大鼠心率变异性频谱中高频成分的中枢机理分析

本文探讨心率变异性（ＨＲＶ）频谱中高频成分的中枢机理。对正常ＳＤ大量给予不同频率的人工通气并电刺激延髓疑核,观察ＨＲＶ频谱的改变,记录与呼吸节律同步的延髓头端腹外侧区（ｒＶＬＭ）及其周围区神经元细胞外单位放电,对ＨＲＶ和放电变异性进行相干函数分析。结果显示：（１）ＨＲＶ的高频成分的中心频率随着人工通气频率的增加而增加,呈高度线性相关,（ｒ＝０．８３,Ｐ〈０．０００１）;（２）对ｒＶＬＭ及其周围区与     

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.     

The genetic contribution to heart rate and heart rate variability in quiescent mice

Recent studies have suggested a genetic component to heart rate (HR) and HR variability (HRV). However, a systematic examination of the genetic contribution to the variation in HR and HRV has not been performed. This study investigated the genetic contribution to HR and HRV using a wide range of inbred and recombinant inbred (RI) mouse strains. Electrocardiogram data were recorded from 30 strains of inbred mice and 29 RI strains. Significant differences in mean HR and total power (TP) HRV were identified between inbred strains and RI strains. Multiple significant differences within the strain sets in mean low-frequency (LF) and high-frequency (HF) power were also found. No statistically significant concordance was found between strain distribution patterns for HR and HRV phenotypes. Genomewide interval mapping identified a significant quantitative trait locus (QTL) for HR [LOD (likelihood of the odds) score = 3.763] on chromosome 6 [peak at 53.69 megabases (Mb); designated HR 1 (Hr1)]. Suggestive QTLs for TP were found on chromosomes 2, 4, 5, 6, and 14. A suggestive QTL for LF was found on chromosome 16; for HF, we found one significant QTL on chromosome 5 (LOD score = 3.107) [peak at 53.56 Mb; designated HRV-high-frequency 1 (Hrvhf1)] and three suggestive QTLs on chromosomes 2, 11 and 15. In conclusion, the results demonstrate a strong genetic component in the regulation of resting HR and HRV evidenced by the significant differences between strains. A lack of correlation between HR and HRV phenotypes in some inbred strains suggests that different sets of genes control the phenotypes. Furthermore, QTLs were found that will provide important insight to the genetic regulation of HR and HRV at rest.     

Association of body fat percentage and heart rate variability measures of sympathovagal balance

AimsWe tested the hypothesis that body fat percentage determines cardiac sympathovagal balance in healthy subjects.Main methodsHeart rate variability (HRV) measurements were made of the standard deviation of the normal–normal RR intervals (SDNN) and the low frequency/high frequency (LF/HF) ratio, from time domain and fast Fourier transform spectral analysis of electrocardiogram RR intervals during trials of uncontrolled and controlled (paced) breathing at 0.2 Hz. Body fat percentage was measured by dual energy x-ray absorptiometric (DEXA) scanning. Significance of differences between uncontrolled and controlled (paced) breathing was determined by analysis of variance and correlations between body fat percentage and HRV measurements by Pearson's coefficient at P < 0.05.Key findingsPercent body fat was negatively correlated with LF/HF during the uncontrolled breathing (r = ? 0.56, two-tailed P < 0.05, one-tailed P < 0.01) but not during the paced breathing trial (r = ? 0.34, (P > 0.1).SignificanceWe conclude that sympathetic activity produced by paced breathing at 0.2 Hz can obscure the relationship between body fat percentage and sympathovagal balance and that high body fat percentage may be associated with low sympathetic modulation of the heart rate in healthy adolescent/young adult males.     

Sympathetic nervous system representation in time and frequency domain indices of heart rate variability

This study evaluated the contributions of sympathetic and parasympathetic modulation to heart rate variability during situations in which vagal and sympathetic tone predominated. In a placebo-controlled, randomized, double blind blockade study, six young healthy male individuals received propranolol (0.2 mg x kg(-1)), atropine (0.04 mg x kg(-1)), propranolol plus atropine, or placebo infusions over 4 days. Time-domain indices were calculated during 40 min of rest and 20 min of exercise at 70% of maximal exercise intensity. Spectrum analysis, using fast Fourier transformation, was also performed at rest and during the exercise. The time-domain indices standard deviation of R-R intervals, mean of the standard deviations of all R-R intervals for all 5-min segments, percentage of number of pairs of adjacent R-R intervals differing by more than 50 ms, and square root of the mean of the sum of squares of differences between adjacent R-R intervals were reduced after atropine and propranolol plus atropine. Propranolol alone caused no appreciable change in any of the time-domain indices. At rest, all spectrum components were similar after placebo and propranolol infusions, but following parasympathetic and double autonomic blockade there was a reduction in all components of the spectrum analysis, except for the low:high ratio. During exercise, partial and double blockade did not change significantly any of the spectrum components. Thus, time and frequency-domain indices of heart rate variability were able to detect vagal activity, but could not detect sympathetic activity. During exercise, spectrum analysis is not capable of evaluating autonomic modulation of heart rate.     

A circadian rhythm in heart rate variability contributes to the increased cardiac sympathovagal response to awakening in the morning

Morning hours are associated with a heightened risk of adverse cardiovascular events. Recent evidence suggests that the sleep-wake cycle and endogenous circadian system modulate cardiac function in humans and may contribute to these epidemiological findings. The aim of the present study was to investigate the interaction between circadian and sleep-wake-dependent processes on heart rate variability (HRV). Fifteen diurnally active healthy young adults underwent a 72-h ultradian sleep-wake cycle (USW) procedure (alternating 60-min wake episodes in dim light and 60-min nap opportunities in total darkness) in time isolation. The present study revealed a significant main effect of sleep-wake-dependent and circadian processes on cardiac rhythmicity, as well as a significant interaction between these processes. Turning the lights off was associated with a rapid increase in mean RR interval and cardiac parasympathetic modulation (high-frequency [HF] power), whereas low-frequency (LF) power and sympathovagal balance (LF:HF ratio) were reduced (p?≤?.001). A significant circadian rhythm in mean RR interval and HRV components was observed throughout the wake and nap episodes (p?≤?.001). Sleep-to-wake transitions occurring in the morning were associated with maximal shifts towards sympathetic autonomic activation as compared to those occurring during the rest of the day. Namely, peak LF:HF ratio was observed in the morning, coincidental with peak salivary cortisol levels. These results contribute to our understanding of the observed increase in cardiovascular vulnerability after awakening in the morning.     

Clinical correlates of non-linear indices of heart rate variability in chronic heart failure patients.

We assessed the clinical correlates of a comprehensive set of non-linear heart rate variability (HRV) indices computed from 24-h Holter recordings for 200 stable chronic heart failure (CHF) patients [median age (lower quartile, upper quartile) 54 (47, 58) years, LVEF 23% (19%, 28%)]. A total of 19 non-linear indices belonging to six major families, namely symbolic dynamics, entropy, empirical mode decomposition, fractality-multifractality, unpredictability and Poincaré plots, were considered. Most indices showed a significant association with ejection fraction and with the severity of symptoms, while only two (one each from the fractality and Poincaré plot families) showed an association with aetiology. Only one symbolic dynamics variable was associated with the presence of non-sustained ventricular tachycardia and two symbolic dynamics variables were associated with the rate of ventricular ectopic events. Our results demonstrate the existence of selective links between non-linear indexes of HRV and the clinical status and functional impairment of CHF patients. This indicates that further studies should be designed to investigate the physiopathological mechanisms involved in such links.     

Dimensional analysis of heart rate variability in heart transplant recipients

Periodicities in the heart rate have been known for some time. We discuss these periodicities in normal and transplanted hearts. We then consider the possibility of dimensional analysis of these periodicities in transplanted hearts and problems associated with the record.     

Spectral peak frequency in low-frequency band in cross spectra of blood pressure and heart rate fluctuations in young type 1 diabetic patients

In this study we tested whether joint evaluation of the frequency (f(cs)) at which maxima of power in the cross-spectra between the variability in systolic blood pressure and inter-beat intervals in the range of 0.06-0.12 Hz occur together with the quantification of baroreflex sensitivity (BRS) may improve early detection of autonomic dysfunction in type 1 diabetes mellitus (T1DM). We measured 14 T1DM patients (age 20.3-24.2 years, DM duration 10.4-14.2 years, without any signs of autonomic neuropathy) and 14 age-matched controls (Co). Finger arterial blood pressure was continuously recorded by Finapres for one hour. BRS and f(cs) were determined by the spectral method. Receiver-operating curves (ROC) were calculated for f(cs), BRS, and a combination of both factors determined as F(z)=1/(1+exp(-z)), z=3.09-0.013*BRS-0.027*f(cs). T1DM had significantly lower f(cs) than Co (T1DM: 88.8+/-6.7 vs. Co: 93.7+/-3.8 mHz; p<0.05), and a tendency towards lower BRS compared to Co (T1DM: 10.3+/-4.4 vs. Co: 14.6+/-7.1 ms/mm Hg; p=0.06). The ROC for Fz showed the highest sensitivity and specificity (71.4 % and 71.4 %) in comparison with BRS (64.3 % and 71.4 %) or f(cs) (64.3 % and 64.3 %). The presented method of evaluation of BRS and f(cs) forming an integrated factor Fz could provide further improvement in the risk stratification of diabetic patients.     

Autonomic nervous nonlinear interactions lead to frequency modulation between low- and high-frequency bands of the heart rate variability spectrum

Cardiac sympathetic and parasympathetic neural activities have been found to interact with each other to efficiently regulate the heart rate and maintain homeostasis. Quantitative and noninvasive methods used to detect the presence of interactions have been lacking, however. This may be because interactions among autonomic nervous systems are nonlinear and nonstationary. The goal of this work was to identify nonlinear interactions between the sympathetic and parasympathetic nervous systems in the form of frequency and amplitude modulations in human heart rate data. To this end, wavelet analysis was performed, followed by frequency analysis of the resultant wavelet decomposed signals in several frequency brackets defined as very low frequency (f < 0.04 Hz), low frequency (LF; 0.04-0.15 Hz), and high frequency (HF; 0.15-0.4 Hz). Our analysis suggests that the HF band is significantly modulated by the LF band in the heart rate data obtained in both supine and upright body positions. The strength of modulations is stronger in the upright than supine position, which is consistent with elevated sympathetic nervous activities in the upright position. Furthermore, significantly stronger frequency modulation than in the control condition was also observed with the cold pressor test. The results with the cold pressor test, as well as the body position experiments, further demonstrate that the frequency modulation between LF and HF is most likely due to sympathetic and parasympathetic nervous interactions during sympathetic activations. The modulation phenomenon suggests that the parasympathetic nervous system is frequency modulated by the sympathetic nervous system. In this study, there was no evidence of amplitude modulation among these frequencies.     

Determination of heart rate and heart rate variability in the equine fetus by fetomaternal electrocardiography

Heart rate is an important parameter of fetal well-being. We have analyzed fetal heart rate (HR) and heart rate variability (HRV) by fetomaternal electrocardiography (ECG) in the horse (Equus caballus) from midpregnancy to foaling. It was the aim of the study to detect changes in the regulation of fetal cardiac activity over time and to establish normal values in undisturbed pregnancies. A total of 22 mares were available for the study. Fetomaternal electrocardiography was a reliable technique to detect cardiac signals in fetuses between Day 173 of gestation and foaling. Fetal HR decreased from 115 ± 4 beats/min (Days 170 to 240 of gestation) to 83 ± 3 beats/min (Day 320) to 79 ± 1 beats/min (1 d before foaling; P < 0.001). Mean beat to beat (RR) interval and standard deviation of the RR interval (SDRR) increased (P < 0.001). Gestational age thus affects RR interval and HR in the equine fetus. From Days 270 to 340 of gestation, SDRR increased from 11.4 ± 1.3 msec on Day 270 to 27.8 ± 3.6 msec on Day 340 (P < 0.05), and the root mean square of successive RR differences (RMSSD) tended to increase (P = 0.07), indicating maturation of the fetal autonomous nervous system. For the last 10 d before foaling, fetal HR and HRV remained constant and did not allow predicting the onset of parturition in the horse. Only during the last 30 min before the foal was born, in 4 of 5 fetuses, HR decreased and RR interval increased. Accelerations and decelerations in HR were detectable at all times, but neither their number nor duration changed over time.     

电刺激迷走神经对蟾蜍心率和心率变异的影响

目的:观察不同频率迷走神经刺激对蟾蜍离体心脏的心率及心率变异的影响。方法:将蟾蜍心脏和右侧迷走交感干离体后,以不同频率电刺激神经,记录心电图曲线并作心率变异性(HRV)分析。结果:交感神经阻断后,电刺激迷走交感干,心率(HR)显著下降(P0.01),全部正常心动周期的标准差(SDNN)和相邻正常心动周期差值的均方根(RMSSD)显著升高(P0.01),不同频率刺激组之间没有明显差异;与对照组相比,各指标变化较大;给药组0.2Hz时高频(HF)显著升高(P0.01),低频/高频比值(LF/HF)明显降低(P0.05),0.8Hz时HF和LF/HF接近刺激前水平。结论:一定范围内增加刺激频率,迷走神经降低心率的作用增强;没有交感神经调节条件下的迷走神经对心率和心率变异的调节可能存在不同的机制。     

Influence of external periodic stimuli on heart rate variability in healthy subjects and in coronary heart disease patients

Frequency estimates of the heart rate variability (HRV) spectrum influenced by external periodic stimuli were studied in healthy subjects and patients with coronary heart disease (CHD). Sensory stimulation by periodic eye opening at a rate of 15, 10, 8, 6, or 5 times per minute, as well as spontaneous and controlled breathing at a rate of 15, 10, 8, 6, or 5 times per minute, was used. It was found that the spectral response to external periodic oscillations was determined by a frequency-dependent phenomenon, the maximal amplitude of heart rate variations being observed in the case of external stimuli at a frequency of 0.1 Hz. A resonance frequency in the 0.1-Hz range may be suggested to exist in the cardiovascular controls. Significant differences in the HRV frequency characteristics between CHD patients and healthy subjects were shown. CHD patients had a characteristic decline in HRV responses to external oscillations; the power of these responses did not depend on the frequency of external stimuli.     

Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification

Automatic classification of cardiac arrhythmias using heart rate variability (HRV) analysis has been an important research topic in recent years. Explorations reveal that various HRV feature combinations can provide highly accurate models for some rhythm disorders. However, the proposed feature combinations lack a direct and carefully designed comparison. The goal of this work is to assess the various HRV feature combinations in classification of cardiac arrhythmias. In this setting, a total of 56 known HRV features are grouped in eight feature combinations. We evaluate and compare the combinations on a difficult problem of automatic classification between nine types of cardiac rhythms using three classification algorithms: support vector machines, AdaBoosted C4.5, and random forest. The effect of analyzed segment length on classification accuracy is also examined. The results demonstrate that there are three combinations that stand out the most, with total classification accuracy of roughly 85% on time segments of 20 s duration. A simple combination of time domain features is shown to be comparable to the more informed combinations, with only 1–4% worse results on average than the three best ones. Random forest and AdaBoosted C4.5 are shown to be comparably accurate, while support vector machines was less accurate (4–5%) on this problem. We conclude that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias. The analysis also shows that reasonably accurate arrhythmia classification lies in the range of 10–40 s, with a peak at 20 s, and a significant drop after 40 s.