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Cardiovascular Research 1997 35(1):35-42; doi:10.1016/S0008-6363(97)00107-7
© 1997 by European Society of Cardiology
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Copyright © 1997, European Society of Cardiology

Effect of age on long-term heart rate variability

Vikram K Yeragani*, Edward Sobolewski, Jerald Kay, V.C Jampala and Gina Igel

Veterans Affairs Medical Center and the Wright State University, School of Medicine, Dayton, OH, USA

* Corresponding author: 116-A, VAMC, 4100 West Third Street, Dayton, OH 45428, USA. Tel.: +1 (937) 268-6511, ext. 1260; fax: +1 (937) 267-3924.

Received 20 November 1996; accepted 18 April 1997


   Abstract
Top
 Abstract
1 Introduction
 2 Methods
 3 Statistical analysis
 4 Results
 5 Discussion
 References
 
Objective: Previous studies on short-term time series of heart rate suggest an inverse relationship between age and spectral powers of heart rate variability in various frequency bands. In this study, we examined the relationship between age (6–61 years) and long-term heart rate variability. Methods: We obtained 24-h Holter ECG in 33 healthy human subjects (11 children and 22 adults). The heart rate data were analyzed by using spectral analysis and fractal dimensions of the time series. Results: We found a significant negative correlation between age and very low frequency (VLF, 0.0033–0.04 Hz), low frequency (LF, 0.04–0.15 Hz) and high frequency (HF, 0.15–0.5 Hz) powers and fractal dimensions during awake as well as sleep periods, and a positive correlation between age and LF/HF ratios. Age and ultra-low frequency (ULF, <0.0033 Hz) were modestly and negatively correlated only during the awake period. Conclusions: Sleep ULF power is not significantly affected by age, whereas VLF, LF and HF powers and fractal dimensions of heart rate significantly decrease with age during awake as well as sleep periods.

KEYWORDS Human; Holter ECG; Heart rate variability; Spectral analysis; Age; Sympathetic nervous system


   1 Introduction
Top
 Abstract
 1 Introduction
2 Methods
 3 Statistical analysis
 4 Results
 5 Discussion
 References
 
Heart period (R-R) or heart rate (HR) variability is an important non-invasive tool to study cardiac autonomic function [1, 2]. Several studies on R-R variability dealing with prognosis after myocardial infarction suggest that a decrease in R-R variability especially in the ultra-low-frequency range (ULF, <0.0033 Hz) is associated with significant cardiovascular morbidity and sudden death [3–5]. Abnormalities of HR variability have been described in several conditions such as heart disease, diabetes, central nervous system disorders, fetal anoxia and anxiety disorders such as panic disorder [6–10]. Age has a strong influence on short-term heart rate variability, which should be considered in the interpretation of HR variability data comparing diseased and normal populations [11–13].

Spectral analysis of short-term (about 5 min) HR time series reveals 3 peaks; high frequency (HF, 0.15–0.5 Hz) related to respiration, low frequency (LF, around 0.1 Hz), related to Mayer wave sinus arrhythmia, and very low frequency (VLF, 0.01–0.05 Hz), related to thermoregulatory and peripheral vascular mechanisms [14–16]. Several non-linear techniques have also been used to analyze the heart rate time series due to the non-linear nature of these time series [17–19]. Approximate entropy measures the logarithmic likelihood that runs of patterns that are close remain close on next incremental comparisons and the higher the entropy value, the more random the time series [20]. There are several different algorithms to compute fractal dimension (FD) [21–25]. This specifically measures the space-filling propensity and complexity of the time series.

With increasing age, there is generally a decrease in respiratory sinus arrhythmia (RSA), which is related to HF power in HR variability [11–13]. There is almost a linear decrease of RSA from 20–80 years of age. Shannon et al. found that the HF power declined linearly in supine posture in subjects, 9–28 years of age [11]. Schwartz et al. reported similar findings [12]. Our previous study on short-term HR variability using postural challenge in the age group of 4–43 years showed significant negative correlations between age and supine VLF, LF and HF powers, and standing HF power [13]. The purpose of the present study is to address the effect of age on long-term HR variability as measured by FD and spectral analysis, especially on the ULF power, and also on sleep ULF, VLF, LF and HF powers.


   2 Methods
Top
 Abstract
 1 Introduction
 2 Methods
3 Statistical analysis
 4 Results
 5 Discussion
 References
 
Thirty-three normal subjects [18 females and 15 males; age: 27.8±14.3 years (mean±s.d.)] participated in this study. The adult group consisted of 12 females and 10 males (35.4±10.4 years) and the children's group, 6 females and 5 males (11.1±2.1 years). This study was approved by the Institutional Review Board at the Wright State University School of Medicine, Dayton, OH. A signed informed consent was obtained from participants. The assent of the children was obtained prior to obtaining the informed consent from their parents. The subjects were physically healthy with no history of hypertension, and their routine blood chemistry and ECG did not show any significant abnormalities. These subjects were not taking any medication at least for 4 weeks prior to the studies except for occasional non-opioid analgesics. Among the adult group, two controls were smokers. This investigation confirms with the principles outlined in the Declaration of Helsinki.

2.1 Data collection
Holter records were obtained by using Delmar Cardiocorders over a 24-h time period using standard procedures. Subjects were asked to keep a diary of various activities during the procedure, most importantly when they went to sleep and when they woke up.

2.2 Analysis of the data
The Holter records were scanned using the Delmar 750 A system by one of the investigators. The ECG was sampled at 256 Hz for the analyses. The R-R (interbeat) interval data were downloaded to a PC using a custom-developed program. These data were edited using software which eliminated any glitches due to premature ventricular beats. We used a similar method to the one used by Huikuri et al. [26]. An R-R interval was interpreted as a premature beat if it deviated from previous qualified interval value by more than a tolerance level of 30%. These beats were eliminated and the resulting gaps were filled with an average value in the immediate neighborhood. All data sets had more than 95% qualified beats. The edited time series data were later sampled at 2 Hz using the method of Berger and co-workers [27]to obtain instantaneous HR. This step-wise continuous instantaneous HR signal maintains an amplitude equal to the reciprocal of the current R-R interval and the convolution of the HR signal with the rectangular window has the effect on the power spectrum of multiplication by a low-pass filter [27]. Using a 2 Hz sampling rate would allow an accurate estimation of the power spectrum up to 0.5 Hz. The data were then detrended using a linear detrending technique prior to the computation of spectral analyses and FDs.

From these 24-h time series of instant HR data, we analyzed a continuous time series of 72 000 s (20 h), and also 20 000 s (5 h and 33 min) each of HR data during the awake and sleep conditions. All subjects except one child had more than 20 h of artifact-free data. Awake and sleep data were available for all subjects. As there is a very significant correlation between the mean heart period (HP) and total power, we chose to use HR time series rather than HP time series. These HR time series sampled at 2 Hz were used to perform spectral analysis and to calculate FD values.

2.3 Spectral analysis
The power spectrum was obtained as the magnitude squared of the Fourier transform using a rectangular data window. The powers were integrated in the following frequency bands; ULF <0.0033 Hz; VLF 0.0033–0.04 Hz; LF 0.04–0.15 Hz; HF 0.15–0.5 Hz. Relative powers were calculated as the percentages of total power in each frequency band.

2.4 Fractal dimension (FD)
We used the same method as that used in our previous study to compute FD [25]. The following is a brief description of this method as stated by Katz and George [22]:


Formula

where D is the fractal dimension, L is the total length of the curve, a is the average step length (a=L/n, where n is the total number of steps), K = 2/{pi} and A is the area of the circle potentially filled by an ideal random walk. From this equation, the following is obtained after removing the constant, K from the formula:


Formula

where L is the sum of distances between successive points, d is the planar extent of the curve, which is the farthest distance between starting point and the ith point of the time series, and n the number of steps in the curve.

The fractal dimension (FD) of a planar curve is defined as follows:


Formula

where L is the total length of the curve and d is the diameter (planar extent) of the curve. As suggested by Katz, we adopted the formula:


Formula

where n is the total number of steps in the curve (total number of points – 1), and a the average distance between successive points. We followed the same method of computation suggested by Katz [28], using a customized software program. We calculated FDs for the 20-h data and also the awake and sleep periods (20 000 s) for all the subjects.

We obtained FDs for the 20 000 second awake and sleep time series using unfiltered time series, and also filtered time series for the following bands: ULF, using a Lowpass filter; VLF, using a Bandpass filter with center frequency of 0.018 Hz; LF, using a Bandpass filter with center frequency of 0.1 Hz; and HF, using a Highpass filter. These techniques are similar to those we used to filter the time series of HR in our previous study [25].


   3 Statistical analysis
Top
 Abstract
 1 Introduction
 2 Methods
 3 Statistical analysis
4 Results
 5 Discussion
 References
 
We used BMDP statistical software (Berkeley, CA, USA) to perform the analyses. The main outcome measures of this study were FD and spectral powers in ULF, VLF, LF and HF bands. We performed linear regressions using age as the independent variable and the measures of spectral powers and FD as the dependent variables. We used both absolute and relative powers. We used two-tailed tests and a probability level of 0.05 for significance. We also performed ANOVA to compare males and females with regard to the HR variability measures to find out if there is a significant effect of sex.


   4 Results
Top
 Abstract
 1 Introduction
 2 Methods
 3 Statistical analysis
 4 Results
5 Discussion
 References
 
Table 1Table 2 show the HR data for the 20-h and awake and sleep periods. Children have higher HR and higher VLF, LF and HF powers and FDs of HR. LF/HF ratios were lower in children than in adults. Figs. 1–4GoGoGo summarize the findings of the study. For the total spectral power, 20-h and sleep time series of HR were not significantly related to age (Fig. 1). However, FD of 20-h, awake and sleep HR significantly and negatively correlated with age; 20-h VLF, LF and HF were negatively correlated with age. On the contrary, ULF power did not significantly correlate with age (Fig. 2).


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  		Table 1 Measures of 20-h heart rate variability in children and adults

 

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  		Table 2 Measures of awake and sleep HR variability measures in children and adults

 

Figure 1
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  		Fig. 1 Linear regressions of age and total powers and fractal dimensions during 20-h, awake and sleep periods.

 

Figure 2
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  		Fig. 2 Linear regressions of age and ULF, VLF, LF and HF powers for the 20-h period.

 

Figure 3
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  		Fig. 3 Linear regressions of age and ULF, VLF, LF and HF powers for the awake and sleep periods.

 

Figure 4
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  		Fig. 4 Linear regressions of age and HR, and LF/HF ratios, and HR and total power during sleep and awake periods.

 
For the awake and sleep periods, ULF, VLF, LF and HF powers were all significantly and negatively correlated with age with the exception of sleep ULF power (Fig. 3). Total power did not correlate significantly with mean HR during either awake or sleep periods. However, mean HR correlated significantly and negatively with age. Awake and sleep LF/HF ratios were also significantly and positively correlated with age (Fig. 4).

Mean heart period (R-R interval) had a highly significant positive correlation with the total power of heart period (r = 0.75,0.71, and 0.70 for the 20-h, awake and sleep periods, respectively). As mentioned in Section 2, this is the main reason for using HR instead of heart period as the preferred metric in this study due to the significantly higher HR in children.

Table 3 shows the correlations of age with relative powers for the 20-h, awake and sleep data. Sleep and 20-h ULF power increased with age while sleep and 20-h VLF power decreased with age significantly. Fig. 5 shows the awake time series (20 000 s) of a child and adult with the lowest and the highest FD values. The FDs computed on non-detrended time series correlated highly with the FDs of detrended series (0.99–1).


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  		Table 3 Correlation of age with relative powers of HR variability

 

Figure 5
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  		Fig. 5 Unfiltered time series of HR with FD values for an adult (above) and a child (below) during awake period (20 000 s).

 
4.1 Correlation of age with FD of filtered time series
The correlations for the awake ULF, VLF, LF and HF series were –0.28, –0.66, –0.46 and –0.68 and for the sleep period, 0.23, –0.57, –0.34 and –0.55, respectively.

4.2 Analyses using heart period
FDs of unfiltered series of HP did not correlate significantly with mean HP (awake, r = 0.30; sleep, r = 0.21).

4.3 Effect of sex
There were no significant differences between males and females for any of the HR variability measures.


   5 Discussion
Top
 Abstract
 1 Introduction
 2 Methods
 3 Statistical analysis
 4 Results
 5 Discussion
References
 
The present findings suggest a significant inverse relationship between age and spectral VLF, LF and HF powers during awake as well as sleep periods. This is similar to previous findings on HR variability and aging. However, age did not influence the ULF power during sleep. This probably accounted for the lack of effect of age on 20-h ULF power also. In the study by Bigger et al. [4], age was correlated with R-R variability in healthy persons aged 40–69 years. Their findings showed a significant decrease of VLF, LF and HF with age. There was no significant influence of age on ULF power. Our findings are similar to the study of Bigger et al [4]though the age range in our study is 6–61 years. However, we found a significant but a modest decrease of awake ULF power with increasing age while sleep ULF power did not show such a relationship. These findings are important because of the association of increased cardiovascular morbidity and decreased ULF power. There was no significant relationship between HR and total power, suggesting that the increased HR in children may not have accounted for these findings. Though it is not very clear as to the mechanisms of VLF and ULF power, one study suggests that the renin–angiotensin system modulates these frequencies [29]. Captopril, an ACE inhibitor, appears to increase the VLF and ULF power in patients with acute myocardial infarction [29].

There was also an increase of LF/HF ratios with increasing age, findings similar to our previous study. This suggests that there may be a relative increase in sympathetic influence on HR variability with increasing age. However, there was no increase in absolute LF power with age. The increase in LF/HF ratios probably reflects a steeper decline in HF power with age. The decrease in HR with age also does not support an increase in absolute sympathetic function with increasing age. The decrease in HR with increasing age may be due to several other mechanisms and we are unable to explain it with the current data.

Several studies stressed the importance of the non-linear nature of HR time series and the possible advantage of applying the analyses of fractal dimension and approximate entropy to quantify the complexity of these time series [17–20]. There are several algorithms to compute fractal dimensions such as those for capacity dimension, information dimension, correlation dimension and the Lyapunov dimension [21]. Different techniques may yield different information about the time series. In this study, we used the technique proposed by Katz and George, which measures the space-filling propensity of the time series [22]. This formula was derived from Mandelbrot's original work in this area [23, 24]. The higher the value, the more convoluted the signal. Though 20-h total power was not influenced by age, the FD significantly decreased with increasing age. This is because the FD values using the present algorithm are mainly influenced by the HF content of the time series, thus reflecting cardiac vagal function. However, HF power accounts only for a small fraction of the total power unlike the ULF power which contributes the most to the total power. We have not used approximate entropy (APEN) in this study because this measure correlates highly significantly with FD (0.96–0.99) [25]. It is also much more time-consuming to compute APEN for data segments this long. As both these measures are strongly influenced by the HF content of the time series, one should also use caution in equating total spectral power with measures such as FD or APEN. The possible reason for the HF power to influence the FD values is the contribution of these frequencies to the complexity of the time series. The FD values were the lowest for the ULF time series, and highest for the HF time series, and the FDs of unfiltered time series correlated highly significantly with the FDs of HF time series (r>=0.97). Even when we used non-detrended data for the computation of FD, these values were similar to those using detrended data since this procedure mainly affects the ULF power. We did not also find any specific advantages of using FD in this study compared to the linear measures when we correlated age with the FDs of filtered time series predominantly reflecting ULF, VLF, LF and HF components. We also did not find any significant change in FDs between awake and sleep periods though the total power significantly decreased during sleep. This again is most likely due to the contribution of ULF power toward most of the total power.

In this study, we did not find a significant effect of sex on HR variability measures. However, our sample sizes are rather small. Yamasaki et al. [30]have reported that male subjects had higher relative LF power compared to females between 20 and 78 years of age using Holter records. In another recent study [31], Ryan et al. found that HF power and HR APEN were higher in women in the age group of 20–90 years. Liao et al. also reported similar findings using short-term HR time series in a population-based study [32]. Respiratory rate and tidal volume certainly have an effect on the HR power spectrum, but we are unable to comment on this aspect as our subjects did not have respiratory monitoring during the study [33]. Hyperventilation decreases HF power and deep breathing has the opposite effect [34]. Postural change from supine to standing is associated with an increase in LF power and a decrease in HF power, which appears to be due to vagal withdrawal and increased sympathetic function [35, 36]. The decrease in ULF, VLF and LF powers during sleep is to some extent due to the recumbent posture. It should also be noted that different sleep stages have different effects on sleep [37]. When falling asleep, the LF power significantly decreases while the HF power increases. The morning awakening is associated with a decrease in HF power and an increase in LF power. We are unable to comment on the effects of different sleep stages in the present study due to the lack of EEG data.

The findings of this study further emphasize the need for age-dependent norms for the assessment of HR variability. As the sleep HR ULF power does not appear to be significantly associated with age, this may be a more useful prognostic measure to predict significant cardiovascular morbidity.

Time for primary review 21 days.


   Acknowledgements
 
This study was supported in part by NIMH grant RO1 MH50752 to V.K.Y.


   References
Top
 Abstract
 1 Introduction
 2 Methods
 3 Statistical analysis
 4 Results
 5 Discussion
 References
 

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