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.
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 R–R is the interval between successive Rs), and heart period variability. Measurement of the R–R interval (often termed normal-to-normal or N–N interval when additional filtering is used) is used to derive heart rate 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.
Variability
Variability (or Variation) in the beat-to-beat interval is a physiological phenomenon. Power spectral analysis of the beat-to-beat variations of heart rate or the heart period (R–R interval) partitions the total variance (the "power") of a continuous series of beats into its frequency components, typically identifying two or three main peaks: Very Low Frequency (VLF) <0.04 Hz, Low Frequency (LF), 0.04–0.15 Hz, and High Frequency (HF) 0.15–0.4 Hz.
The HF peak is widely believed to reflect cardiac parasympathetic nerve (PSNS) activity while the LF, although more complex, is often assumed to have a dominant sympathetic (SNS) component. Though LF was previously thought to reflect primarily SNS activity, it is now widely accepted that it reflects a mixture of both the SNS and PSNS.
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 (which 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).
The SA node receives several different inputs and the instantaneous heart rate or RR interval and its variation are the results of such 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.
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, and fatigue severity in chronic fatigue syndrome. On the other hand, for patients having high blood pressure (hypertension), higher HRV is a risk factor for atrial fibrillation.
Psychological and social aspects
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 is reduced whilst the low-frequency (LF) component is elevated. Furthermore, PTSD patients demonstrated no LF or HF reactivity to recalling a traumatic event. Statistical quantitative differences have also been found among healthy, depressed, and psychotic people.
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 reported to be an index of the influence of both the parasympathetic and the sympathetic nervous system. For example, high HRV is shown to reflect proper emotion regulation, decision-making, and attention, and low HRV reflects the opposite. The parasympathetic nervous system works to decrease the heart rate, while the SNS works to increase the heart rate. 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. However, HRV is elevated by negative news in persons who react more strongly to negative news than to positive news. 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.
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. Researchers have suggested that HRV can index attention. It was 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. Furthermore, HRV has been able to index the role of attention and performance, supporting high HRV as a biomarker of increased attention and performance.
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.
Phenomena
There are two primary fluctuations: Respiratory arrhythmia (or respiratory sinus arrhythmia)von Haller A. Elementa Physiologica. Lausanne, Switzerland: 1760; T II, Lit VI, 330 is directly caused by the central respiratory rhythm, which tracks the respiratory rate across a range of frequencies and is a major cause of heart rate variability in humans. The respiratory rhythm contributes to sinus arrhythmia in normal unanesthetized subjects during mechanical hyperventilation with positive pressure. Low-frequency oscillations are 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.
Pattern formation in heart rate The regulation of heart rate, encompassing both acceleration and deceleration of the heartbeat, is highly specific. Distinct patterns in heart rate can be observed during everyday movements, such as kneeling down or standing up. These patterns reflect highly sensitive physiological regulatory mechanisms that enable a healthy heart to adapt to various life influences.
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 Time-domain methods are based on the beat-to-beat or NN intervals (typically determined from RR intervals), which are analysed to give variables such as: SDNN (standard deviation of NN intervals). Often calculated over a 24-hour period. SDNN reflects all the cyclic components responsible for variability in the period of recording, therefore it represents total variability.