A preliminary study on the effect of age and heart rate on the short-term heart rate variability (original) (raw)
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Investigation of the correlation between heart rate and heart rate variability
Computers in Cardiology 1995, 1995
Pathologic conditions are flequently associated with ntarked changes in heart rate (HR), which itself influences ifs variability (HR v). Accordingly, some researchers consider the HR and the HR V to he equivalent measures. Question: Does the HRV afford dvferent information than the HR itself7 Method: The HRV were investigated in ten patients during a normal autonomic tone and a pharmacological autonomic b lockade, respectively. The results were analysed by calculation of the product-nroment correlation coeflcient and by means of linear regression of he HRV and the HR. Results: Signrficant correldion were found between the IIR and all of the HRV bands. The regression coeflcients of the HR and HRI' increased mnrkedy qper a .ympathetic blockade. The extent of the rqression decreased to nearly zero dtrring a total ve,qetnti\*e blockade. C'onclusions: ( I ) ll?ere is a signijicant negative correlation between the HR and the HRV.
International Journal of Cardiology, 2013
Heart rate (HR) is a strong risk factor for cardiovascular mortality [1], whereas heart rate variability (HRV) may predict outcomes in different disease states . HRV is significantly associated with average heart rate, therefore, one can say that HRV actually provides information on two quantities, i.e. on HR and its variability. It is hard to determine which of these two plays a principal role in the prognostic value of HRV. Furthermore, the association between HRV and HR is not only a physiological phenomenon but also a mathematical one, which is due to non-linear (mathematical) relationship between RR interval and HR . However, by a mathematical modification one may strengthen, weaken or even remove the HRV dependence on HR . Such modifications may enable to explore the HR influence on the prognostic power of HRV. One can also investigate if the modified HRV, with different dependence on HR, exhibits different predictive powers for various modes of death . In this article we show how to modify the association between HRV and HR as well as list potential perspectives for such approach.
2005
HRV may be calculated using R-R interval fluctuations (RR) or fluctuations of instantaneous heart rates (IHR) (i.e. 1/RR signal). Because of the non-linear relationship between RR-intervals and heart rates, the relation between HRV and average heart rate (HR) may be different depending on whether RR or IHR is employed. This non-linearity also brings problems concerning comparison of HRV between patients revealing different HR's. Aim: To prove that the type of signal (RR or IHR) determines outcomes when analyzing HRV among patients with different HR's and to investigate whether the normalization procedure or the use of corrected signals remove problems concerning the non-linearity. Method: ECG recordings (512 beats) of 55 patients were considered. RR and IHR were calculated. Both signals were divided by their average values yielding the corrected RR and IHR. HRV spectra were estimated from these 4 kinds of signals. Total power (TP), LF and HF components, LF/HF coefficient and normalized values (i.e. nLF, nHF) were calculated. Results: TP and LF estimated from RR correlated negatively with HR but those calculated from IHR revealed a trend towards a positive correlation-respective correlation coefficients differed, p<0.01. The sign of correlation between HF and HR was the same regardless of whether RR or IHR was applied-correlation coefficients did not statistically differ. The correlations of HR with LF/HF and nLF were significantly positive but with nHF significantly negative in all cases (RR, IHR or corrected RR, IHR). The application of corrected signals yielded very similar results despite different signal origins (RR or IHR). Conclusions: Absolute values of TP and LF may exhibit different relationships with HR depending on whether RR or IHR is applied. HRV parameters calculated from corrected signals, nLF, nHF and LF/HF reveal a consistent relation with HR regardless of whether RR or IHR is used. Thus, the application of correction or normalization procedures removes problems related to the non-linear relationship between R-R intervals and heart rates.
The Effect of Age on Shortñterm Heart Rate Variability
1999
The aim of the study is to establish age dependence of the indices of short-term spectral analysis of HRV in the supine and standing position. We tested 132 healthy subjects (76 man and 56 woman) divided into two age groups: 12-24 years (n=62) and 43-70 years (n=70). Total spectral power was divided into three components: high frequency (HF) (150-500 mHz), low frequency (LF) (50-150 mHz) and very low frequency (VLF) (20-50 mHz). There were significantly lower values for total spectral power, indices calculated from the LF and HF components and mean frequencies of the VLF and HF components in the older group than in the younger group in the supine position. On the other hand, the values for indices %VLF, VLF/LF, VLF/HF and LF/HF were significantly higher in the older group. The differences between supine and standing values were either significantly less in the older group than in the younger group or did not exhibit a remarkable variation; the sole exception being the change in total spectral power which was significantly greater in the older group.
Effect of Aging on Short Term Heart Rate Variability
Journal of Bangladesh Society of Physiologist, 2015
Background: Heart rate variability has been considered as an indicator of autonomic nerve function status. Few works have been done to assess the heart rate variability in normal healthy subjects in different countries. Objectives: To assess the cardiac autonomic nerve function status in healthy Bangladeshi population of different age groups by analyzing time domain measures of Heart Rate Variability. Methods: This cross sectional study was conducted in the Department of Physiology, Dhaka Medical College, Dhaka from the period of July 2012 to June 2013. For this purpose, a total number of 180 healthy subjects were selected with the age ranging from 15-60 years of both sexes. All the study subjects were divided into 3 different groups according to age (Control 15-30 years; middle age 31-45 years; older age 46-60 years). Each group included 60 subjects of which 30 were male and 30 were female. The subjects were selected from different areas of Dhaka city by personal contacts. HRV para...
The Effect of Age on Short – Term Heart Rate Variability
1999
The aim of the study is to establish age dependence of the indices of short-term spectral analysis of HRV in the supine and standing position. We tested 132 healthy subjects (76 man and 56 woman) divided into two age groups: 12–24 years (n=62) and 43–70 years (n=70). Total spectral power was divided into three components: high frequency (HF) (150–500 mHz), low frequency (LF) (50–150 mHz) and very low frequency (VLF) (20–50 mHz). There were significantly lower values for total spectral power, indices calculated from the LF and HF components and mean frequencies of the VLF and HF components in the older group than in the younger group in the supine position. On the other hand, the values for indices %VLF, VLF/LF, VLF/HF and LF/HF were significantly higher in the older group. The differences between supine and standing values were either significantly less in the older group than in the younger group or did not exhibit a remarkable variation; the sole exception being the change in tota...
Heart Rate Variability Fraction-A New Reportable Measure of 24-Hour R-R Interval Variation
Annals of Noninvasive Electrocardiology, 2005
Background: The scatterplot of R-R intervals has several unique features. Its numerical evaluation may produce a new useful index of global heart rate variability (HRV) from Holter recordings. Methods: Two-hundred and ten middle-aged healthy subjects were enrolled in this study. The study was repeated the next day in 165 subjects. Each subject had a 24-hour ECG recording taken. Preprocessed data were transferred into a personal computer and the standard HRV time-domain indices: standard deviation of total normal R-R intervals (SDNN), standard deviation of averaged means of normal R-R intervals over 5-minute periods (SDANN), triangular index (TI), and pNN50 were determined. The scatterplot area (0.2-1.8 second) was divided into 256 boxes, each of 0.1second interval, and the number of paired R-R intervals was counted. The heart rate variability fraction (HRVF) was calculated as the two highest counts divided by the number of total beats differing from the consecutive beat by <50 ms. The HRVF was obtained by subtracting this fraction from 1, and converting the result to a percentage. Results: The normal value of the HRVF was 52.7 ± 8.6%. The 2-98% range calculated from the normal probability plot was 35.1-70.3%. The HRVF varied significantly with gender (female 48.7 ± 8.4% vs male 53.6 ± 8.6%, P = 0.002). The HRVF correlated with RRI (r = 0.525) and showed a similar or better relationship with SDNN (0.851), SDANN (0.653), and TI (0.845) than did the standard HRV measures with each other. Bland-Altman plot showed a good day-by-day reproducibility of the HRVF, with the intraclass correlation coefficient of 0.839 and a low relative standard error difference (1.8%). Conclusion: We introduced a new index of HRV, which is easy for computation, robust, reproducible, easy to understand, and may overcome the limitations that belong to the standard HRV measures. This index, named HRV fraction, by combining magnitude, distribution, and heart-rate influences, might become a clinically useful index of global HRV.
Age and Sex Differences in Heart Rate Variability and Vagal Specific Patterns – Baependi Heart Study
Global heart, 2020
Background: Heart rate variability (HRV) is a noninvasive method for assessing autonomic function. Age, sex, and chronic conditions influence HRV. Objectives: Our aim was to evaluate HRV measures exploring differences by age, sex, and race in a sample from a rural area. Methods: Analytical sample (n = 1,287) included participants from the 2010 to 2016 evaluation period of the Baependi Heart Study, a family-based cohort in Brazil. Participants underwent 24-hour Holter-ECG (Holter) monitoring. To derive population reference values, we restricted our analysis to a 'healthy' subset (i.e. absence of medical comorbidities). A confirmatory analysis was conducted with a subgroup sample that also had HRV derived from a resting ECG 10'-protocol obtained during the same time period. Results: The 'healthy' subset included 543 participants. Mean age was 40 ± 14y, 41% were male, 74% self-referred as white and mean body-mass-index was 24 ± 3kg/m 2. Time domain HRV measures showed significant differences by age-decade and by sex. Higher values were observed for males across almost all age-groups. Parasympathetic associated variables (rMSSD and pNN50) showed a U-shaped distribution and reversal increase above 60y. Sympathetic-parasympathetic balance variables (SDNN, SDANN) decreased linearly by age. Race differences were no significant. We compared time domain variables with complete data (Holter and resting ECG) between 'healthy' versus 'unhealthy' groups. Higher HRV values were shown for the 'healthy' subset compared with the 'unhealthy' group. Conclusion: HRV measures vary across age and sex. A U-shaped pattern and a reversal increase in parasympathetic variables may reflect an age-related autonomic dysfunction even in healthy individuals that could be used as a predictor of disease development.
PLOS ONE, 2016
The dynamical fluctuations in the rhythms of biological systems provide valuable information about the underlying functioning of these systems. During the past few decades analysis of cardiac function based on the heart rate variability (HRV; variation in R wave to R wave intervals) has attracted great attention, resulting in more than 17000-publications (PubMed list). However, it is still controversial about the underling mechanisms of HRV. In this study, we performed both linear (time domain and frequency domain) and nonlinear analysis of HRV data acquired from humans and animals to identify the relationship between HRV and heart rate (HR). The HRV data consists of the following groups: (a) human normal sinus rhythm (n = 72); (b) human congestive heart failure (n = 44); (c) rabbit sinoatrial node cells (SANC; n = 67); (d) conscious rat (n = 11). In both human and animal data at variant pathological conditions, both linear and nonlinear analysis techniques showed an inverse correlation between HRV and HR, supporting the concept that HRV is dependent on HR, and therefore, HRV cannot be used in an ordinary manner to analyse autonomic nerve activity of a heart.