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Age-adjusted normal confidence intervals for heart rate variability in healthy subjects during head-up tilt
InternationalJournalof Cardiology50(1995)117-124
ELSEVIER
Age-adjusted normal confidence intervals for heart rate variability in healthy subjects during head-up tilt Gianfranco Piccirillo*, Filippo L. Fimognari, Emanuela Viola, Vincenzo Marigliano Cattedra
di Geriatria,
Istituto
di I Clinica
Medica.
Policlinico
Umberro
I, Universita
di Roma
“LA Sapienza”,
00161
Rome, Italy
Received16January1995;revisionaccepted 5 April 1995
Abstract Purpose: Aging leadsto a declinein autonomicnervoussystemfunction. In this study, designedto assess the influenceof ageon neuroautonomicregulationof cardiacactivity, heart rate variability wasmeasuredby power spectral analysisand normalrangesweredeterminedin a population of healthy subjects.Patients and methoak In 83 healthy volunteers(42 men and 41 women;age range 25-85 years) autonomic nervoussystemfunction was assessed by autoregressive spectralanalysisof heart rate variability in clinostatismand after passiveorthostaticload (head-uptilt). The analysisconsideredtwo spectralcomponentsrelevant to the study of the autonomicnervoussystem- highfrequencypower (= 0.25Hz) and low-frequencypower (= 0.10 Hz) - and the ratio betweenthem. Low-frequency spectralcomponents,in particular the ratio betweenlow- andhigh-frequencyspectra,reflectsympatheticactivity; highfrequencycomponentsreflect parasympatheticactivity. Results: For data comparison,the study groupwassubdivided into three agegroups:25 subjects(12 menand 13 women)under 44 yearsof age; 28 (15 men and 13women)aged 44-64 years;and 30 (15 menand 15 women)over 64 yearsof age.The natural logarithmsand normalizedunits of low- and high-frequencypower, and the low-to-high power ratio were usedto calculate95% confidenceintervals. Powerspectralanalysisat baselineand after posturaltilt showedsignificantly higher low-frequencypower of heart rate variablity (P < 0.05), natural logarithm of power (P < 0.001)and normalizedunits (P < 0.001)in the two youngergroupsthan in the oldestgroup. The two youngerage-groupsalsohad significantlyincreasedhigh-frequency power(P < 0.05)and natural logarithm of power (P < 0.05).The oldestagegroup had signifrcantlyincreasedhighfrequencypower analyzedin normalizedunits (P < 0.001).Conclusion: The age-relatedloweringobservedin nearly all the spectralfrequencycomponentsof heart rate variability confirmsin healthy subjectsthat autonomicnervous systemfunction declineswith age.
Keywork Autonomic nervoussystem;Aging; Sympatheticnervoussystem;Powerspectralanalysis;Head-uptilt
1. Introduction
* Corresponding author,Tel.:+3964463301-2-3; Fax:+396 4940594.
Ample evidence has now confirmed spectral analysis of heart rate variability as a valid method for exploring autonomic nervous system function
0167-5273/95/$09.50 @ 1995ElsevierScience IrelandLtd. All rightsreserved SSDI 0167-5273(95)02351-V
118
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[ll. It has the notable advantage of being a noninvasive test that is simple to perform. The procedure consists of an electrocardiographic recording, measurement of 512 electrocardiographic RR intervals and a spectrum analysis by means of an autoregressive algorithm. It permits the distinction of three main oscillation bands: a high-frequency band (HF), (-0.25 Hz); a low-frequency band (LF) (- 0.10 Hz) and a very-low frequency band (VLF) (0.0033-0.04 Hz). HF and LF powers reflect autonomic nervous system activity (2-51. HF power is thought to arise from parasympathetic activity, LF power from combined parasympathetic and sympathetic activity. The consensus is that the ratio between low- and highfrequency powers (LF:HF) reflects sympathetic activity [6]. Aging induces a decline in autonomic nervous system function [7-lo]. We assessed the influence of age on cardiovascular neuroautonomic regulation in a spectral analysis of heart rate variability with the specific aim of defining the normal ranges in healthy subjects. Power spectra were analyzed in all subjects at rest and after sympathetic stress induced by passive head-up tilt [2,5,6,11,12]. 2. Patients and methods 2.1. Subjects and study protocol
Eighty-three healthy volunteers (42 men and 41 women), whose ages ranged from 25 to 85 years took part in the study. Criteria for exclusion included diastolic blood pressure 195 mmHg; systolic blood pressure 1 160 mmHg; body-mass index > 26 (kg/m*); smoking (> 5 cigarettes per day); diabetes (presence of glucosuria or fasting glycemia > 120 mg/dl or 110 mg/dl at 2 h after glucose loading); cholesterolemia 1 220 mg/dl; and a history or demonstrable evidence of cardiovascular, respiratory, renal (presence of proteinuria and creatinine > 1.5 mg/dl); hepatic, gastrointestinal, or systemic disease. In addition, a complete history was obtained and all subjects underwent a full clinical examination followed by electrocardiography at baseline and during stress; echocardiography; and a two-dimensional echo Doppler study of the neck vessels. For purposes of
Journal of Cardiology 50 (1995) 117-124
comparison the subjects were divided into three age groups: Group 1, under 44 years; Group 2, 44-64 years; and Group 3, over 64 years. All participants gave their informed consent to the study, which was approved by the hospital ethical committee. None of the subjects had taken medicinal or other drugs during the 3 months before the study. 2.2. Power spectral analysisof heart rate variability and passive head-up tilt
Spectral analysis took place according to the following protocol: at 08:OOh, after blood pressure measurement, in a quiet, comfortable room at a temperature of 24’C, each subject rested supine for at least 30 min before undergoing a 15-min electrocardiographic recording. Subjects then underwent head-up tilt testing, a passive orthostatic maneuver obtained with a motorized tilt table. After 15 min upright (90”) the subject was returned to the supine position (0”) for a second 15-min electrocardiographic recording. Transit from 0” to 90’ took about 15 s. If hypotension (a systolic blood-presssure change of > 20 mmHg) developed during postural tilt, testing was stopped and the subject was excluded from the study. Tracings were analyzed with a custom-designed software program running on an IBM-compatible computer with a 486 microprocessor. Electrocardiographic signals were digitized, stored on hard disk and sampled at a rate of 500 Hz, with 12 precision bits. The QRS complex (lead II) was automatically recognized by a classic derivative/ threshold algorithm. An expert cardiologist subsequently checked each QRS complex and the triggering R wave. Ectopic beats were corrected for linear interpolation with the adjacent complexes. Electrocardiographic tracings with > 1% premature beats were eliminated from the analysis. Power spectra were calculated from a consecutive series of 512 RR intervals. The power spectral densities of the baseline and tilt recordings were computed by an autoregressive algorithm developed in our laboratory. The autoregressive algorithm for spectral analysis has been described in detail elsewhere [2,13- 161. Spectral power was expressed in ms*.
G. Piccirillo
et al. /International
Journal
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50 (1995)
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119
1200
REST T VLF LF HF
6.05
POUER:913 WUER:429 POUEft:271 ~@~m:213
6.18 0.16
0.21
NUsS NUs:44
0.26
0.31
688
0.36
Fig. 1. Changes in the power spectrum in a normal subject during passage from recumbency to orthostatism. On the left the baseline spectrum in the subject at rest; on the right, post-tilt spectrum obtained after 15 min of passive head-up tilt. PSD, power spectral density; T, total; VLF, very low frequency (0.0033-0.4 Hz); LF, low frequency (0.04-O. 15 Hz); HF, high frequency (0. IS-O.40 Hz); NUs, normalized units.
Autonomic nervous system function was evaluated from two spectral components: one at high frequency (0.16-0.40 Hz Eq) and the other at low frequency (0.04-o. 15 Hz Eq) (Fig. 1) [ 1, 4- 111. Spectral data were also reported as total power (TP) (Fig. 1) (0.0033-0.4 Hz Eq) and verylow-frequency (VLF) power (Fig. 1). Unlike the other two spectral components, VLF is not detectable in all subjects [6,17]. So that the skewed distribution characteristic of spectral data could be avoided, data were transformed into natural logarithm (In) and into normalized units (NUs) [18]. Transformation of data into normalized units also helped to accentuate sympatho-vagal activity. Normalized units were calculated as follows: LF NUs = LF power/(TP - VLF power) x 100); HF NUs = HF power/(TP - VLF power) x 100. The final index calculated was the ratio between LF and HF powers (LF:HF). During and after headup tilt arterial blood pressures were monitored with a noninvasive volume-clamp device FINAPRES (Omeda). 2.3. Statistical analysis
Data are expressed as means i 1 standard error. Variables such as the subjects’ general characteristics (age, weight, and body-mass index); blood pressure; heart rate; LF and HF NUs; In of VLF, LF and HF; at baseline and after tilt in the
three age ranges were compared by means of an analysis of variance (ANOVA) and the Bonferroni’s test. Student’s paired t-test was used to analyze changes in variables that had a linear distribution (arterial pressures, median RR intervals, LF and HF NUs, and In of VLF, LF and HF). For nonlinear data, including TP, VLF, LF and HF power, the Wilcoxon rank-sum analysis was used for comparison between data from the three groups before and after tilt and the Kruskal-Wallis test and Mann-Whitney U-test were used for comparison between groups. Because the LF:HF ratio had a linear distribution at baseline but a nonlinear distribution after tilt, the baseline results in the three groups were compared by ANOVA and the Bonferroni test; whereas changes in the data obtained before and after tilt were compared by the Wilcoxon rank sum test. The KruskalWallis test and Mann-Whitney U-test were used to compare the LF:HF ratio in the three groups after tilt. The 95% confidence intervals were calculated for data with a linear distribution. Linear regression was applied to determine a relation between variables, and the correlation coefficient between these variables was calculated. A P-value < 0.05 was considered to indicate statistical significance. 3. Results
Of the 102 healthy volunteers (53 men and 49 women) originally enrolled in the study, 19 (10
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Table 1 Basal characteristic in three groups of subjects Age ~44 years Group 1
Age 44-64 years Group 2
Journal
of Cardiology
25 12/13 30.0 f 174.1 f 70.2 f 23.2 zt
Age ~44 years Group 1
Age >64 years Group 3
28 15113 30 15/15 0.9 54.8 f 1.1 71.7 * 6.4 170.2 & 10.3 169.2 f 3.6 69.3 zt 6.4 67.0 zt 1.6 24.0 zt 1.3 23.5 zt
RR (ms)
1.1 12.5 4.5 1.5
Table 2 Blood pressure, heart rate and heart rate variability Age ~44 years Group 1
Age 44-64 years Group 2
Age >64 years Group 3
126.6+ (9.7) 80.0+
130.8+ (13.4) 79.9 (10.1) 889.4 (130.1) 1625.3+ (101.8) 893.4 (86.6) 6.63 (0.15) 242.0++ (16.7) + $8) 32.8++ (1.4) 0.1 0.01 489.8+ (22.9)
Rest
SBP (mmHg)
114.8#** (11.4) DBP (mmHg) 74 o+*p (9.9) 907.8+ RR (m.9 (155.8) Total power (ms2) 2883.95 (356.1) VLF power (ms2) 1124.2 (158.7) VLF In (tit) LF power (ms2) 747.41 (120.8) LF In 6.3@ (0.1) LF NUs 47 9++@ (2.6) 0.1 CF (Hz) 0.01 HF power (ms2) 938.0++9 (233.9) + HF In &) HF NUs 51 o++@ (2.7) 0.24 CF (Hz) 0.05 1.1++gg LFIHF (0.11)
(6.1)
853.0 (149.4) 2081.7+” (145.4) 860.7 (67.9) 6.29 (0.34) 524.7# (56.9) nn (z9) 45.5+* (2.5) 0.1 0.01 696.2++” (112.9) 6.3++ (0.15) 53 8++# (2.4) 0.22 0.06 1 I+* (0.28)
(tZ5) 66.7++ (1.4) 0.25 0.08 0.5++ (0.03)
Tilt
SBP (mmHg)
1114**@ (23.5)
133.3 (16.6)
117-124
Table 2 (continued)
DBP (mmHg) :ex (M/F) Age (years) Height (cm) Weight (kg) BMI (kg/m2)
50 (1995)
136.0 (13.7)
Age 44-64 years Group 2
Age >64 years Group 3
78.0*#
83.9
83.7
(8.0)
(6.8)
(6.8)
824.1 (169.7) Total power (ms2) 2224.2* (177.2) VLF power (ms2) 1127.1 (122.1) VLF In (fK) LF POWER (ms2) 805.85 (133.7) LF In 6.4$ (0.13) LF NUs 77.41 (3.5) CF Hz 0.1 0.01 HF power (ms2) 291.3s (72.7) HF In 4.9 (0.29) HF NUs 22.81 (3.5) CF (Hz) 0.26 (0.06) LFlHF IO.91 (3.6)
815.3 (127.2) 1669.3 (143.1) 922.8 (89.8) (,4;‘, 582.3’ (66.1) (if) 75.5”
(1.8)
0.1 0.01 181.5”” (25.1) (0%) 23.5* (1.7) 0.23 (0.05) 3.8” (0.4)
861.2 (114.1) 1864.3 (136.9) 1060.7 (121.9) 6.7 (0.15) 497.3 (116.5) (0s.c 50.0 (2.5) 0.1 0.01 409.3 (29.4)
(f23) 46.3 (3.2) 0.20 (0.07) (:b:
Rest, baseline conditions; Tilt, 15 mitt after 15 mm of head-up tilt testing; SBP, systolic blood pressure; DBP, diastolic blood pressure; median RR, median RR intervals; TP total power; VLF power, very-low-frequency power; VLF In, natural logarithm obtained from very-low-frequency power; LF power, low-frequency power; LF In, natural logarithm obtained from low-frequency power; LF NUs, low-frequency normalized units; HF power, high-frequency power; HF In, natural logarithm of high-frequency power; HF NUs, high-frequency normalized units; LFHF, the ratio between low-frequency and high-frequency spectra; CF, center frequency. Note that the mean VLF In during rest in Group 3 was higher than that of the other two groups, in particular it was higher than that of Group 1, even though Group 1 had higher mean VLF power. This apparent contradiction results from the widely dispersed VLF power values and from the data flattening determined by log tranformations. ++P c 0.001: Rest vs. tilt; +P C 0.05: Rest vs. tilt. **P c 0.001: Group 1 vs. Group 2; *P < 0.05: Group 1 vs. Group 2. BP c 0.001: Group 1 vs. Group 3; SP c 0.05: Group 1 vs. Group 3. *P c 0.001: Group 2 vs. Group 3; #P < 0.05 Group 2 vs. Group 3.
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n q
REST TILT
0
LF NUs
. 50.
L p
~p
1
GROUP
~P<0001f
2
GROUP
3
Fig. 2. Changes in high-frequency spectral power analyzed in normalized units in the 83 subjects subdivided into three age-groups (Group 1: < 44 years of age; Group 2: 44-64 years; Group 3: >64 years) at baseline (REST) and after 15 min of passive head-up tilt (TILT). NUs LF, normalized units obtained from low-frequency (0.04-O. 15 Hz). Note the significant increase in NUs LF in all three age groups after tilt. &P c 0.001: Group I vs. Group 3; ##P < 0.001 Group 2 vs. Group 3.
men and 9 women) were excluded because they had a <80-mmHg fall in systolic blood pressure during tilt-testing. The 83 remaining subjects (42 men and 41 women, age range 25-85 years) had the following age distribution: 25 subjects belonged to the >44year age group; 28 to the 44- to 64-year group; and 30 to the >64-year group. The three groups had comparable sex distribution, height, weight and body-mass index (Table 1). Spectrum analysis at baseline showed that the two younger groups (< 44 years of age and from 44 to 64 years) had significantly higher TP of heart rate variability (P < 0.05) than the over 64-yearolds (Table 2). The two younger groups also had significantly greater LF power, LF In and NUs (Fig. 2) and a LF:HF ratio (P c 0.001) than the over 64-year-olds, with no significant difference between groups. The youngest group (< 44 years of age) and the middle-aged group (44-64 years of age) had significantly greater HF power of heart rate variability (P < 0.05) than the over 64-yearolds but the oldest group had significantly greater HF NUs than those of the two under 64-year-old groups. Postural tilt-testing caused a significant increase in LF NUs and LF:HF (P < 0.001) in all age groups but the two younger groups had significantly higher after-tilt LF NUs and LF:HF than the oldest group. (Table 2, Fig. 2).
Tilt-testing induced a decrease in baseline HF power in all three groups. In the two younger groups all components decreased significantly (HF power, P < 0.001; HF In P < 0.05; and HF NUs P < 0.001) (Table 2), whereas in the over 64-yearolds the decrease reached statistical significance only for HF power (P c 0.05) and HF NUs (P < 0.001). In addition, these components resulted significantly lower in the two younger groups (P c 0.001) (Table 2). Table 3 95% Confidence limits of power logarithm and normalized unit Age <44 years Group I
Age 44-64 years Group 2
Age >64 years Group 3
6.07-6.63 42.78-53.07 5.89-6.74
45.72-56.41 0.83-1.28
5.94-6.32 40.5-50.48 5.99-6.58 48.95-58.63 0.54-1.65
5.29-5.61 30.13-35.58 6.04-6.25 63.96-69.51 0.43-0.58
6.19-6.71 70.56-84.31
6.03-6.42
71.91-79.22
5.79-6.16 45.14-55.01 5.77-6.08 39.99-52.72 0.29-4.24
Rest
LF In LF NUs HF In HF NUs LF:HF Tilt
LF In LF NUs HF In HF NUs LF:HF
4.33-5.48
16.07-29.70 3.88-18.02
4.78-5.23 20.24-26.94 3.08-4.54
122
G. Piccirillo et al. /International Journal of Cardiology 50 (1995) 117-124
3, n=e3 a Rest I-: -8.571 p< 6.881
2,
i-‘ti r: -0.349
p< 8.881
1, J
18
28.
.3e
48
--se YEFIRS
68
R
88
-98’
Fig. 3. Highly significant inverse correlation between age and In LF (natural logarithm of low-frequency power, calculated at frequencies from 0.04 to 0.15 Hz) in normal subjects at baseline (REST) and after 15 min of head-up tilt (TILT).
Confidence intervals varied in the three age groups (Table 3). Baseline values of LF In, LF NUs, and LF:HF appeared to diminish with advancing age. The HF In also decreased with advancing age, the only exception being the lower limit in the over 64-year-old group, which only slightly exceeded that of the other two groups dur-
. . 0 I
.
ing rest but markedly exceeded it after tilt. Contidence intervals for baseline HF In had a far narrower range than those for the other tested spectral parameters of heart rate variability and also had the lowest lower limit. HF NUs and LF NUs behaved in an opposite manner, HF NUs increasing with advancing age. Because the under
.
.
. .
Fig. 4. Highly significant inverse correlation between age and NUs LF (normalized units from low-frequency power, calculated at frequencies from 0.04 to 0.15 Hz) in the 83 subjects at baseline (REST) and after 15 min of head-up tilt (TILT),
G. Piccirillo et al. /International Journal of Cardiology 50 (1995) 117-124
44-year-old group had dispersed, non-linear, LF:HF after tilt, their LF:HF ranged widely. This range tends nevertheless to diminish with age. Regression analysis showed a significant inverse correlation between age and the LF In (Fig. 3) and age and LF NUs (Fig. 4) during rest and after tilt. 4. Discussion Spectral analysis has recently been introduced also in the study of autonomic diabetic neuropathy, with interesting results [ 19-221. Because of the age-related decline in neuroautonomic function, however, comparative data are needed from studies in age-matched subjects. Our aim in this study was therefore to fultil this need by providing information that might be useful in the clinical assessment of neuroautonomic function. Many studies have described the effects of age on the spectral components of heart rate variability in patients [2,6,17]. But to study the normal ranges in a healthy population means calculating the confidence intervals of the parameters obtained from spectral analysis. Our data further confirm an age-related decline in neuroautonomic function. Total power, the expression of global variablility and hence of global control over sympathovagal interaction, diminishes conspicuously after the age of 64 years (Table 2). In other words, because aging causes a decline in the influence of neuroautonomic control, it lowers heart rate variability and hence reduces spectral power. The low-frequency spectral components that depend largely upon adrenergic activity (LF power, LF In and NUs) diminished with age both at baseline and after postural tilt (Tables 2,3) (Figs. 2,3). The LF:HF ratio, currently considered the spectral marker of sympathetic activity, appeared depressed in the elderly. A similar behavior has already been reported by Yo et al. in a study examining the effect of age on the LF:HF power ratio in healthy and hypertensive subjects [ 111. But we found that heart rate variability did not differ significantly in subjects under 44 and in those from 44 to 64 years of age (Table 2). This suggests that the age-related decline in sympathetic activity starts from the sixth decade of life. The low HF
123
power suggested a similar decline in parasympathetic function (Table 2). The higher HF NUs in the oldest subjects depended not on increased HF power but on the markedly decreased LF power (Table 2). The decrease in HF power agrees with Lipsitz’s finding of an age-related decline in a spectral measure obtained with fast Fourier transform [12]. Acknowledgment We thank Andrea Lo Verde (BSc Eng) for providing valuable scientific and technical support, Dr Elvira Santagada for performing the spectral evaluations and Dr Carmel Bucca for preparing the manuscript. References 111 Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger
AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981; 213: 220-222. 121 Pagani M, Lombardi F, Guxzetti S. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res 1986; 59: 178-193. [31 Inoue K, Miyake S, Kumashiroshamiro M, Ogata H, Yoshimura 0. Power spectral analysis of heart rate variability in traumatic quadriplegic humans. Am J Physiol 1990, 258 (Heart Circ Physiol 27): H1722-H1726. 141 Rottman JN, Steinman RC, Albrecht P, Bigger JT Jr., Rolnitxky LM, Fleiss JL. Efficient estimation of the heart period power spectrum suitable for physiologic or pharmacologic studies. Am J Cardiol 1990; 66: 1522- 1524. 151 Mahiani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation 1991; 84: 482-492. 161 Yo Y, Nagano M, Nagano N, Iiyama K, Higaki J, Mikami H, Ogihara T. Effects of age and hypertension on autonomic nervous regulation during passive head-up tilt. Hypertension 1994; 23 (suppl I); 1-82-I-86. [71 Clark CV, Mapstone R. Age-adjusted normal tolerance limits for cardiovascular autonomic assessmentin the elderly. Age. Ageing 1986; 15: 221-229. 181 O’Brien IAD, O’Hare P, Corral RJM. Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. Br Heart J 1986; 55: 348-354. 191 Vita G, Princi P, Calabro R, Toscano A, Manna L, Messina C. Cardiovascular reflex tests assessmentof ageadjusted normal range. J Neurol Sci 1986; 75: 263-274.
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Goldstraw PW, Warren DJ. The effect of age on the cardiovascular responses to isometric exercise: a test of autonomic function. Gerontology 1985; 31: 54-58. Weise F, Heydenreich F, Runge U. Contributions of sympathetic and vagal mechanisms to genesis of heart rate fluctuation during orthostatic load: a spectral analysis. J Auton Nerv Syst 1987; 21: 127-134. Lipsitz LA, Mietus J, Moody GB, Golberger AL. Spectral characteristics of heart rate variability before and during postural tilt. Relation to aging and risk syncope. Circulation 1990; 81: 1803-1810. Baselli G, Cerutti S, Civardi S, Lombardi F, Malliani A, Merri M, Pagani M, Rizzo G. Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. Int J Biomed Comput 1987; 20: 51-70. Kay SM, and Marple SL Jr. Spectrum analysis. A modern perspective. Proc IEEE 1981; 69 (11): 1380-1419. Akaike H. Statistical predictor identification. Ann Int Stat Math 1970; 22: 203-217. Zetterberg LH. Estimation of parameters for a linear difference equation with application to EEG analysis. Math Biosci 1969; 5: 227-275. Korkushko OV, Shatilo VB, Plachinda Yu I, Shatilo TV. Autonomic control of cardiac chronotropic in man as a
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function of age: assessmentby power spectral analysis of heart rate variability. J Auton Nerv Syst 1991; 32: 191-198. 1181 Brovelli M, Baselli G, Cerutti S. Computerized analysis for an experimental validation of neurophysiological models of heart rate control. In: Computers in cardiology. Silver Spring, MD IEEE Computer Society Press, 1983; 205-208. [19] Ziegler D, Dannehl K, Muhlen H, Spuler M, Gries FA. Prevalence of cardiovascular autonomic dysfunction assessedby spectral analysis and standard tests of heartrate variation in newly diagnosed IDDM patients. Diabetes Care 1992; 15: 908-911. [20] Pagani M, Malfatto G, Pierini S, Casati R, Masu AM, Poli M, Guzzetti F, Lombardi F, Cerutti S, Malliani A. Spectral analysis of heart rate variability in assessmentof autonomic diabetic neuropathy. J Auton Nerv Syst 1988; 143-153. [21] Thomaseth K, Cobelli C, Bellavere F, Balzani I, De Masi G, Carenza P. Heart rate spectral analysis for assessing autonomic regulation in diabetic patients. J Auton Nerv Syst 1990; 30: S169-S172. [22] Comi G, Natali Sora MG, Bianchi A, Bontempi B, Gianoglio P, Cerutti S, Micossi P, Canali N. Spectral analysis of short-term heart rate variability in diabetic patients. J Auton Nerv Syst 1990; 30: S45-S50.
ELSEVIER
Age-adjusted normal confidence intervals for heart rate variability in healthy subjects during head-up tilt Gianfranco Piccirillo*, Filippo L. Fimognari, Emanuela Viola, Vincenzo Marigliano Cattedra
di Geriatria,
Istituto
di I Clinica
Medica.
Policlinico
Umberro
I, Universita
di Roma
“LA Sapienza”,
00161
Rome, Italy
Received16January1995;revisionaccepted 5 April 1995
Abstract Purpose: Aging leadsto a declinein autonomicnervoussystemfunction. In this study, designedto assess the influenceof ageon neuroautonomicregulationof cardiacactivity, heart rate variability wasmeasuredby power spectral analysisand normalrangesweredeterminedin a population of healthy subjects.Patients and methoak In 83 healthy volunteers(42 men and 41 women;age range 25-85 years) autonomic nervoussystemfunction was assessed by autoregressive spectralanalysisof heart rate variability in clinostatismand after passiveorthostaticload (head-uptilt). The analysisconsideredtwo spectralcomponentsrelevant to the study of the autonomicnervoussystem- highfrequencypower (= 0.25Hz) and low-frequencypower (= 0.10 Hz) - and the ratio betweenthem. Low-frequency spectralcomponents,in particular the ratio betweenlow- andhigh-frequencyspectra,reflectsympatheticactivity; highfrequencycomponentsreflect parasympatheticactivity. Results: For data comparison,the study groupwassubdivided into three agegroups:25 subjects(12 menand 13 women)under 44 yearsof age; 28 (15 men and 13women)aged 44-64 years;and 30 (15 menand 15 women)over 64 yearsof age.The natural logarithmsand normalizedunits of low- and high-frequencypower, and the low-to-high power ratio were usedto calculate95% confidenceintervals. Powerspectralanalysisat baselineand after posturaltilt showedsignificantly higher low-frequencypower of heart rate variablity (P < 0.05), natural logarithm of power (P < 0.001)and normalizedunits (P < 0.001)in the two youngergroupsthan in the oldestgroup. The two youngerage-groupsalsohad significantlyincreasedhigh-frequency power(P < 0.05)and natural logarithm of power (P < 0.05).The oldestagegroup had signifrcantlyincreasedhighfrequencypower analyzedin normalizedunits (P < 0.001).Conclusion: The age-relatedloweringobservedin nearly all the spectralfrequencycomponentsof heart rate variability confirmsin healthy subjectsthat autonomicnervous systemfunction declineswith age.
Keywork Autonomic nervoussystem;Aging; Sympatheticnervoussystem;Powerspectralanalysis;Head-uptilt
1. Introduction
* Corresponding author,Tel.:+3964463301-2-3; Fax:+396 4940594.
Ample evidence has now confirmed spectral analysis of heart rate variability as a valid method for exploring autonomic nervous system function
0167-5273/95/$09.50 @ 1995ElsevierScience IrelandLtd. All rightsreserved SSDI 0167-5273(95)02351-V
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[ll. It has the notable advantage of being a noninvasive test that is simple to perform. The procedure consists of an electrocardiographic recording, measurement of 512 electrocardiographic RR intervals and a spectrum analysis by means of an autoregressive algorithm. It permits the distinction of three main oscillation bands: a high-frequency band (HF), (-0.25 Hz); a low-frequency band (LF) (- 0.10 Hz) and a very-low frequency band (VLF) (0.0033-0.04 Hz). HF and LF powers reflect autonomic nervous system activity (2-51. HF power is thought to arise from parasympathetic activity, LF power from combined parasympathetic and sympathetic activity. The consensus is that the ratio between low- and highfrequency powers (LF:HF) reflects sympathetic activity [6]. Aging induces a decline in autonomic nervous system function [7-lo]. We assessed the influence of age on cardiovascular neuroautonomic regulation in a spectral analysis of heart rate variability with the specific aim of defining the normal ranges in healthy subjects. Power spectra were analyzed in all subjects at rest and after sympathetic stress induced by passive head-up tilt [2,5,6,11,12]. 2. Patients and methods 2.1. Subjects and study protocol
Eighty-three healthy volunteers (42 men and 41 women), whose ages ranged from 25 to 85 years took part in the study. Criteria for exclusion included diastolic blood pressure 195 mmHg; systolic blood pressure 1 160 mmHg; body-mass index > 26 (kg/m*); smoking (> 5 cigarettes per day); diabetes (presence of glucosuria or fasting glycemia > 120 mg/dl or 110 mg/dl at 2 h after glucose loading); cholesterolemia 1 220 mg/dl; and a history or demonstrable evidence of cardiovascular, respiratory, renal (presence of proteinuria and creatinine > 1.5 mg/dl); hepatic, gastrointestinal, or systemic disease. In addition, a complete history was obtained and all subjects underwent a full clinical examination followed by electrocardiography at baseline and during stress; echocardiography; and a two-dimensional echo Doppler study of the neck vessels. For purposes of
Journal of Cardiology 50 (1995) 117-124
comparison the subjects were divided into three age groups: Group 1, under 44 years; Group 2, 44-64 years; and Group 3, over 64 years. All participants gave their informed consent to the study, which was approved by the hospital ethical committee. None of the subjects had taken medicinal or other drugs during the 3 months before the study. 2.2. Power spectral analysisof heart rate variability and passive head-up tilt
Spectral analysis took place according to the following protocol: at 08:OOh, after blood pressure measurement, in a quiet, comfortable room at a temperature of 24’C, each subject rested supine for at least 30 min before undergoing a 15-min electrocardiographic recording. Subjects then underwent head-up tilt testing, a passive orthostatic maneuver obtained with a motorized tilt table. After 15 min upright (90”) the subject was returned to the supine position (0”) for a second 15-min electrocardiographic recording. Transit from 0” to 90’ took about 15 s. If hypotension (a systolic blood-presssure change of > 20 mmHg) developed during postural tilt, testing was stopped and the subject was excluded from the study. Tracings were analyzed with a custom-designed software program running on an IBM-compatible computer with a 486 microprocessor. Electrocardiographic signals were digitized, stored on hard disk and sampled at a rate of 500 Hz, with 12 precision bits. The QRS complex (lead II) was automatically recognized by a classic derivative/ threshold algorithm. An expert cardiologist subsequently checked each QRS complex and the triggering R wave. Ectopic beats were corrected for linear interpolation with the adjacent complexes. Electrocardiographic tracings with > 1% premature beats were eliminated from the analysis. Power spectra were calculated from a consecutive series of 512 RR intervals. The power spectral densities of the baseline and tilt recordings were computed by an autoregressive algorithm developed in our laboratory. The autoregressive algorithm for spectral analysis has been described in detail elsewhere [2,13- 161. Spectral power was expressed in ms*.
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119
1200
REST T VLF LF HF
6.05
POUER:913 WUER:429 POUEft:271 ~@~m:213
6.18 0.16
0.21
NUsS NUs:44
0.26
0.31
688
0.36
Fig. 1. Changes in the power spectrum in a normal subject during passage from recumbency to orthostatism. On the left the baseline spectrum in the subject at rest; on the right, post-tilt spectrum obtained after 15 min of passive head-up tilt. PSD, power spectral density; T, total; VLF, very low frequency (0.0033-0.4 Hz); LF, low frequency (0.04-O. 15 Hz); HF, high frequency (0. IS-O.40 Hz); NUs, normalized units.
Autonomic nervous system function was evaluated from two spectral components: one at high frequency (0.16-0.40 Hz Eq) and the other at low frequency (0.04-o. 15 Hz Eq) (Fig. 1) [ 1, 4- 111. Spectral data were also reported as total power (TP) (Fig. 1) (0.0033-0.4 Hz Eq) and verylow-frequency (VLF) power (Fig. 1). Unlike the other two spectral components, VLF is not detectable in all subjects [6,17]. So that the skewed distribution characteristic of spectral data could be avoided, data were transformed into natural logarithm (In) and into normalized units (NUs) [18]. Transformation of data into normalized units also helped to accentuate sympatho-vagal activity. Normalized units were calculated as follows: LF NUs = LF power/(TP - VLF power) x 100); HF NUs = HF power/(TP - VLF power) x 100. The final index calculated was the ratio between LF and HF powers (LF:HF). During and after headup tilt arterial blood pressures were monitored with a noninvasive volume-clamp device FINAPRES (Omeda). 2.3. Statistical analysis
Data are expressed as means i 1 standard error. Variables such as the subjects’ general characteristics (age, weight, and body-mass index); blood pressure; heart rate; LF and HF NUs; In of VLF, LF and HF; at baseline and after tilt in the
three age ranges were compared by means of an analysis of variance (ANOVA) and the Bonferroni’s test. Student’s paired t-test was used to analyze changes in variables that had a linear distribution (arterial pressures, median RR intervals, LF and HF NUs, and In of VLF, LF and HF). For nonlinear data, including TP, VLF, LF and HF power, the Wilcoxon rank-sum analysis was used for comparison between data from the three groups before and after tilt and the Kruskal-Wallis test and Mann-Whitney U-test were used for comparison between groups. Because the LF:HF ratio had a linear distribution at baseline but a nonlinear distribution after tilt, the baseline results in the three groups were compared by ANOVA and the Bonferroni test; whereas changes in the data obtained before and after tilt were compared by the Wilcoxon rank sum test. The KruskalWallis test and Mann-Whitney U-test were used to compare the LF:HF ratio in the three groups after tilt. The 95% confidence intervals were calculated for data with a linear distribution. Linear regression was applied to determine a relation between variables, and the correlation coefficient between these variables was calculated. A P-value < 0.05 was considered to indicate statistical significance. 3. Results
Of the 102 healthy volunteers (53 men and 49 women) originally enrolled in the study, 19 (10
120
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Table 1 Basal characteristic in three groups of subjects Age ~44 years Group 1
Age 44-64 years Group 2
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of Cardiology
25 12/13 30.0 f 174.1 f 70.2 f 23.2 zt
Age ~44 years Group 1
Age >64 years Group 3
28 15113 30 15/15 0.9 54.8 f 1.1 71.7 * 6.4 170.2 & 10.3 169.2 f 3.6 69.3 zt 6.4 67.0 zt 1.6 24.0 zt 1.3 23.5 zt
RR (ms)
1.1 12.5 4.5 1.5
Table 2 Blood pressure, heart rate and heart rate variability Age ~44 years Group 1
Age 44-64 years Group 2
Age >64 years Group 3
126.6+ (9.7) 80.0+
130.8+ (13.4) 79.9 (10.1) 889.4 (130.1) 1625.3+ (101.8) 893.4 (86.6) 6.63 (0.15) 242.0++ (16.7) + $8) 32.8++ (1.4) 0.1 0.01 489.8+ (22.9)
Rest
SBP (mmHg)
114.8#** (11.4) DBP (mmHg) 74 o+*p (9.9) 907.8+ RR (m.9 (155.8) Total power (ms2) 2883.95 (356.1) VLF power (ms2) 1124.2 (158.7) VLF In (tit) LF power (ms2) 747.41 (120.8) LF In 6.3@ (0.1) LF NUs 47 9++@ (2.6) 0.1 CF (Hz) 0.01 HF power (ms2) 938.0++9 (233.9) + HF In &) HF NUs 51 o++@ (2.7) 0.24 CF (Hz) 0.05 1.1++gg LFIHF (0.11)
(6.1)
853.0 (149.4) 2081.7+” (145.4) 860.7 (67.9) 6.29 (0.34) 524.7# (56.9) nn (z9) 45.5+* (2.5) 0.1 0.01 696.2++” (112.9) 6.3++ (0.15) 53 8++# (2.4) 0.22 0.06 1 I+* (0.28)
(tZ5) 66.7++ (1.4) 0.25 0.08 0.5++ (0.03)
Tilt
SBP (mmHg)
1114**@ (23.5)
133.3 (16.6)
117-124
Table 2 (continued)
DBP (mmHg) :ex (M/F) Age (years) Height (cm) Weight (kg) BMI (kg/m2)
50 (1995)
136.0 (13.7)
Age 44-64 years Group 2
Age >64 years Group 3
78.0*#
83.9
83.7
(8.0)
(6.8)
(6.8)
824.1 (169.7) Total power (ms2) 2224.2* (177.2) VLF power (ms2) 1127.1 (122.1) VLF In (fK) LF POWER (ms2) 805.85 (133.7) LF In 6.4$ (0.13) LF NUs 77.41 (3.5) CF Hz 0.1 0.01 HF power (ms2) 291.3s (72.7) HF In 4.9 (0.29) HF NUs 22.81 (3.5) CF (Hz) 0.26 (0.06) LFlHF IO.91 (3.6)
815.3 (127.2) 1669.3 (143.1) 922.8 (89.8) (,4;‘, 582.3’ (66.1) (if) 75.5”
(1.8)
0.1 0.01 181.5”” (25.1) (0%) 23.5* (1.7) 0.23 (0.05) 3.8” (0.4)
861.2 (114.1) 1864.3 (136.9) 1060.7 (121.9) 6.7 (0.15) 497.3 (116.5) (0s.c 50.0 (2.5) 0.1 0.01 409.3 (29.4)
(f23) 46.3 (3.2) 0.20 (0.07) (:b:
Rest, baseline conditions; Tilt, 15 mitt after 15 mm of head-up tilt testing; SBP, systolic blood pressure; DBP, diastolic blood pressure; median RR, median RR intervals; TP total power; VLF power, very-low-frequency power; VLF In, natural logarithm obtained from very-low-frequency power; LF power, low-frequency power; LF In, natural logarithm obtained from low-frequency power; LF NUs, low-frequency normalized units; HF power, high-frequency power; HF In, natural logarithm of high-frequency power; HF NUs, high-frequency normalized units; LFHF, the ratio between low-frequency and high-frequency spectra; CF, center frequency. Note that the mean VLF In during rest in Group 3 was higher than that of the other two groups, in particular it was higher than that of Group 1, even though Group 1 had higher mean VLF power. This apparent contradiction results from the widely dispersed VLF power values and from the data flattening determined by log tranformations. ++P c 0.001: Rest vs. tilt; +P C 0.05: Rest vs. tilt. **P c 0.001: Group 1 vs. Group 2; *P < 0.05: Group 1 vs. Group 2. BP c 0.001: Group 1 vs. Group 3; SP c 0.05: Group 1 vs. Group 3. *P c 0.001: Group 2 vs. Group 3; #P < 0.05 Group 2 vs. Group 3.
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n q
REST TILT
0
LF NUs
. 50.
L p
~p
1
GROUP
~P<0001f
2
GROUP
3
Fig. 2. Changes in high-frequency spectral power analyzed in normalized units in the 83 subjects subdivided into three age-groups (Group 1: < 44 years of age; Group 2: 44-64 years; Group 3: >64 years) at baseline (REST) and after 15 min of passive head-up tilt (TILT). NUs LF, normalized units obtained from low-frequency (0.04-O. 15 Hz). Note the significant increase in NUs LF in all three age groups after tilt. &P c 0.001: Group I vs. Group 3; ##P < 0.001 Group 2 vs. Group 3.
men and 9 women) were excluded because they had a <80-mmHg fall in systolic blood pressure during tilt-testing. The 83 remaining subjects (42 men and 41 women, age range 25-85 years) had the following age distribution: 25 subjects belonged to the >44year age group; 28 to the 44- to 64-year group; and 30 to the >64-year group. The three groups had comparable sex distribution, height, weight and body-mass index (Table 1). Spectrum analysis at baseline showed that the two younger groups (< 44 years of age and from 44 to 64 years) had significantly higher TP of heart rate variability (P < 0.05) than the over 64-yearolds (Table 2). The two younger groups also had significantly greater LF power, LF In and NUs (Fig. 2) and a LF:HF ratio (P c 0.001) than the over 64-year-olds, with no significant difference between groups. The youngest group (< 44 years of age) and the middle-aged group (44-64 years of age) had significantly greater HF power of heart rate variability (P < 0.05) than the over 64-yearolds but the oldest group had significantly greater HF NUs than those of the two under 64-year-old groups. Postural tilt-testing caused a significant increase in LF NUs and LF:HF (P < 0.001) in all age groups but the two younger groups had significantly higher after-tilt LF NUs and LF:HF than the oldest group. (Table 2, Fig. 2).
Tilt-testing induced a decrease in baseline HF power in all three groups. In the two younger groups all components decreased significantly (HF power, P < 0.001; HF In P < 0.05; and HF NUs P < 0.001) (Table 2), whereas in the over 64-yearolds the decrease reached statistical significance only for HF power (P c 0.05) and HF NUs (P < 0.001). In addition, these components resulted significantly lower in the two younger groups (P c 0.001) (Table 2). Table 3 95% Confidence limits of power logarithm and normalized unit Age <44 years Group I
Age 44-64 years Group 2
Age >64 years Group 3
6.07-6.63 42.78-53.07 5.89-6.74
45.72-56.41 0.83-1.28
5.94-6.32 40.5-50.48 5.99-6.58 48.95-58.63 0.54-1.65
5.29-5.61 30.13-35.58 6.04-6.25 63.96-69.51 0.43-0.58
6.19-6.71 70.56-84.31
6.03-6.42
71.91-79.22
5.79-6.16 45.14-55.01 5.77-6.08 39.99-52.72 0.29-4.24
Rest
LF In LF NUs HF In HF NUs LF:HF Tilt
LF In LF NUs HF In HF NUs LF:HF
4.33-5.48
16.07-29.70 3.88-18.02
4.78-5.23 20.24-26.94 3.08-4.54
122
G. Piccirillo et al. /International Journal of Cardiology 50 (1995) 117-124
3, n=e3 a Rest I-: -8.571 p< 6.881
2,
i-‘ti r: -0.349
p< 8.881
1, J
18
28.
.3e
48
--se YEFIRS
68
R
88
-98’
Fig. 3. Highly significant inverse correlation between age and In LF (natural logarithm of low-frequency power, calculated at frequencies from 0.04 to 0.15 Hz) in normal subjects at baseline (REST) and after 15 min of head-up tilt (TILT).
Confidence intervals varied in the three age groups (Table 3). Baseline values of LF In, LF NUs, and LF:HF appeared to diminish with advancing age. The HF In also decreased with advancing age, the only exception being the lower limit in the over 64-year-old group, which only slightly exceeded that of the other two groups dur-
. . 0 I
.
ing rest but markedly exceeded it after tilt. Contidence intervals for baseline HF In had a far narrower range than those for the other tested spectral parameters of heart rate variability and also had the lowest lower limit. HF NUs and LF NUs behaved in an opposite manner, HF NUs increasing with advancing age. Because the under
.
.
. .
Fig. 4. Highly significant inverse correlation between age and NUs LF (normalized units from low-frequency power, calculated at frequencies from 0.04 to 0.15 Hz) in the 83 subjects at baseline (REST) and after 15 min of head-up tilt (TILT),
G. Piccirillo et al. /International Journal of Cardiology 50 (1995) 117-124
44-year-old group had dispersed, non-linear, LF:HF after tilt, their LF:HF ranged widely. This range tends nevertheless to diminish with age. Regression analysis showed a significant inverse correlation between age and the LF In (Fig. 3) and age and LF NUs (Fig. 4) during rest and after tilt. 4. Discussion Spectral analysis has recently been introduced also in the study of autonomic diabetic neuropathy, with interesting results [ 19-221. Because of the age-related decline in neuroautonomic function, however, comparative data are needed from studies in age-matched subjects. Our aim in this study was therefore to fultil this need by providing information that might be useful in the clinical assessment of neuroautonomic function. Many studies have described the effects of age on the spectral components of heart rate variability in patients [2,6,17]. But to study the normal ranges in a healthy population means calculating the confidence intervals of the parameters obtained from spectral analysis. Our data further confirm an age-related decline in neuroautonomic function. Total power, the expression of global variablility and hence of global control over sympathovagal interaction, diminishes conspicuously after the age of 64 years (Table 2). In other words, because aging causes a decline in the influence of neuroautonomic control, it lowers heart rate variability and hence reduces spectral power. The low-frequency spectral components that depend largely upon adrenergic activity (LF power, LF In and NUs) diminished with age both at baseline and after postural tilt (Tables 2,3) (Figs. 2,3). The LF:HF ratio, currently considered the spectral marker of sympathetic activity, appeared depressed in the elderly. A similar behavior has already been reported by Yo et al. in a study examining the effect of age on the LF:HF power ratio in healthy and hypertensive subjects [ 111. But we found that heart rate variability did not differ significantly in subjects under 44 and in those from 44 to 64 years of age (Table 2). This suggests that the age-related decline in sympathetic activity starts from the sixth decade of life. The low HF
123
power suggested a similar decline in parasympathetic function (Table 2). The higher HF NUs in the oldest subjects depended not on increased HF power but on the markedly decreased LF power (Table 2). The decrease in HF power agrees with Lipsitz’s finding of an age-related decline in a spectral measure obtained with fast Fourier transform [12]. Acknowledgment We thank Andrea Lo Verde (BSc Eng) for providing valuable scientific and technical support, Dr Elvira Santagada for performing the spectral evaluations and Dr Carmel Bucca for preparing the manuscript. References 111 Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger
AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981; 213: 220-222. 121 Pagani M, Lombardi F, Guxzetti S. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res 1986; 59: 178-193. [31 Inoue K, Miyake S, Kumashiroshamiro M, Ogata H, Yoshimura 0. Power spectral analysis of heart rate variability in traumatic quadriplegic humans. Am J Physiol 1990, 258 (Heart Circ Physiol 27): H1722-H1726. 141 Rottman JN, Steinman RC, Albrecht P, Bigger JT Jr., Rolnitxky LM, Fleiss JL. Efficient estimation of the heart period power spectrum suitable for physiologic or pharmacologic studies. Am J Cardiol 1990; 66: 1522- 1524. 151 Mahiani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation 1991; 84: 482-492. 161 Yo Y, Nagano M, Nagano N, Iiyama K, Higaki J, Mikami H, Ogihara T. Effects of age and hypertension on autonomic nervous regulation during passive head-up tilt. Hypertension 1994; 23 (suppl I); 1-82-I-86. [71 Clark CV, Mapstone R. Age-adjusted normal tolerance limits for cardiovascular autonomic assessmentin the elderly. Age. Ageing 1986; 15: 221-229. 181 O’Brien IAD, O’Hare P, Corral RJM. Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. Br Heart J 1986; 55: 348-354. 191 Vita G, Princi P, Calabro R, Toscano A, Manna L, Messina C. Cardiovascular reflex tests assessmentof ageadjusted normal range. J Neurol Sci 1986; 75: 263-274.
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function of age: assessmentby power spectral analysis of heart rate variability. J Auton Nerv Syst 1991; 32: 191-198. 1181 Brovelli M, Baselli G, Cerutti S. Computerized analysis for an experimental validation of neurophysiological models of heart rate control. In: Computers in cardiology. Silver Spring, MD IEEE Computer Society Press, 1983; 205-208. [19] Ziegler D, Dannehl K, Muhlen H, Spuler M, Gries FA. Prevalence of cardiovascular autonomic dysfunction assessedby spectral analysis and standard tests of heartrate variation in newly diagnosed IDDM patients. Diabetes Care 1992; 15: 908-911. [20] Pagani M, Malfatto G, Pierini S, Casati R, Masu AM, Poli M, Guzzetti F, Lombardi F, Cerutti S, Malliani A. Spectral analysis of heart rate variability in assessmentof autonomic diabetic neuropathy. J Auton Nerv Syst 1988; 143-153. [21] Thomaseth K, Cobelli C, Bellavere F, Balzani I, De Masi G, Carenza P. Heart rate spectral analysis for assessing autonomic regulation in diabetic patients. J Auton Nerv Syst 1990; 30: S169-S172. [22] Comi G, Natali Sora MG, Bianchi A, Bontempi B, Gianoglio P, Cerutti S, Micossi P, Canali N. Spectral analysis of short-term heart rate variability in diabetic patients. J Auton Nerv Syst 1990; 30: S45-S50.