and pathophysiological changes were observed.     

An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal

ObjectiveThe present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented.Materials and methodsFirst, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones.ResultsIn this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient.ConclusionThe ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals.     

Hilbert-Huang transform for analysis of heart rate variability in cardiac health

This paper introduces a modified technique based on Hilbert-Huang transform (HHT) to improve the spectrum estimates of heart rate variability (HRV). In order to make the beat-to-beat (RR) interval be a function of time and produce an evenly sampled time series, we first adopt a preprocessing method to interpolate and resample the original RR interval. Then, the HHT, which is based on the empirical mode decomposition (EMD) approach to decompose the HRV signal into several monocomponent signals that become analytic signals by means of Hilbert transform, is proposed to extract the features of preprocessed time series and to characterize the dynamic behaviors of parasympathetic and sympathetic nervous system of heart. At last, the frequency behaviors of the Hilbert spectrum and Hilbert marginal spectrum (HMS) are studied to estimate the spectral traits of HRV signals. In this paper, two kinds of experiment data are used to compare our method with the conventional power spectral density (PSD) estimation. The analysis results of the simulated HRV series show that interpolation and resampling are basic requirements for HRV data processing, and HMS is superior to PSD estimation. On the other hand, in order to further prove the superiority of our approach, real HRV signals are collected from seven young health subjects under the condition that autonomic nervous system (ANS) is blocked by certain acute selective blocking drugs: atropine and metoprolol. The high-frequency power/total power ratio and low-frequency power/high-frequency power ratio indicate that compared with the Fourier spectrum based on principal dynamic mode, our method is more sensitive and effective to identify the low-frequency and high-frequency bands of HRV.     

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.     

Non-linear dynamic analysis of the cardiac rhythm during transient myocardial ischemia.

Coronary artery occlusions related to myocardial ischemia drive cardiac control system reactions that may lead to heart failure. The purpose of this study was to assess the autonomic nervous system (ANS) response during prolonged percutaneous transluminal coronary angioplasty (PTCA). Continuous ECG data were acquired from 50 patients before and during PTCA, with occlusions in the left anterior descending, left circumflex or right coronary artery. Heart rate variability (HRV) was analyzed for 3-min segments of the R-R interval signal obtained from ECG data. The ANS behavior was evaluated by HRV analysis using fractal-like indices. The fractal scalar exponent alpha(1) and power-law slope beta decreased considerably during PTCA. This indicates that significant reactions of autonomic control of the heart rate occurred during coronary artery occlusions, with a reduction in complexity of the ANS.     

Comparative Evaluation of Heart Rate Variability Based on the Data of ECG and Blood Pressure Measurements

The development of new approaches to the assessment of heart rate variability (HRV) is an important problem, since HRV reflects the functioning of cardiovascular control and is affected by various diseases. The purpose of this study was to evaluate the informative value of statistical and spectral HRV parameters calculated from pulse interval (PI) data of blood pressure as compared with those calculated from RR-interval data of electrocardiograms (ECG). We recorded ECG in conscious rats using skin adhesive electrodes simultaneously with blood pressure signal obtained through a catheter in the femoral artery. It has been found that the PI sequence can be used to calculate the statistical HRV indices that describe the HRV at time intervals about 1 min or longer, but statistical indices of the PI and RR intervals may differ in the analysis of beat-tobeat variations. The power spectra of the RR intervals and PI coincide in the low-frequency region, including the band of baroreflex cardiac rhythm oscillation. However, they can differ in the high-frequency region (at respiration frequency and above).     

Separation of Respiratory Influences from the Tachogram: A Methodological Evaluation

The variability of the heart rate (HRV) is widely studied as it contains information about the activity of the autonomic nervous system (ANS). However, HRV is influenced by breathing, independently of ANS activity. It is therefore important to include respiratory information in HRV analyses in order to correctly interpret the results. In this paper, we propose to record respiratory activity and use this information to separate the tachogram in two components: one which is related to breathing and one which contains all heart rate variations that are unrelated to respiration. Several algorithms to achieve this have been suggested in the literature, but no comparison between the methods has been performed yet. In this paper, we conduct two studies to evaluate the methods'' performances to accurately decompose the tachogram in two components and to assess the robustness of the algorithms. The results show that orthogonal subspace projection and an ARMAX model yield the best performances over the two comparison studies. In addition, a real-life example of stress classification is presented to demonstrate that this approach to separate respiratory information in HRV studies can reveal changes in the heart rate variations that are otherwise masked by differing respiratory patterns.     

Mechanisms of Periodic Heart Rate Oscillations: A Study Using Controlled Breathing Tests

An orthostatic test with frequency-controlled breathing (with periods of 4, 6, 8, 10, and 12 s) was used to analyze frequency estimates of the heart rate variability (HRV) spectrum in the low frequency (LF) and high frequency (HF) ranges in 36 volunteers (26 men and 10 women) aged 19–21 years without signs of heart or respiratory pathology. The subjects took a breath at the moment of an auditory signal. There were no other requirements for the respiration rhythm. Variables were compared using Wilcoxon’s test for pairwise comparisons; correlations were estimated by Spearman’s rank correlation R test. The sensitivities of the LF and HF ranges of the HRV spectrum to periodic respiratory perturbations at different frequencies were demonstrated to differ from each other. Autonomous 0.10- and 0.25-Hz circuits of oscillatory processes were found in HRV. The transition zone of influence of these circuits was located in the region around 0.125 Hz. The characteristics of the 0.10- and 0.25-Hz oscillations in HRV were studied. It was demonstrated that the 0.10-Hz oscillatory process is a potent mechanism of heart rate control, is affected by external factors, and determines the dynamics of the autonomic nervous state of the body, while the 0.25-Hz process is a regulatory mechanism of medium strength, is resistant to external factors, and characterizes the adaptation reserve of the autonomic nervous control of the heart rate, as well as the autonomic nervous state of the body. Resonance responses in the HRV spec-trum can be used for studying the characteristics of the 0.10- and 0.25-Hz oscillations.__________Translated from Fiziologiya Cheloveka, Vol. 31, No. 3, 2005, pp. 76–83.Original Russian Text Copyright © 2005 by Kiselev, Kirichuk, Posnenkova, Gridnev.     

Real-time detection of workload changes using heart rate variability

This work presents a novel approach to detecting real-time changes in workload using heart rate variability (HRV). We propose that for a given workload state, the values of HRV vary in a sub-range of a Gaussian distribution. We describe methods to monitor a HRV signal in real-time for change points based upon sub-Gaussian fitting. We tested our method on subjects sitting at a computer performing a low workload surveillance task and a high workload video game task. The proposed algorithm showed superior performance compared to the classic CUSUM method for detecting task changes.     

A modified Zeeman model for producing HRV signals and its application to ECG signal generation

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.     

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。     

Informational and statistical measures of the self-organization of the heart regulation systems in the analysis of heart rate variability

The results of orthostatic tests measuring heart rate variability (HRV) in athletes are outlined here, using the Dirichlet distribution and the properties of information entropy. It has been shown that informational and statistical measures used for the analysis of HRV reflect the state of homeostasis regulating the cardiac activity and its dynamics with a higher degree of accuracy than the conventional indicators of variation statistics and spectral analysis.     

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。     

Low HRV entropy is strongly associated with myocardial infarction.

Heart rate variability (HRV) is a marker of autonomous activity in the heart. An important application of HRV measures is the stratification of mortality risk after myocardial infarction. Our hypothesis is that the information entropy of HRV, a non-linear approach, is a suitable measure for this assessment. As a first step, to evaluate the effect of myocardial infarction on the entropy, we compared the entropy to standard HRV parameters. The entropy was estimated by compressing the tachogram with Bzip2. For univariate comparison, statistical tests were used. Multivariate analysis was carried out using automatically generated decision trees. The classification rate and the simplicity of the decision trees were the two evaluation criteria. The findings support our hypothesis. The meanNN-normalized entropy is reduced in patients with myocardial infarction with very high significance. One entropy parameter alone exceeds the discrimination strength of multivariate standards-based trees.     

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.     

Cardiorespiratory Information Dynamics during Mental Arithmetic and Sustained Attention

An analysis of cardiorespiratory dynamics during mental arithmetic, which induces stress, and sustained attention was conducted using information theory. The information storage and internal information of heart rate variability (HRV) were determined respectively as the self-entropy of the tachogram, and the self-entropy of the tachogram conditioned to the knowledge of respiration. The information transfer and cross information from respiration to HRV were assessed as the transfer and cross-entropy, both measures of cardiorespiratory coupling. These information-theoretic measures identified significant nonlinearities in the cardiorespiratory time series. Additionally, it was shown that, although mental stress is related to a reduction in vagal activity, no difference in cardiorespiratory coupling was found when several mental states (rest, mental stress, sustained attention) are compared. However, the self-entropy of HRV conditioned to respiration was very informative to study the predictability of RR interval series during mental tasks, and showed higher predictability during mental arithmetic compared to sustained attention or rest.     

Heart rate regulation processed through wavelet analysis and change detection: some case studies

Heart rate variability (HRV) is an indicator of the regulation of the heart, see Task Force (Circulation 93(5):1043-1065, 1996). This study compares the regulation of the heart in two cases of healthy subjects within real life situations: Marathon runners and shift workers. After an update on the state of the art on HRV processing, we specify our probabilistic model: We choose modeling heartbeat series by locally stationary Gaussian process (Dahlhaus in Ann Stat 25, 1997). HRV is then processed by the combination of two statistical methods: (1) Continuous wavelet transform for calculating the spectral density energy in the high frequency (HF) and low frequency (LF) bands and (2) Change point analysis to detect changes of heart regulation. Next, we plot the variations of the HF and LF energy in extreme conditions for both populations. This puts in light, that physical activities (rest, moderate sport, marathon race) can be ordered in a logical continuum. This allows to define a new index based on HF and LF energy that is log HF + log LF which appears relevant to measure HR regulation. The results obtained are pertinent but have to be completed by further studies.     

Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case.

Signals from different systems are analyzed during sleep on a beat-to-beat basis to provide a quantitative measure of synchronization with the heart rate variability (HRV) signal, oscillations of which reflect the action of the autonomic nervous system. Beat-to-beat variability signals synchronized to QRS occurrence on ECG signals were extracted from respiration, electroencephalogram (EEG) and electromyogram (EMG) traces. The analysis was restricted to sleep stage 2. Cyclic alternating pattern (CAP) periods were detected from EEG signals and the following conditions were identified: stage 2 non-CAP (2 NCAP), stage 2 CAP (2 CAP) and stage 2 CAP with myoclonus (2 CAP MC). The coupling relationships between pairs of variability signals were studied in both the time and frequency domains. Passing from 2 NCAP to 2 CAP, sympathetic activation is indicated by tachycardia and reduced respiratory arrhythmia in the heart rate signal. At the same time, we observed a marked link between EEG and HRV at the CAP frequency. During 2 CAP MC, the increased synchronization involved myoclonus and respiration. The underlying mechanism seems to be related to a global control system at the central level that involves the different systems.