Fig. 7.1
Power spectrum of short-term HRV and frequency ranges modulated by sympathetic and vagal heart rate controls. While cardiac vagal control system can modulate heart rate in the entire frequency band, cardiac sympathetic control system can modulate heart rate at frequencies <0.15 Hz. Consequently, while the LF component of HRV is mediated by both sympathetic and vagal nerves, the HF component is mediated purely by the vagus
7.2.1.1 HF Component of HRV
The HF component of HRV usually corresponds to RSA, and thus, its frequency is identical to breathing frequency (e.g., when breathing frequency is 15 cycle/min, the frequency of HF component is 15 cycle/60 s = 0.25 Hz). This means that when breathing frequency decreases below 9 cycle/min (0.15 Hz), HRV caused by RSA is detected as a part of LF component and physiologic HF component disappears; HF component under such conditions, if observed, should be interpreted as a different physiologic entity from RSA or an artifact.
The mechanisms generating RSA is discussed in Chap. 8 in detail. Briefly, RSA is mediated by the vagus originated from the nucleus ambiguus in the medulla oblongata that is modulated by the input from the respiratory center and generates vagal outflow fluctuating with respiration [18]. By this mechanism, vagal flow increases during expiration and decreases during inspiration, generating RSA. Although respiratory fluctuation exists also in sympathetic outflow, it does not transfer to HRV when the breathing frequency is above 0.15 Hz.
7.2.1.2 LF Component of HRV
The LF component of HRV is thought to be heart rate variation caused by Mayer wave [19] in blood pressure fluctuation through the arterial baroreceptor reflex mechanism (Fig. 7.2) [20, 21]. Mayer wave is a component of physiological arterial blood pressure fluctuation with a period around 10 s. It is also called the third-order variation of blood pressure and has been found by Cion in 1874 and described by Mayer in 1876 [19].
Fig. 7.2
Arterial blood pressure and R-R interval of ECG in a healthy subject. Mayer wave is observed in the compressed strip of arterial blood pressure (upper panel after 5 s) at a period around 13 s. In the trend of R-R intervals (lower panel), fluctuations corresponding HF (period around 3 s) and LF (period around 13 s) components are observed. The LF fluctuation of R-R interval shows several-second delay behind Mayer wave of arterial blood pressure
The mechanisms generating Mayer wave are still controversial, and there are theories proposing peripheral, central, and resonance origins. Blood vessel contraction by sympathetic vasomotor function is known to occur with 5-s delay after sympathetic neural activation. Simulation studies have reported that such delay systems cause spontaneous fluctuation in the baroreceptor reflex feedback loop at a period of 10 s [20]. The LF component of HRV is decreased in patients with low baroreceptor reflex sensitivity independently of the presence of sympathetic innervation to the heart [21].
7.2.2 Long-Term HRV
Although the LF and HF components are the major constituents of short-term HRV, long-term HRV comprises nonperiodic components with a broad range of spectrum as the major constituents [22]. For descriptive purposes, fluctuations at frequencies lower than LF component are divided into two components: very-low-frequency (VLF) component (0.003–0.04 Hz) and ultralow frequency (ULF) component (≤0.003 Hz). Unlike LF and HF components, VLF and ULF are nonperiodic components that form no distinct peaks in power spectrum. The nonperiodic component of long-term HRV is also called 1/f fluctuation or fractal component, because it has power negatively correlated with frequency in the power spectrum and it furnishes the properties of fractal dynamics including long-term negative correlation and scale-independent self-affine structure [23].
Long-term HRV from ambulatory ECG recordings under daily activities includes the influences of circadian and ultradian variations in physiological functions, physical and mental activities, and environmental factors. Accordingly, analysis of long-term HRV does not suit for the evaluations of specific autonomic functions, but it may be useful for the evaluations of overall performance of autonomic regulations. In fact, long-term HRV provides prognostic information, particularly all-cause mortality risk in patients with cardiac and other diseases, which have stronger predictive power than short-term HRV indices [24–26].
7.3 Data Collection for HRV
7.3.1 Data Sources of HRV
HRV can be analyzed when at least one lead of continuous ECG recording is available. The ECG needs to be stored as digital data at a sampling frequency of no less than 125 Hz (desirably, 500–1000 Hz) to avoid artificial cycle length fluctuations caused by under sampling. The ECG data are converted into time series of beat-to-beat cycle length by measuring all R-R intervals (Fig. 7.3). For this purpose, accurate automated classifications (normal sinus rhythm, atrial or ventricular ectopic beat, and artifact) are requited for all beats, the results of classification should be reviewed, and all errors in the annotation need to be edited completely.
Fig. 7.3
Measurement of HRV from ECG. For the analysis of HRV, time series of beat-to-beat cycle lengths are measured as R-R intervals under sinus rhythm. The temporal position of each R-R interval is usually defined as the position of subsequent R wave. For the purpose of frequency domain analyses, R-R interval time series are interpolated and resampled at equidistantly devided time points
Although pulse wave signal may be used as a surrogate of ECG, several limitations should be recognized, which include difficulty in beat classifications, limited accuracy in measurement of beat-to-beat cycle length, and modifications by the frequency characteristic of pulse wave conduction [27].
7.3.1.1 Standardization for Short-Term HRV Measurement
HRV is affected by various intrinsic and extrinsic factors such as environmental temperature, physical activities [28, 29], mental activities [30, 31], food intake [32], smoking [33, 34], and sleep/awake rhythm. For the assessment of autonomic function, subjects need to avoid strenuous exercise, smoking, alcohol and caffeine intake from the previous night, and food intake from 3 h before the study, and the measurement should be performed at constant time of day in an air-conditioned and calm experimental room after >15 min supine rest for equilibrium. Although the length of recording is determined by the purposes, continuous 5-min recording after the stabilization of heart rate is the standard.
For short-term HRV, controlled breathing with metronome signal may be used so that breathing frequency of subjects is kept at >0.15 Hz and heart rate to respiration ratio at >2. There are three reasons for this:
Thus, when breathing frequency is not controlled, the frequency of HF component and heart rate should be checked if these conditions are satisfied.
- 1.
To evaluate cardiac vagal function separately from sympathetic influences, breathing frequency needs to be kept at >0.15 Hz (the upper frequency limit of sympathetic heart rate control).
- 2.
The magnitude of HF component decreases with increasing breathing frequency independently of cardiac vagal activity.
- 3.
If heart rate to respiration ratio decreases to <2, respiratory fluctuation in cardiac vagal activity, if exists, is not reflected by HRV.
7.3.1.2 ECG Recordings for Long-Term HRV
For the assessment of long-term HRV, 24-h Holter ECG recordings are useful. ECG signals are digitized at 125–500 Hz in the recorders, and all R waves are classified automatically. Recent Holter ECG scanners have software for HRV analysis as the standard or optional function. For 24-h long-term HRV, there may be defect of data due to insufficient recording length or to temporary electrode troubles. In such case, care should be needed for the effects of day/night difference in HRV. Because the long-term HRV indices have been standardized as 24-h data, when the substantial data defects occurred disproportionally during daytime or nighttime, it causes bias of sampling.
7.4 Methods for HRV Analysis
A variety of measures have been used for quantifying the characteristics of HRV. They are classified by the method of analysis as shown in Table 7.1. From time domain analysis, statistical and geometric measures are calculated. These measures mainly applied to 24-h long-term recordings and used for risk stratification for mortality and adverse prognosis among patients with cardiac and other diseases. Frequency domain analysis is used for both short-term and long-term recordings. Short-term measures of HRV are used for the assessment of autonomic function, and long-term measures are used for risk stratification among patients with cardiac and other diseases. Nonlinear and fractal dynamics analyses are used mainly for long-term recordings and provide prognostic indices such as α1 of detrended fluctuation analysis (DFA) [36, 37], which is a powerful predictor of mortality risk in patients after myocardial infarction [39] and those with end-stage renal disease on chronic hemodialysis therapy [40].
Table 7.1
Measures of HRV
Variable | Unit | Definition |
|---|---|---|
1. Time domain analysis | ||
1.1 Statistical measures | ||
Mean NN | ms | Mean of all N-N intervalsa during 24 h |
SDNN | ms | Standard deviation of all N-N intervals during 24 h |
SDANN | ms | Standard deviation of the averages of N-N intervals in all 5 min segments during 24 h |
RMSSD | ms | The square root of the mean of squared differences between adjacent N-N intervals during 24 h |
SDNN index | ms | Mean of the standard deviations of all N-N intervals for all 5 min segments during 24 h |
SDSD | ms | Standard deviation of differences between adjacent N-N intervals during 24 h |
NN50 count | Number of pairs of adjacent N-N intervals differing by more than 50 ms during 24 h | |
pNN50 | % | NN50 count divided by the total number of all N-N intervals during 24 h |
1.2 Geometric measures | ||
HRV triangular index | ms | Total number of all N-N intervals during 24 h divided by the height of the histogram of all N-N intervals measured on a discrete scale with bin width of 7.8125 ms (1/128 s) |
TINN | ms | Baseline width of the minimum square difference triangular interpolation of the highest peak of the histogram of all N-N intervals during 24 h |
2. Frequency domain analysis | ||
2.1 Measures for analysis of short-term recordings (5 min) | ||
Total power | ms2 | The variance of N-N intervals during 5 min |
VLF | ms2 | Power in very-low-frequency range (≤0.04 Hz) |
LF | ms2 | Power in low-frequency range (0.04–0.15 Hz) |
LF amp | ms | Mean amplitude of LF: sqrt (2*LF) |
LF norm | n.u. | LF power in normalized unit: LF/(Total power – VLF)*100 |
HF | ms2 | Power in high-frequency range (0.15–0.4 Hz) |
HF amp | ms | Mean amplitude of HF: sqrt (2*HF) |
HF norm | n.u. | HF power in normalized unit: HF/(Total power – VLF)*100 |
LF/HF | Power ratio between LF and HF: LF(ms2)/HF(ms2) | |
2.2 Measures for long-term recordings (24 h) | ||
Total power | ms2 | The variance of N-N intervals during 24 hb |
VLF | ms2 | Power in very-low-frequency range (≤0.04 Hz)b |
LF | ms2 | Power in low frequency range (0.04–0.15 Hz)b |
HF | ms2 | Power in high-frequency range (0.15–0.4 Hz)b |
β | Power-law scaling exponent: slope of the linear regression of the spectrum in a log-log scale below 0.04 Hz | |
3. Nonlinear and fractal dynamics | ||
ApEn | Complexity of fluctuation measured by approximate entropy [35] | |
DFA index | ||
λ | Non-Gaussianity index of probability density function for abrupt changes in heart rate [38] | |
4. Others
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