Heart rate variability
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Heart rate variability (HRV) is a physiological phenomenon where the time interval between heart beats varies. It is measured by the variation in the beat-to-beat interval.
Other terms used include: "cycle length variability", "RR variability" (where R is a point corresponding to the peak of the QRS complex of the ECG wave; and RR is the interval between successive Rs), and "heart period variability".
See also Heart rate turbulence.
Methods used to detect beats include: ECG, blood pressure, and the pulse wave signal derived from a photoplethysmograph (PPG). ECG is considered superior because it provides a clear waveform, which makes it easier to exclude heartbeats not originating in the sinoatrial node. The term "NN" is used in place of RR to emphasize the fact that the processed beats are "normal" beats.
Contents
Clinical significance
Reduced HRV has been shown to be a predictor of mortality after myocardial infarction[1][2] although others have shown that the information in HRV relevant to acute myocardial infarction survival is fully contained in the mean heart rate[3].
A range of other outcomes/conditions may also be associated with modified (usually lower) HRV, including congestive heart failure, diabetic neuropathy, depression post-cardiac transplant, susceptibility to SIDS and poor survival in premature babies.
Variation
Variation in the beat-to-beat interval is a physiological phenomenon. The SA node receives several different inputs and the instantaneous heart rate or RR interval and its variation is the results of these inputs.
The main inputs are the sympathetic and the parasympathetic nervous systems (the two branches of the autonomic nervous system), and humoral factors. Respiration gives rise to waves in heart rate mediated primarily via the parasympathetic nervous system, and it is thought that the lag in the baroceptor feedback loop may give rise to 10 second waves in heart rate (associated with Mayer waves of blood pressure), but this remains controversial.
Factors that affect the input are the baroreflex, thermoregulation, hormones, sleep-wake cycle, meals, physical activity, and stress.
Heart rate variability phenomena
There are two primary fluctuations:
- Respiratory arrhythmia (or Respiratory sinus arrhythmia) [4][5]. This heart rate variation is associated with respiration and faithfully tracks the respiratory rate across a range of frequencies.
- Low-frequency oscillations[6]. This heart rate variation is associated with Mayer waves (Traube–Hering–Mayer waves) of blood pressure and is usually at a frequency of 0.1 Hz or a 10-second period.
HRV analysis
The most widely used methods can be grouped under time-domain and frequency-domain. Other methods have been proposed, such as non-linear methods.
Time-domain methods
These are based on the beat-to-beat or NN intervals, which are analysed to give variables such as:
- SDNN, the standard deviation of NN intervals. Often calculated over a 24-hour period.
- SDANN, the standard deviation of the average NN intervals calculated over short periods, usually 5 minutes. SDANN is therefore a measure of changes in heart rate due to cycles longer than 5 minutes.
- RMSSD, the square root of the mean squared difference of successive NNs.
- NN50, the number of pairs of successive NNs that differ by more than 50 ms.
- pNN50, the proportion of NN50 divided by total number of NNs.
Frequency-domain methods
Several methods are available. Power spectral density (PSD), using parametric or nonparametric methods, provides basic information on the power distribution across frequencies. One of the most commonly used PSD methods is the discrete Fourier transform.
Non-linear methods
Given the complexity of the mechanisms regulating heart rate, it is reasonable to assume that applying HRV analysis based on methods of non-linear dynamics will yield valuable information. Although chaotic behavior has been assumed, more rigorous testing has shown that heart rate variability cannot be described as a chaotic process. The most commonly used non-linear method of analysing heart rate variability is the Poincaré plot. Each data point represents a pair of successive beats, the x-axis is the current RR interval, while the y-axis is the previous RR interval. HRV is quantified by fitting mathematically defined geometric shapes to the data [7]. Other methods used are the correlation dimension, nonlinear predictability [8], pointwise correlation dimension and approximate entropy[9].
Sources
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External links
- review article on the mechanisms of cardiovascular variability in the Journal of Physiologyde:Herzfrequenzvariabilität
fr:Variabilité de fréquence cardiaque hu:Szívfrekvencia-variabilitás pl:Zmienność rytmu zatokowego sv:Heart rate variability
zh:心率變異分析- ↑ Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN. (1992). "Frequency domain measures of heart period variability and mortality after myocardial infarction". Circulation. 85 (1): 164–171. PMID 1728446. Check
|pmid=value (help). - ↑ Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. (1987). "Decreased heart rate variability and its association with increased mortality after acute myocardial infarction". Am J Cardiol. 59 (4): 256–262. PMID 3812275.
- ↑ Abildstrom SZ, Jensen BT, Agner E; et al. (2003). "Heart rate versus heart rate variability in risk prediction after myocardial infarction". Journal of Cardiovascular Electrophysiology. 14 (2): 168–73. PMID 12693499.
- ↑ Hales S. Statistical Essays: Containing Haemastaticks. London, UK: Innys, Manby and Woodward; 1733.
- ↑ von Haller A. Elementa Physiologica. Lausanne, Switzerland: 1760; T II, Lit VI, 330
- ↑ Sayers (1973). "Analysis of Heart Rate Variability". Ergonomics. 16 (1): 17–32. PMID 4702060.
- ↑ Brennan M,Palaniswami M, Kamen P. Do existing measures of Poincaré plot geometry reflect non-linear features of heart rate variability? Biomedical Engineering, IEEE Transactions on, Proc. IEEE Transactions on Biomedical Engineering, 2001, 48, 1342-1347
- ↑ Kanters JK, Holstein-Rathlou NH, Agner E (1994). "Lack of evidence for low-dimensional chaos in heart rate variability". Journal of Cardiovascular Electrophysiology. 5 (7): 591–601. PMID 7987529.
- ↑ Storella RJ, Wood HW, Mills KM; et al. (1994). "Approximate entropy and point correlation dimension of heart rate variability in healthy subjects". Integrative Physiological & Behavioral Science. 33 (4): 315–20. PMID 10333974.
