heart rate variability heart rate variability heart rate variability (hrv) is the physiological phenomenon of variation in the time interval between heartbeats. it is measured by the variation in the beat-to-beat interval. heart rate variability visualized with r-r interval changes electrocardiogram (ecg) recording of a canine heart that illustrates beat-to-beat variability in r–r interval (top) and heart rate (bottom). other terms used include "cycle length variability", "r–r 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". methods used to detect beats include ecg, blood pressure, ballistocardiograms, and the pulse wave signal derived from a photoplethysmograph (ppg). ecg is considered the gold standard for hrv measurement because it provides a direct reflection of cardiac electric activity. 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 (usually lower) hrv, including congestive heart failure, diabetic neuropathy, post–cardiac-transplant depression, susceptibility to sids and poor survival in premature babies, as well as fatigue severity in chronic fatigue syndrome. psychological and social aspects a simplified representation of the neurovisceral integration model there is interest in hrv in the field of psychophysiology. for example, hrv is related to emotional arousal. high-frequency (hf) 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 (ptsd), hrv and its hf component (see below) is reduced whilst the low-frequency (lf) 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 nervous system (psns) activity and activate sympathetic nervous system (sns) 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 to decrease heart rate, while the sns works 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 (lower hrv) 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, like others, decomposes heart rate variability based on frequency domain characteristics. however, it places more emphasis on respiratory sinus arrhythmia and its transmission by a hypothesized neural pathway 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 (psns) 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 (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. decreased psns activity or increased sns activity will result in reduced hrv. high frequency (hf) activity (0.15 to 0.40 hz), especially, has been linked to psns activity. activity in this range is associated with the respiratory sinus arrhythmia (rsa), 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 (lf) activity (0.04 to 0.15 hz). 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 (or respiratory sinus 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 (traube–hering–mayer waves) of blood pressure and is usually at a frequency of 0.1 hz, or a 10-second period. artifact errors in the location of the instantaneous heart beat will result in errors in the calculation of the hrv. hrv is highly sensitive to artifact and errors in as low as even 2% of the data will result in unwanted biases in hrv calculations. to ensure accurate results therefore it is critical to manage artifact and rr errors appropriately prior to performing any hrv analyses. 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. sdann 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 ("root mean square of successive differences"), the square root of the mean of the squares of the successive differences between adjacent nns. sdsd ("standard deviation of successive differences"), 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 ("estimated breath cycle"), the range (max-min) 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 (sequential) 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 the series of nn intervals also can be converted into a geometric pattern such as: 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 (in practice 20 min to 24 h) – 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 (or rr) interval with the immediately preceding nn (or rr) interval – also called "poincare plot" or (apparently in error) a "lorenz plot"; and so forth. a simple formula is then used that judges the variability on the basis of the geometric and/or graphics properties of the resulting pattern. 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 (hf) from 0.15 to