**HEART RATE VARIABILITY (HRV)**

It 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.

**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.

**Mental and social aspects**

In the field of psychophysiology, there is great interest in HRV. For example, decreased HRV may be apparent in the presence of depression. Moreover, the Polyvagal Theory derives from a psychophysiologic imputation of importance to HRV. This theory emphasizes the role of heart rate variability in understanding the magnitude and nature of vagal outflow to the heart. However, there is no anatomic or physiologic 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 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.

**No****n****-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]