Heart rate variability
Heart rate variability is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-to-beat interval.
Other terms used include: "cycle length variability", "RR variability", and "heart period variability".
Methods used to detect beats include: ECG, blood pressure,
ballistocardiograms,
and the pulse wave signal derived from a photoplethysmograph. 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.
Clinical significance
Reduced HRV has been shown to be a predictor of mortality after myocardial infarction although others have shown that the information in HRV relevant to acute myocardial infarction survival is fully contained in the mean heart rate.A range of other outcomes and conditions may also be associated with modified HRV, including congestive heart failure, diabetic neuropathy, post–cardiac-transplant depression, susceptibility to SIDS and poor survival in premature babies.
Psychological and social aspects
There is interest in HRV in the field of psychophysiology. For example, HRV is related to emotional arousal. High-frequency activity has been found to decrease under conditions of acute time pressure and emotional strain and elevated anxiety state, presumably related to focused attention and motor inhibition. HRV has been shown to be reduced in individuals reporting to worry more. In individuals with post-traumatic stress disorder, HRV and its HF component is reduced whilst the low-frequency component is elevated. Furthermore, PTSD patients demonstrated no LF or HF reactivity to recalling a traumatic event.The neurovisceral integration is a model of HRV that views the central autonomic network as the decision maker of cognitive, behavioral and physiological regulation as they pertain to a continuum of emotion. The neurovisceral integration model describes how the prefrontal cortex regulates activity in limbic structures which act to suppress parasympathetic activity and activate sympathetic circuits. Variation in the output of these two branches of the autonomic system produces HRV and activity in the prefrontal cortex can hence modulate HRV.
HRV is the measure of the inconsistent gaps between each heartbeat and is used as an index for different aspects of psychology. HRV is reported to be an index of the influence of both the parasympathetic nervous system and the sympathetic nervous systems. Different aspects of psychology represent the balance of these two influences. For example, high HRV is shown proper emotion regulation, decision-making, and attention, and low HRV reflects the opposite. The parasympathetic nervous system works quickly to decrease heart rate, while the SNS works slowly to increase heart rate, and this is important because it applies to the different psychological states mentioned above. For example, someone with high HRV may reflect increased parasympathetic activity, and someone with low HRV may reflect increased sympathetic activity.
Emotions stem from the time and impact of a situation on a person. The ability to regulate emotions is essential for social environments and well-being. HRV has provided a window to the physiological components associated with emotional regulation. HRV has been shown to reflect emotional regulation at two different levels, while resting and while completing a task. Research suggests that a person with higher HRV while resting can provide more appropriate emotional responses compared to those that have low HRV at rest. Empirical research found that HRV can reflect better emotional regulation by those with higher resting HRV, particularly with negative emotions. When completing a task, HRV is subject to change, especially when people need to regulate their emotions. Most importantly, individual differences are related to the ability to regulate emotions. Not only is emotional regulation necessary, but so is attention.
Previous research has suggested that a large part of the attention regulation is due to the default inhibitory properties of the prefrontal cortex. Top-down processes from the prefrontal cortex provide parasympathetic influences, and if for some reason, those influences are active, attention can suffer. For example, researchers have suggested that HRV can index attention. For example, a group of researchers found that groups with high anxiety and low HRV have poor attention. In line with this research, it has also been suggested that increased attention has been linked to high HRV and increased vagus nerve activity. The vagus nerve activity reflects the physiological modulation of the parasympathetic and sympathetic nervous system. The activity behind the prefrontal cortex and the parasympathetic and sympathetic nervous system can influence heart activity. However, people are not all affected the same. A systematic review of HRV and cognitive function suggested that resting HRV can predict individual differences in attentional performance. Even in psychological concepts such as attention, HRV can index individual differences. Furthermore, HRV has been able to index the role of attention and performance, supporting high HRV as a biomarker of increased attention and performance. Both emotion and attention can shed light on how HRV is used as an index for decision making.
Decision-making skills are found to be indexed by HRV in several studies. Previous research has suggested that both emotion and attention are linked to decision making; for example, poor decision making is linked to the inability to regulate or control emotions and attention and vice versa. Decision making is negatively affected by lower HRV and positively affected by higher levels of HRV. Most importantly, resting-state HRV was found to be a significant predictor of cognitive functions such as decision making. HRV, accompanied by a psychological state, such as anxiety, has been found to lead to poor decisions. For example, a group of researchers found that low HRV was an index of higher uncertainty leading to poor decision-making skills, especially those with higher levels of anxiety. HRV was also used to assess decision-making skills in a high-risk game and was found to be an index higher sympathetic activation when making decisions involving risk. HRV can index psychological concepts, such as the ones outlined above, to assess the demand for the situations that people experience.
The polyvagal theory is another way to describe the pathways in the autonomic nervous system that mediate HRV. The polyvagal theory highlights three main ordinal processes, inactive response to an environmental threat, the active response to an environmental threat, and the fluctuation between the connect and disconnect to an environmental threat. This theory decomposes heart rate variability based on frequency domain characteristics with an emphasis on respiratory sinus arrhythmia and its transmission by a neural pathway that is distinct from other components of HRV. There is anatomic and physiological evidence for a polyvagal control of the heart.
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 are the results of these inputs.The main inputs are the sympathetic and the parasympathetic nervous system and humoral factors. Respiration gives rise to waves in heart rate mediated primarily via the PSNS, and it is thought that the lag in the baroreceptor feedback loop may give rise to 10 second waves in heart rate, but this remains controversial.
Factors that affect the input are the baroreflex, thermoregulation, hormones, sleep-wake cycle, meals, physical activity, and stress.
Decreased PSNS activity or increased SNS activity will result in reduced HRV. High frequency activity, especially, has been linked to PSNS activity. Activity in this range is associated with the respiratory sinus arrhythmia, a vagally mediated modulation of heart rate such that it increases during inspiration and decreases during expiration. Less is known about the physiological inputs of the low frequency activity. Though previously thought to reflect SNS activity, it is now widely accepted that it reflects a mixture of both the SNS and PSNS.
Phenomena
There are two primary fluctuations:- Respiratory arrhythmia. This heart rate variation is associated with respiration and faithfully tracks the respiratory rate across a range of frequencies.
- Low-frequency oscillations. This heart rate variation is associated with Mayer waves of blood pressure and is usually at a frequency of 0.1 Hz, or a 10-second period.
Artifact
Robust management of artifacts, including RWave identification, interpolation and exclusion requires a high degree of care and precision. This can be very time consuming in large studies with data recorded over long durations. Software packages are able to assist users with a variety of robust and tested artifact management tools. These software programs also include some automated capability but it is important that a human review any automated artifact management and edit accordingly.
Analysis
The most widely used methods can be grouped under time-domain and frequency-domain. A joint European and American task-force described standards in HRV measurements in 1996. 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. SDNN is therefore a measure of changes in heart rate due to cycles longer than 5 minutes. SDNN reflects all the cyclic components responsible for variability in the period of recording, therefore it represents total variability.
- RMSSD, the square root of the mean of the squares of the successive differences between adjacent NNs.
- SDSD, the standard deviation of the successive differences between adjacent 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.
- NN20, the number of pairs of successive NNs that differ by more than 20 ms.
- pNN20, the proportion of NN20 divided by total number of NNs.
- EBC, the range within a moving window of a given time duration within the study period. The windows can move in a self-overlapping way or be strictly distinct windows. EBC is often provided in data acquisition scenarios where HRV feedback in real time is a primary goal. EBC derived from PPG over 10-second and 16-second sequential and overlapping windows has been shown to correlate highly with SDNN.
Geometric methods
Geometric Measures HRV triangular index: integral of density distribution / maximum of density distribution maximum
HRV triangular index = Number of all NN intervals / maximum number. Dependent on the length of the bin -> quote the bin size+ relative insensitive to the analytic quality of the series of NN intervals - need of reasonable number of NN intervals to generate the geometric pattern -not appropriate to assess short-term changes in HRV
- the sample density distribution of NN interval durations;
- sample density distribution of differences between adjacent NN intervals;
- a scatterplot of each NN interval with the immediately preceding NN interval — also called "Poincare plot" or a "Lorenz plot";
Frequency-domain methods
Frequency domain methods assign bands of frequency and then count the number of NN intervals that match each band. The bands are typically high frequency from 0.15 to 0.4 Hz, low frequency from 0.04 to 0.15 Hz, and the very low frequency from 0.0033 to 0.04 Hz.Several methods of analysis are available. Power spectral density, 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.
Methods for the calculation of PSD may be generally classified as nonparametric and parametric. In most instances, both methods provide comparable results. The advantages of the nonparametric methods are the simplicity of the algorithm used and the high processing speed. The advantages of parametric methods are smoother spectral components that can be distinguished independent of preselected frequency bands, easy postprocessing of the spectrum with an automatic calculation of low- and high-frequency power components with an easy identification of the central frequency of each component, and an accurate estimation of PSD even on a small number of samples on which the signal is supposed to maintain stationarity. The basic disadvantage of parametric methods is the need of verification of the suitability of the chosen model and of its complexity.
In addition to classical FFT-based methods used for the calculation of frequency parameters, a more appropriate PSD estimation method is the Lomb–Scargle periodogram. Analysis has shown that the LS periodogram can produce a more accurate estimate of the PSD than FFT methods for typical RR data. Since the RR data is an unevenly sampled data, another advantage of the LS method is that in contrast to FFT-based methods it is able to be used without the need to resample and detrend the RR data.
Alternatively, to avoid artefacts that are created when calculating the power of a signal that includes a single high-intensity peak, the concept of the 'instantaneous Amplitude' has been introduced, which is based on the Hilbert transform of the RR data.
A newly used HRV index, which depends on the wavelet entropy measures, is an alternative choice. The wavelet entropy measures are calculated using a three-step procedure defined in the literature. First, the wavelet packet algorithm is implemented using the Daubechies 4 function as the mother wavelet with a scale of 7. Once the wavelet coefficients are obtained, the energy for each coefficient are calculated as described in the literature. After calculating the normalized values of wavelet energies, which represent the relative wavelet energy, the wavelet entropies are obtained using the definition of entropy given by Shannon.
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 low dimensional chaotic process. However, application of chaotic globals to HRV has been shown to predict diabetes status. 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. Other methods used are the correlation dimension, symbolic dynamics, nonlinear predictability, pointwise correlation dimension, detrended fluctuation analysis,approximate entropy, sample entropy, multiscale entropy analysis, sample asymmetry and memory length. It is also possible to represent long range correlations geometrically.
Long term correlations
Sequences of RR intervals have been found to have long-term correlations. However, one flaw with these analyses is their lack of goodness-of-fit statistics, i.e. values are derived that may or may not have adequate statistical rigor. Different types of correlations have been found during different sleep stages.Cross correlation with other systems
The question of how heartbeat rhythms are correlated with other physiologic systems such as lung and brain was studied by Bashan et al . It is found that while during wake, light and REM sleep the correlation between heartbeat with other physiological systems are high, they almost vanish during deep sleep.Duration and circumstances of ECG recording
Time domain methods are preferred to frequency domain methods when short-term recordings are investigated. This is due to the fact that the recording should be at least 10 times the wavelength of the lowest frequency bound of interest. Thus, recording of approximately 1 minute is needed to assess the HF components of HRV, while more than 4 minutes are needed to address the LF component.Although time domain methods, especially the SDNN and RMSSD methods, can be used to investigate recordings of long durations, a substantial part of the long-term variability is day–night differences. Thus, long-term recordings analyzed by time domain methods should contain at least 18 hours of analyzable ECG data that include the whole night.
Physiological correlates of HRV components
Autonomic influences of heart rate
Although cardiac automaticity is intrinsic to various pacemaker tissues, heart rate and rhythm are largely under the control of the autonomic nervous system. The parasympathetic influence on heart rate is mediated via release of acetylcholine by the vagus nerve. Muscarinic acetylcholine receptors respond to this release mostly by an increase in cell membrane K+ conductance. Acetylcholine also inhibits the hyperpolarization-activated "pacemaker" current. The "Ik decay" hypothesis proposes that pacemaker depolarization results from slow deactivation of the delayed rectifier current, Ik, which, due to a time-independent background inward current, causes diastolic depolarization. Conversely, the "If activation" hypothesis suggests that after action potential termination, If provides a slowly activating inward current predominating over decaying Ik, thus initiating slow diastolic depolarization.The sympathetic influence on heart rate is mediated by release of epinephrine and norepinephrine. Activation of β-adrenergic receptors results in cAMP-mediated phosphorylation of membrane proteins and increases in ICaL and in If the end result is an acceleration of the slow diastolic depolarization.
Under resting conditions, vagal tone prevails and variations in heart period are largely dependent on vagal modulation. The vagal and sympathetic activity constantly interact. Because the sinus node is rich in acetylcholinesterase, the effect of any vagal impulse is brief because the acetylcholine is rapidly hydrolyzed. Parasympathetic influences exceed sympathetic effects probably through two independent mechanisms: a cholinergically induced reduction of norepinephrine released in response to sympathetic activity, and a cholinergic attenuation of the response to an adrenergic stimulus.
Components
The RR interval variations present during resting conditions represent beat-by-beat variations in cardiac autonomic inputs. However, efferent vagal activity is a major contributor to the HF component, as seen in clinical and experimental observations of autonomic maneuvers such as electrical vagal stimulation, muscarinic receptor blockade, and vagotomy. More problematic is the interpretation of the LF component, which was considered by some as a marker of sympathetic modulation but is now known to include both sympathetic and vagal influences. For example, during sympathetic activation the resulting tachycardia is usually accompanied by a marked reduction in total power, whereas the reverse occurs during vagal activation. Thus the spectral components change in the same direction and do not indicate that LF faithfully reflects sympathetic effects.HRV measures fluctuations in autonomic inputs to the heart rather than the mean level of autonomic inputs. Thus, both withdrawal and saturatingly high levels of autonomic input to the heart can lead to diminished HRV.