CLINICAL RESEARCH: ELECTROPHYSIOLOGY
Dissection of long-range heart rate variability
Controlled induction of prognostic measures by activity in the laboratory
Daniel Roach, PhD*,
Wendy Wilson, AART*,
Debbie Ritchie, MN* and
Robert Sheldon, MD, PhD*,*
* Cardiovascular Research Group, University of Calgary, Calgary, Alberta, Canada
Manuscript received July 17, 2003;
revised manuscript received January 7, 2004,
accepted January 12, 2004.
* Reprint requests and correspondence: Dr. Robert Sheldon, University of Calgary, Faculty of Medicine, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada.
sheldon{at}ucalgary.ca
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Abstract
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OBJECTIVES: We sought to determine whether the long-range measures of heart rate variability (HRV)—the standard deviation of sequential 5-min heart period mean values (SDANN) and the heart period spectral amplitude in the ultra-low frequency band <0.0033 Hz (ULF)—had their origins partly in physical activity.
BACKGROUND: The SDANN and ULF are prognostic HRV factors whose physiologic origins are obscure. Their discontinuous presence throughout the day suggested that they arise from changes in heart period due to activity.
METHODS: Heart period sequences were recorded from 14 patients with left ventricular dysfunction and 14 control subjects during an unrestricted 24-h day, 4-h supine rest, and 4-h epoch with scripted activities.
RESULTS: The SDANN was higher during activity than during rest (74 ± 23 ms vs. 43 ± 17 ms, p < 0.0001), as were ULF magnitudes (p < 0.0001). The increase in SDANN was due to specific activities that contributed heavily (p < 0.0001 by analysis of variance); for example, a 10-min walk and 90-min rest each contributed 22% of total SDANN. Patients with heart disease had a lower SDANN and ULF and a higher mean heart rate than control subjects during all recordings. The proportional ranges in heart period were the same in the two groups during controlled, scripted activities but were wider in control subjects than in patients during ambulatory recordings, suggesting decreased activity by patients.
CONCLUSIONS: Activity increases SDANN by increasing the range of heart periods. Patients with diminished ventricular function have a reduced SDANN on ambulatory electrocardiograms, possibly and partly because of a higher mean heart rate and reduced variations in physical activity.
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Abbreviations and Acronyms
| | ACE | = angiotensin-converting enzyme | | ANOVA | = analysis of variance | | ECG | = electrocardiogram | | HRV | = heart rate variability | | LV | = left ventricular | | SDANN | = standard deviation of sequential 5-min heart period mean values | | SDNN | = standard deviation of all heart periods | | ULF | = ultra-low frequency band <0.0033 Hz |
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Measures of long-range heart rate variability (HRV) are important prognostic predictors. They include the standard deviation of the mean values of successive 5-min heart period epochs (SDANN) and the power in the ultra-low frequency band <0.0033 Hz (ULF). The SDANN correlates well with the log of the ULF power (1). Reductions in SDANN and ULF predict poor survival for patients with chronic, severe mitral regurgitation (2), acute or recent myocardial infarction (3–5), and idiopathic dilated cardiomyopathy (6), as well as for 6,693 outpatients assessed for arrhythmias (7).
Despite this, their physiologic origins are obscure. Hypothesized causes include oscillatory and aperiodic, deterministic changes (8,9), as well as heart period fluctuations due to peripheral vasomotor, thermoregulatory, or renin-angiotensin systems (10). We have shown in healthy ambulatory subjects that SDANN arises from epochs of time when patients have local mean heart period values that differ from the 24-h mean heart period (i.e., mainly at night) (11). Similarly, ULF power occurs predominantly when patients are changing their behavior from passive to active, and vice versa. We hypothesized that both SDANN and ULF do not arise from systemic oscillatory processes; rather, they arise from changes in activity levels. Our goals were: 1) to directly determine the effect of changes in activity level on SDANN and ULF; and 2) to determine the cause of reduced HRV in patients with left ventricular (LV) dysfunction.
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Methods
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Experimental and analytic approach.
The subjects included patients with poor LV function and healthy people in the same age range in order to increase the likely range of HRV and to allow a comparison between the two populations. They underwent three recording sessions on separate days. The first was an unrestricted 24-h session to assess HRV during usual daily activities. The second was a 4-h session of enforced bed rest to obtain recordings under wakefulness but with a range of restricted activities. An observer was present to monitor activities and prevent sleeping. The third was a 4-h session during which the subjects followed a script with a range of normal daily activities. The scripted activities began with 10 min of standing, followed by 40 min of seated reading, 50 min for seated lunch, 90 min of supine rest, 10 min for a washroom visit, 10 min of sitting, 10 min of standing, 10 min of treadmill walking at 1.7 km/h, and finally 10 min of sitting. Thus, the "activity day" provided a controlled range of physical activity, and the control "rest day" provided bed rest. The minimum 10-min epochs were selected to be at least twice the minimum sampling interval of SDANN and ULF.
Subjects.
All subjects gave written, informed consent; the protocol was approved by the University of Calgary Conjoint Medical Ethics Review Board. Patients were eligible if they had an LV ejection fraction  40%. Exclusion criteria included limiting orthopedic or vascular disease, drugs that might cause chronotropic incompetence, and a permanent pacemaker. Control healthy subjects were in a population with the same age range as the patients. During the study, three healthy volunteers were found to have significant coronary artery disease and were released from the study for treatment; other volunteers replaced them.
Data acquisition.
Ambulatory electrocardiographic (ECG) recordings were acquired using a system that uses a synchronization pulse to reduce variability due to tape speed fluctuations. The recordings were analyzed using the Marquette 8000 Scanner, with version 5.7 of the Marquette Arrhythmia Analysis Program to identify and label each QRS complex. Entire recordings were analyzed by an operator to eliminate cycles in which ventricular beats were without normal P waves. These beats were replaced by linear interpolation between adjacent normal beats. Unclassified beats were corrected manually. No recordings had ectopy more severe than isolated complexes or couplets. The corrected sequences were analyzed in MATLAB (The Mathworks, Natick, Massachusetts).
Time and frequency domain analyses.
For most analyses, we used SDANN, because it can be calculated in short well-defined epochs. The ULF requires much longer sequences, and this makes time-frequency analysis difficult. The SDANN was calculated as the standard deviation of the sequence of the mean heart periods of normal beats within successive 300-s epochs. The standard deviation of all heart periods (SDNN) was calculated as the standard deviation of the sequence of all heart periods of normal beats. For power spectral density functions, we first computed a 24-h Fourier transformation (11). Using linear interpolation, the 24-h heart period sequences were uniformly sampled 218 times using a sample interval of 0.329 s. The interpolated sequences had their mean values removed, were Hanning windowed, and then transformed to the frequency domain using a fast Fourier transform. The ULF power is the sum of the power in band <0.0033 Hz, excluding the direct current component, and the ULF magnitude is the sum of the magnitude of the ULF components. Note that the ULF magnitude is ULF power1/2 and is dimensionally equivalent to SDANN; both are expressed as milliseconds.
We calculated the contributions of the specific activities in each 5-min epoch to the total SDANN of the scripted activity day (11). First, we replaced the mean heart period value of each successive 300-s epoch with the 4-h mean heart period and then recalculated the SDANN of this altered 4-h sequence. The difference between the original SDANN and the SDANN of the altered sequence is the local SDANN (i.e., the contribution made by that particular 300-s epoch toward total SDANN). Local SDANN values were normalized such that their sum equaled the total SDANN.
Statistical analysis.
We used the Kolmogorov-Smirnov test to examine the normality of distributions. Normal distributions are reported as the mean value ± SD. Non-normal distributions are reported as 25%, median, and 75% quartile values. Analysis of variance (ANOVA) was used for grouped variables of normal distributions, and the Kruskal-Wallis test was used for grouped variables of non-normal distributions. The unpaired t test was used to compare normal distributions, and the Mann-Whitney U nonparametric test was used for comparing non-normal distributions.
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Results
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The 14 patients (13 men) with heart disease had a mean age of 64 ± 11 years (Table 1). Two had idiopathic dilated cardiomyopathies and 12 had old myocardial infarctions. Their mean LV ejection fraction was 29 ± 8%. None were taking beta-blockers, verapamil, diltiazem, or amiodarone. Their mean New York Heart Association functional class was 1.7 ± 0.6. Medications used included diuretics (n = 12), angiotensin-converting enzyme (ACE) inhibitors (n = 11), nitrates (n = 7), digoxin (n = 3), and apresoline (n = 2). None had medications held or discontinued to participate in the study. There were 14 control subjects (age 66 ± 7 years, 5 women). None were taking medications. All subjects completed the protocol.
Heart period sequences and HRV.
The patient and control recordings were generally similar (Fig. 1). Both showed diurnal fluctuations in heart period for the 24-h recordings, with similarly distributed changes in heart period during the activity day and relatively little variation in heart period during the rest day. Patients had a lower mean heart period than did controls (Table 2) during each of the three sessions (p 0.0002). The SDNN, the measure of total variation in heart period sequences, was lower in patients than in controls in all three sessions (p = 0.0009 to 0.01) and was higher on scripted activity days than scripted rest days (p = 0. 0013 to 0.0036).
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Figure 1 Heart period recordings. (Top panels) Recordings from a 24-h day from a control subject (left) and heart failure patient (right). (Middle panels) Recordings from a 4-h scripted activity day from the same subjects. (Bottom panels) Recordings from a 4-h rest day from the same subjects. SDANN = standard deviation of sequential 5-min heart period mean values. ULFmag = ULF magnitude.
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In the total population, the SDANN was higher during scripted activity days than during rest days (74 ± 23 ms vs. 43 ± 17 ms, p < 0.0001). This effect was noted separately for both patients (63 ± 25 ms vs. 37 ± 19 ms, p < 0.005) and control subjects (84 ± 14 ms vs. 50 ± 12 ms, p < 0.0001). In the total population, the ULF magnitude was higher during scripted activity days than during rest days (71 ± 29 ms vs. 38 ± 14 ms, p < 0.0001). This effect was noted separately for both patients (63 ± 34 ms vs. 34 ± 15 ms, p = 0.0067) and control subjects (80 ± 22 ms vs. 42 ± 13 ms, p < 0.0001). Finally, the SDANN and ULF magnitude correlated well with each other within each type of recording session (24-h day, R2 = 0.92; scripted rest, R2 = 0.60; scripted activity, R2 = 0.89; p < 0.0001 for each). In summary, scripted physical activities increase the SDANN and ULF magnitude more than that which occurs during enforced rest.
Contribution of specific activities to SDANN.
We predicted that the induction of specific activities would be accompanied temporally by changes in SDANN. Numerous local contributions to SDANN were scattered throughout the recording period, particularly at times of maximal local deviation of the heart period from the global mean value (Fig. 2). Specific events contributed heavily (p < 0.0001 by ANOVA) to total SDANN (Table 3). For example, a 10-min walk and 90-min rest each contributed 22% of total SDANN. Although the mean contribution per 5-min epoch was 1.61 ms, the intensity of the localized SDANN contribution per 5-min epoch was highly localized (p < 0.0001 by ANOVA). For example, a 90-min supine rest and 10-min treadmill walk contributed 1.2 ms/5 min and 8.0 ms/5 min, respectively. Thus, specific physical activities contribute localized and idiosyncratic proportions of overall SDANN.
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Figure 2 Temporally localized contributions to standard deviation of sequential 5-min heart period mean values (SDANN) due to scripted activities. (Top panel) Heart period recordings from a control subject and patient during a scripted activity day. (Bottom panel) Contribution per 5-min epoch of each activity in each subject to overall SDANN.
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Table 3 Contributions of Each of the Specific Activities During the Scripted Activity Day Depicted in Figure 3 to the Total SDANN*
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Sources of reduced heart period variability in patients.
We wished to determine whether (and why) induced SDANN and ULF were lower in the patients with heart disease (Table 2). The SDANN was lower in patients than in controls during their daily activities (92 ± 47 ms vs. 154 ± 47 ms, p = 0.002), and the ULF magnitude was lower in patients than in controls during their daily activities (93 ± 51 ms vs. 154 ± 52 ms, p = 0.0047). Similarly, the SDANN was lower in patients than in controls during scripted activity days (37 ± 19 ms vs. 50 ± 12 ms, p = 0.049) and scripted rest days (63 ± 25 ms vs. 84 ± 14 ms, p = 0.013). Similar directional differences in ULF tended toward significance. Therefore, patients had a lower SDANN (and possibly a lower ULF magnitude) than did control subjects in all recordings.
This protocol provides a preliminary insight into the causes of the differences in these measures of HRV between patients and control subjects. The SDANN is a standard deviation and therefore is the product of the mean heart period and the dimensionless coefficient of variation. The mean heart period reflects the global averaged heart period response to activities during the recording session. The coefficient of variation reflects the range of responses of heart period to the range of physical activities. Is SDANN lower in patients because the mean heart period is lower, or because the coefficient of variation is lower?
We first determined whether the differences in SDANN between patients and controls were associated with differences in the mean heart periods or the coefficients of variation under controlled conditions (Table 4). During the scripted rest day, patients and controls had similar coefficients of variation (0.042 ± 0.017 vs. 0.042 ± 0.014), but patients had lower mean heart periods (816 ± 116 ms vs. 1,088 ± 130 ms, p < 0.0001). Similarly, during the scripted activity day, patients and controls had similar coefficients of variation (0.085 ± 0.031 vs. 0.089 ± 0.015), but patients had lower mean heart periods (760 ± 105 ms vs. 933 ± 107 ms, p = 0.0002). Therefore, under controlled conditions, the lower SDANN of patients is associated with a lower mean heart period, and not a lower coefficient of variation, thus, in turn, a surrogate measure of the range of activities. In contrast, during the unrestricted 24-h day, patients had both lower mean heart periods (775 ± 69 ms vs. 952 ± 127 ms, p = 0.0002) and lower coefficients of variation (0.116 ± 0.055 vs. 0.162 ± 0.045, p = 0.025) than did control subjects. Therefore, during unrestricted activities, patients have a lower SDANN that is associated with both a higher mean heart rate and possibly a reduced range of physical activities.
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Table 4 Contributions of Mean Heart Period and Coefficient of Variation to the Reduced SDANN in Patients With Heart Disease
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Discussion
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Previous reports suggested that long-range HRV might arise in the renin-angiotensin system or in thermoregulation. In three conscious dogs, ACE inhibition increased power 2.5- to 4-fold in a bandwidth <0.1 Hz (12). Similarly, captopril increased SDANN by 27% to 35% in patients within a week of acute myocardial infarction (13) or with mild-to-moderate heart failure (14). The mechanism of this effect might be due to bradykinin, renin, angiotensin, or peripheral vasomotor tone (15). A second and untested source is thermoregulation. Thermoregulatory skin blood flow fluctuations might be reflected in blood pressure fluctuations and, in turn, transduced by arterial baroreceptors into heart period fluctuations (16).
These uncertainties lead us to search for temporally localized origins of SDANN and ULF on 24-h ambulatory ECGs of healthy subjects. The local contributions to the signals were discontinuous, with SDANN predominantly arising during sleep (when the heart period is furthest from the 24-h mean heart period) and ULF predominantly arising during the transitions to and from sleep (when the largest and most persistent heart period changes occur [11]). The apparent origin of SDANN from sleep may be explained on theoretical grounds. The SDANN is the sum of the squares of the differences between the temporally local mean heart periods and the global mean heart period, and because people spend only a minority of the 24-h day asleep, the mean heart rate at this time will be further from the mean than the mean heart rate at other times (11). We used scaled wavelets to show that ULF mainly detected one-way nonharmonic variations in heart rate around times of arising and going to bed.
Origins of SDANN and ULF in physical activities.
Here we report that SDANN and ULF are induced under controlled conditions with a protocol that included a range of physical activities, and that SDANN arises discontinuously during the activity protocol. It is not physical activity, per se, that causes long-range HRV; rather, it is the range of physical activities, each with their own metabolic and hemodynamic demands and with their own mean heart rates, which is reflected in a corresponding range of heart rates and therefore heart rate variabilities. The importance of activity was highlighted by the demonstration that SDANN and ULF could be induced with activity-responsive pacemakers in levels very similar to those induced by pacemakers that tracked sinus node activity (17). There is a covariant relationship between HRV and body movement (18), and heart period variance and the power in the frequency band of 0.0033 to 0.033 ms are higher on exercise recordings than on rest recordings (19). Therefore increasing the range of physical activities increases SDANN, and does so by increasing the range of heart periods.
The SDNN and SDANN are related measures. Both are standard deviations; SDANN reflects the distribution of the mean heart period of sequences 5 min long, whereas SDNN reflects the distribution of all heart periods. The SDANN therefore reflects changes that take place over long periods of time, whereas SDNN reflects all changes in heart period. The SDNN behaves much like SDANN, suggesting that much of it may arise similarly.
Clinical implications.
Mean heart periods were lower and increases in SDANN and ULF were smaller in patients with poor ventricular function than in control subjects. When the activity range was controlled, the coefficient of variation was similar in patients and controls, suggesting that patients might have less ranges of activity than controls on the 24-h recordings. Taken together, the data suggest that SDANN and ULF may be surrogate measures for two important factors: mean heart rate and range of physical activity. The latter raises the possibility that SDANN and ULF may be measures of functional capacity. Measuring the functional capacity of patients provides useful information on their clinical status and prognosis and is commonly assessed with techniques such as the 6-min walk (20).
A simple assessment of functional status, based on daily activities in the community rather than in the clinic, might be helpful. However, Table 4 shows that the reduction in SDANN and ULF in patients is associated with both a higher mean heart rate and lower coefficient of variation. Indeed, the usefulness of SDANN and ULF might arise because they reflect both the mean heart rate, which is itself a prognostic measure (21,22), and the coefficient of variation, which may reflect functional capacity. Those patients who have a high heart rate and/or are inactive, either by choice or because they are too ill, will have a reduced long-range HRV, and each factor portends a poor prognosis compared with more active patients.
This study illustrates a difficulty with conventional long-range estimates of HRV. Obtaining an ambulatory ECG recording is equivalent to performing an uncontrolled experiment, with each recording being an idiosyncratic mix of subject-driven physical activity and mean heart rate. The latter might be influenced by drugs that affect the sinus node, such as beta-blockers (23), calcium channel antagonists (24), antimuscarinic agents (25), and amiodarone (26). Also, subjects with high heart rates or those confined to bed for reasons other than functional capacity and volition may have misleadingly low SDANN and ULF values.
Conceptual implications.
This study illustrates an events-based conceptual and methodologic framework for understanding and possibly modifying HRV (27–30). This approach focuses on the individual responses of the sinus node to transient physiologic stresses and does not assume any global properties; rather, it focuses on temporally localized structures within the recordings and overcomes the problem of stationarity that confounds frequency domain analyses. We named this a lexical approach, with each of the distinct structures being termed a "lexon," in that these structures seem to be arranged like words in a sentence. This approach identified the burst, a transient tachycardia induced by exercise initiation (27,28); an abrupt, large-magnitude, transient bradycardia that occurs in both humans and in conscious rabbits (29); the controlled induction of heart rate turbulence (30); and the discontinuous and physiologic origin of SDANN and ULF (11,17).
Study limitations.
Our patients were not completely drug free. However, none were receiving drugs with substantial effects on the sinus node (23–26). Many were taking ACE inhibitors, but these drugs increase rather than decrease HRV (13–15). We did not compare the number of metabolic equivalents performed by patients and control subjects during their typical days; instead, we elected to measure the response of HRV to a uniform amount of activity. We focused our study on long-range measures of HRV, and SDNN was included as a global measure of all ranges of HRV. We have not performed similar calculations for the frequency bandwidths centered on 0.10 or 0.25 Hz or their time domain equivalents, because we would expect to find them different between rest and activity days. They are sensitive to posture, which is different between the two scripted days, but this does not address our primary hypothesis.
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Footnotes
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This study was supported by grants GR13914 from the Medical Research Council, Ottawa, Canada, and from the Calgary General Hospital Research and Development Committee, Calgary, Canada. Dr. Roach was a Postdoctoral Fellow of the Heart and Stroke Foundation of Canada, Ottawa, Canada.
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Abstract
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