全身麻醉状态下心率变异性的连续小波分析

为更准确地通过分析心率变异性(HRV)判断麻醉深度随时间的变化,需要密切关注麻醉状态下HRV低频(LF)和高频(HF)成分随时间的变化情况,采用连续小波变换(CWT),将CWT中的尺度转换为频率,对患者麻醉前后的HRV信号(RR间期序列)进行了时频分析。其时频能量图以及LF、HF能量值都表明麻醉后HRV信号的LF和HF成分受到了抑制,LF/HF值也由麻醉前的9.021 9降为麻醉后的3.557 3。CWT和传统的时频分析方法在分析同一麻醉后HRV的时频分布表明,CWT可以更准确地定位HRV时域信号中出现突变的时间以及引起频率变化的频段范围。因此,CWT作为分析麻醉状态下HRV的一种新方法,能提供HRV更为准确的时频定位,进而提供更为准确的麻醉深度监控结果。     

分频段Hilbert谱熵的心率变异信号分析方法

心率变异(HRV)信号分析对心脏系统疾病的辅助诊断、监护及评估有十分重要的意义。本研究将Hilbert谱分析方法和非线性熵分析方法相结合,提出了基于分频段Hilbert谱熵的HRV信号分析的新方法。依据Hilbert谱的时频多分辨率特性和HRV信号频谱特征,在不同频段计算HRV信号的Hilbert谱熵和加权表示的全频段谱熵。对HRV信号的生理因素按频段适当分离后进行Hilbert谱熵的分析,更有利于表征某些生理病理的特征。对MIT-BIH数据库中实际HRV信号分析表明,这两种熵值能有效地区分年轻人、老年人及房颤患者三个样本组和健康人、充血性心力衰竭患者两个样本组,统计性能优于普通的时频熵方法,为临床HRV信号分析提供了一种有效方法。     

HHT谱分析方法在利用ECG信号检测睡眠呼吸暂停中的应用

目的:根据睡眠呼吸暂停与心率变化的关系,探讨从心电图中检测睡眠呼吸暂停的方法.方法:通过经验模态分解(EMD)技术将一非线性、非稳态过程的心率变异信号分解为一组内在模态函数(IMFs),对每个IMF进行Hilbert变换,获得HRV信号幅度和频率的时间分布,再根据已获得的HH谱,进而得到边际谱,然后提取信号能量的时频分布、瞬时频率、瞬时能量比、瞬时幅度的标准差等特征向量,根据特征向量的变化检测出睡眠呼吸暂停的位置和分布.结果:对同一个体的HRV信号的分析结果显示,正常呼吸阶段HRV信号的特征向量与睡眠呼吸暂停阶段HRV信号的特征向量有明显区别,实验结果证实了本文所提方法的有效性.结论:该检测方法物理意义明确,诊断结果精度高,为睡眠呼吸暂停综合症的早期诊断、监护及预后评估提供了新的分析工具.     

基于小波变换与基于短时傅立叶变换的超声多普勒血流信号时频分布比较研究

本研究讨论了一种改进的Morlet(MMORL)小波变换在分析超声多普勒血流信号时的优点。用小波变换和短时傅立叶变换计算得出时频分布与理论的时频分布进行了比较。结果得出小波变换能提供较好的时频分辨率的折中效果，能产生更精确的平均频率和带宽。     

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。     

基于小波变换的心率变异性信号的分解

根据心率变异性（Heartr ate Variability，HRV）的牲，运用小波变换的分析方法将HRV信号分解成1／f分形部分和非1／f部分，有利于提取HRV信号的特征量和进行1／f部分的定量分析。     

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。     

高可靠度心率变异性信号获取的实验研究

心率变异性信号的获取在生理研究和临床诊断中都有着重要的应用价值。为了保证心率变异性分析的准确性，必须考虑心率变异性的获取方法。本文利用信号奇异点及其小波变换的关系，设计了HRV信号的R波获取软件。对MIT／BIH心电数据库中的37个记录文件进行R波的检测实验，检测实验效果令人满意。     

基于非负盲分离的胎儿心率检测方法

胎儿心率监测是一种有效评估胎儿当前健康状况的重要参考依据。为了可以快速准确地获取胎儿心率,该文提出一种基于非负盲分离的胎儿心率检测方法。该方法首先对采集得到的腹壁信号进行预处理,平稳小波变换后重构出母亲心电信号;接着,采用相减法去除母亲心电信号,再把剩下含有噪声的胎儿心电信号通过时频变换得到Born-Jordan分布;最后,利用非负矩阵分解得到胎儿心电的特征信号,检测其R波位置求得胎儿瞬时心率。实验结果表明,该方法可以快速、准确有效地获得胎儿地瞬时心率数据。     

两种计算正常成年人短时心率变异性方法的比较研究

目的利用光电容积脉搏波法分析心率变异性,为通过心率变异性(heart rate variability,HRV)参数表征自主神经系统的变化提供新的研究方法。方法对46个健康成年受试者分别同步采集自主呼吸状态下的心电信号和光电容积脉搏波信号,利用二阶差分极大值提取心电图的RR间期,搜索脉搏波极大值提取脉搏波主波波峰间期PP,然后计算通用HRV参数SDNN、RMSSD和LF/HF,并利用回归的统计学方法结合Bland-Altman随机分析法对这两种方法获取的HRV结果进行比较。结果两种方法计算得到的心率变异性参数结果的Pearson相关系数分别为0.998、0.995和0.992,均显著相关(P0.01),Bland-Altman分布图也均在一致性区间内,说明这两种方法在计算心率变异性上具有一致性。结论利用光电容积脉搏波法分析心率变异具有可行性。     

基于时频分析的EEG信号分析处理方法研究进展

由于脑电信号(EEG)是典型的非平稳时变信号,因此时频分析方法比较适用于分析和处理EEG信号。本文在简要介绍时频分析的发展及主要方法的基础上,综述了时频分析方法在EEG信号分析处理中的应用及研究进展,并对现存的问题作了探讨。     

基于非线性尺度小波变换的表面肌电信号的分类

表面肌电信号（Surface EMG,sEMG）是一种复杂的非线性非平稳信号。我们介绍了一种非线性尺度小波变换（Wavelet transform with nonlinear scale，NWT）。由于NWT具有渐进缩短时间分辨率的特点．所以有利于从sEMG信号获得精确的时一频信息。首先，用NWT将sEMG信号（30组前臂内旋和30组外旋的sEMG信号）变换为强度分布（时频分布）．然后，用由主成分分析获得的强度分布特征值构成特征向量．最后，用BP神经网络对两种信号模式的特征向量进行分类识别。结果表明：与两种传统的时频分析方法相比，NWT能够获得较高的正确识别率．同时降低了神经网络计算的复杂度。     

Analysis of cardiac health using fractal dimension and wavelet transformation

Analysis of heart rate variation (HRV) has become a popular non-invasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper presents the continuous time wavelet analysis of heart rate variability signal for disease identification. Fractal dimension (FD) of heart rate signals are calculated and compared with the wavelet analysis patterns. The FD obtained indicates more than 90% confidence interval for all the classes studied.     

Time-frequency and time-varying analysis for assessing the dynamic responses of cardiovascular control

Time-frequency or time-variant methods have been extensively applied in the study of the heart-rate variability (HRV) signal. In fact, the frequency content of HRV signal has a strong correlation with the control system assessing heart rate. In particular, the power related to the low-frequency (LF) and high-frequency (HF) components have been demonstrated to correlate to the action of sympathetic and parasympathetic branches of the autonomic nervous system. However, the analysis is restricted to stationary conditions, unless time-frequency methods are employed for detecting dynamic changes that may occur during physiological and pathological conditions.This article reviews the most diffused tools for time-frequency analysis, starting from linear decomposition of the signal (including short-time Fourier transform and wavelet and wavelet packet decomposition), to quadratic time-frequency distributions (including Wigner-Ville transform and Cohen's class of distributions), and finally to adaptive or time-variant autoregressive (AR) models, in both the mono- and bivariate forms. In the past few years, these approaches have been applied in several studies related to cardiovascular responses during nonstationary pathophysiological events. Among them, we will recall and discuss myocardial ischemia (spontaneous or induced), drug infusion, rest-tilt maneuver and syncope, neurophysiological, and sleep investigations.     

Time-Frequency Analysis of Heart Rate Variability During Transient Segments

The spectral characteristics of heart rate variability (HRV) are related to the modulation of the autonomic nervous system. As the physiological condition is changed by such external stimuli such as drugs, postural changes, and anesthesia, or by internal deregulation such as in syncope, adjective autonomic responses could alter HRV characteristics. Time-frequency analysis is commonly used to investigate the time-related HRV characteristics. An alteration of the autonomic regulation resulting in a change in mean heart rate induces a transient component in heart rate, which, with any analysis method based on signals from multiple beats, results in the apparent spread of the spectrum of frequencies. This obscures the spectral components related to the autonomic function. In this paper we investigated the influence of the transient component in several time-frequency methods including the short-time Fourier transform, the Choi-Williams distribution, the smoothed pseudo Wigner–Ville distribution (SPWVD), the filtering SPWVD compensation, and the discrete wavelet transform. One simulated signal and two heart rate signals during general anesthesia and postural change were used for this assessment. The result demonstrates that the filtering SPWVD compensation and the discrete wavelet transform have small spectrum interference from the transient component. © 2001 Biomedical Engineering Society. PAC01: 8719Hh, 8780-y     

心率变异性短时分析的非平稳性影响研究

针对短时心率变异性(heart ratev ariability,HRV)分析在临床中常常不能得到一致性结果的情况,研究几种常用HRV短时分析参数受非平稳性的影响。通过几种参数与平稳性衡量参数(均值、标准差)的相关性分析,结果表明,各短时参数在长时数据中呈现出随时间变化的波动,其中HFnorm和SE受非平稳干扰影响大,LFnorm和BE受非平稳干扰影响小。从而推论,非平稳干扰是影响短时HRV分析结果一致性的一个原因,尽量排除非平稳干扰,严格保证数据的可比性前提,可提高其分析结果的可靠性和一致性;同时,HRV中的低频波动不只包含了非平稳干扰的影响,还蕴含了心脏动力系统的固有特性,短时分析参数由于未能包含这部分低频信息,所以不能对心脏动力系统提供全面描述,这是导致短时HRV分析结果一致性差的另一个重要原因。     

Intraindividual analysis of instantaneous heart rate variability

We examined the use and potential of quantifying instantaneous heart rate variability (HRV) using a joint time-frequency and time-domain methods. These new techniques are promising, because they provide tools to quantify nonstationary, beat-by-beat changes in HRV components, and are therefore flexible with respect to the design of experimental protocols. A smoothed pseudo-Wigner-Ville distribution (SPWVD) and a time-domain index using polynomial filtering produced fairly coherent estimates of band-specific HRV amplitudes, whereas SPWVD yielded additional information on the frequency characteristics of HRV. Instantaneous HRV appeared to have a complex and a frequency-specific relationship to cardiac activity and electrodermal activity, It is concluded that the time-frequency analysis of HRV is a very promising method for mapping transient changes in the frequency and amplitude characteristics of cardiac dynamics.