Heart Rate Variability 317

Introduction

Heart rate variability (HRV) reflects the dynamic interplay between sympathetic and parasympathetic influences on the heart. Its analysis provides a non-invasive window into autonomic regulation, helping assess sympathovagal balance and identify disturbances in cardiovascular autonomic function.

Physiological Aspects

Heart rate variability (HRV) is the cardiac beat-to-beat variation (variation in cardiac cycle length), a physiological phenomenon that occurs mainly due to variation in cardiac activity during the respiratory cycle (respiratory sinus arrhythmia) at rest, though the circadian rhythm, environmental factors and exercise also contribute to it. Resting heart rates can vary; some have rates of 100 beats/min while others beat at only 60 beats/min for no obvious reason.

1. The rate of the heart and its beat-to-beat variations are dependent on the rate of discharge of the primary pacemaker, the SA node, which is influenced by autonomic activities that are controlled in a complex way by a variety of reflexes, central irradiations and cortical factors.
2. As SA nodal discharge is largely controlled by parasympathetic (vagal) influence, and sinus arrhythmia is primarily due to alteration in vagal tone in inspiration and expiration, HRV is mainly influenced by vagal activity, though both the divisions of ANS influence it.
3. Recently, HRV has been proposed as the most sensitive indicator of autonomic function, especially for the assessment of sympathovagal balance, the balance between the sympathetic and parasympathetic activity of the individual at any given time.
4. The state of sympathovagal balance is used for the prediction of many cardiovascular (CV) dysfunctions and other dysfunctions affecting cardiovascular function, its main use is in the CV risk stratification. However, the use of HRV analysis is limited in the diagnosis and management of CV and other diseases.

Technical Terms

Heart rate variability can be quantified in time and frequency domains.

1. Time domain measures include the usual tools of assessment of variations, as done in statistics. Time domain is easier to assess but finer aspects of variations are not appreciated. Shortly, the overall magnitude of HRV is assessed well but the individual contribution of various factors is not elucidated.
2. On the other hand, the variations in the instantaneous heart rate can be assessed spectrally. That is, an RR tachogram is plotted using the RR intervals in the five-minute lead II ECG.
3. The RR tachogram is considered as a non-periodic signal which is transformed to its frequency spectrum using fast-Fourier transformation algorithm or autoregressive modeling.
4. The biggest advantage of this complex mathematical transformation is that the distribution of magnitude of variations in different frequency bands corresponds to the activity of different physiological systems. The entire frequency spectrum 0 to 0.4 Hz is divided as follows:

HRV Components

The power spectrum of HRV in mammals usually reveals three spectral components. These components are (Fig. 35.1):

1. A high frequency band (HF) 0.15–0.4 Hz
2. A low-frequency band (LF) 0.04–0.15 Hz
3. A very low-frequency band (VLF) 0–0.04 Hz

HF Component

HF component is caused by vagal tone during the respiratory cycle.

1. The inspiratory inhibition of vagal activity is evoked centrally in the cardiovascular center and explains why heart rate fluctuates with the respiratory frequency.
2. In addition, peripheral reflexes arising from thoracic stretch receptors also contribute to this so-called respiratory sinus arrhythmia (RSA).
3. As RSA is clearly abolished by atropine or vagotomy, the power of the HF component has been used as an index of the vagal drive.

LF Component

LF component of HRV is usually characterized by an oscillatory pattern with a period of 10 seconds.

1. This rhythm originates from self-oscillation in the vasomotor part (sympathetic component) of the baroreflex loop as a result of negative feedback and it is commonly associated with synchronous fluctuations in blood pressure, the so-called Mayer waves.
2. Thus, LF component mainly represents sympathetic power. However, parasympathetic drive also contributes to it.

VLF Component

VLF component accounts for all other heart rate changes, including those associated with thermoregulation and humoral (especially, rennin-angiotensin mechanism) and local factors.

Power Spectrum Analysis of HRV

Power spectrum of HRV is analyzed by two methods: Fast-Fourier transform and autoregressive modeling.

Fast Fourier Transform

Any electrophysiological signal can be described as a sum of sine waves and this decomposition is called the Fast Fourier Transform (FFT). An efficient algorithm to carry out this transformation is the FFT, which, with some improvements and modifications, is still in use in many applications, such as voice analysis or vibration studies.

1. The analysis of short-term HRV (SHRV) is another one of these applications.
2. FFT algorithms impose some constraints on the signal to analyze because an evenly sampled, infinite and stationary time series is required.

Autoregressive Modeling

An alternative method to the FFT is the autoregressive (AR) identification algorithm combined with power spectral estimation for the assessment of SHRV. This method fits the data to a prior defined model and estimates the parameters of the model. The power spectrum implied by the model is then computed.

1. FFT or AR modeling methods share a common goal: the estimation of the power spectrum of a signal.
2. FFT-based methods are also called nonparametric methods because the time domain prior to spectral analysis is greatly simplified.

Physiological basis: The FFT and autoregressive algorithms are the most commonly used tools to study the SHRV.

1. The final step in SHRV analysis includes the application of power spectrum estimation methods to characterize the frequency components associated with vagal and/or sympathetic outflow.
2. AR methods are parametric because they require prior information of the system under study. Thus, it was suggested that FFT-based methods are still the best choice for the assessment of SHRV in comparative studies, where no previous knowledge of the system is available.
3. In addition, FFT algorithms are readily available in many different languages, even in commercial statistical packages.
4. Once the basic spectral content of the system is known and an initial model of the signal can be formulated, AR algorithms should be a better choice because they provide better frequency resolution and avoid the problems of spectral leakage.
5. The electrocardiogram (ECG) is the most appropriate signal to study SHRV because it offers the most accurate representation of the electrical cardiac events.
6. In particular, the QRS complex of the ECG sharply defines the onset of ventricular electrical depolarization and is the closest approach to time the occurrence of pacemaker potentials, which, in turn, are modulated by the autonomic outflow.

HRV Indices

Analysis of HRV has two parts: time-domain and frequency-domain.

1. HRV assessed by calculation of indices is based on statistical operations on R-R intervals (time domain analysis) or by spectral analysis of an array of R-R intervals (frequency domain analysis).
2. Both methods require accurate timing of R waves.
3. The analysis can be performed on short electrocardiogram (ECG) segments (lasting from 0.5 to 5 minutes) or on 24-hour ECG recordings.
4. The analysis of 5 min ECG recording is called short term HRV and of 24 h ECG recording is called long term HRV.

Time Domain Analysis

Two types of heart rate variability indices are distinguished in time domain analysis. Beat-to-beat or short-term variability (STV) indices represent fast changes in heart rate. Long-term variability (LTV) indices are slower fluctuations (fewer than 6 per minute). Both types of indices are calculated from the R-R intervals occurring in a chosen time window (usually between 0.5 and 5 minutes).

1. An example of a simple STV index is the standard deviation (SD) of beat-to-beat R-R interval differences within the time window.
2. Examples of LTV indices are the SD of all the R-R intervals, or the difference between the maximum and minimum R-R interval length, within the window.
3. With calculated heart rate variability indices, respiratory sinus arrhythmia contributes to STV, and baroreflex- and thermoregulation-related heart rate variabilities contributes to LTV.

Frequency Domain Analysis

Ever since spectral analysis was introduced as a method to study heart rate variability, an increasing number of investigators have preferred this method to time domain analysis for the calculation of heart rate variability indices.

1. The main advantage of spectral analysis of signals is the possibility to study their frequency-specific oscillations.
2. Thus, not only the amount of variability but also the oscillation frequency (number of heart rate fluctuations per second) can be obtained.
3. Spectral analysis involves decomposing the series of sequential R-R intervals into a sum of sinusoidal functions of different amplitudes and frequencies by the Fourier transform algorithm.
4. The result can be displayed (power spectrum) with the magnitude of variability as a function of frequency. Thus, the power spectrum reflects the amplitude of the heart rate fluctuations present at different oscillation frequencies.

Measurement of HRV Indices

Time Domain Methods

The variations in heart rate may be evaluated by a number of methods. Perhaps the simplest to perform are the time domain measures.

1. In these methods, either the heart rate at any point in time or the intervals between successive normal complexes are determined.
2. In a continuous ECG record, each QRS complex is detected, and the so-called normal-to-normal (NN) intervals, i.e. all intervals between adjacent QRS complexes resulting from sinus node depolarization or in the instantaneous heart rate are determined.
3. Simple time domain variables that can be calculated include the mean NN interval, the mean heart rate, the difference between the longest and shortest NN interval, the difference between night and day heart rate and so forth.

Statistical Methods

From a series of instantaneous heart rates or cycle intervals, particularly those recorded over longer periods, traditionally 24 hours, more complex statistical time domain measures can be calculated.

1. These may be divided into two classes: (a) Those derived from direct measurements of the NN intervals or instantaneous heart rate, and (b) those derived from the differences between NN intervals.
2. These variables may be derived from analysis of the total ECG recording or may be calculated using smaller segments of the recording period.
3. The most commonly used measures derived from interval differences include RMSSD, the square root of the mean squared differences of successive NN intervals; NN50, the number of interval differences of successive NN intervals greater than 50 ms; and pNN50; the proportion derived by dividing NN50 by the total number of NN intervals (Table 35.1).
4. All of these measurements of the short-term variation estimate high-frequency variations in heart rate and, thus, are highly correlated.

Geometrical Methods

A series of NN intervals also can be converted into a geometric pattern, such as the sample density distribution of NN interval durations, sample density distribution of difference between adjacent NN intervals, Lorenz plot of NN or RR intervals and so forth.

1. A simple formula is used that judges the variability on the basis of the geometric and/or graphics properties of the resulting pattern.
2. The HRV triangular index measurement is the integral of the density distribution (that is, the number of all NN intervals) divided by the maximum of the density distribution.
3. The major advantage of the geometric methods lies in their relative insensitivity to the analytical quality of the series of NN intervals.
4. The major disadvantage of the geometric methods is the need for a reasonable number of NN intervals to construct the geometric pattern.

The methods expressing overall HRV and its long-and short-term components cannot replace each other. The selection of method used should correspond to the aim of each particular study.

Table 35.1: Selected time-domain measures of HRV.

Variable	Units	Description
SDNN	ms	Standard deviation of all NN intervals
SDANN	ms	Standard deviation of the averages of NN intervals in all 5 min segments of the entire recording
RMSSD	ms	The square root of the mean of the sum of the squares of the differences between adjacent NN intervals
SDNN index	ms	Mean of the standard deviations of all NN intervals for all 5 min segments of the entire recording
SDSD	ms	Standard deviation of differences between adjacent NN intervals
NN50 count		Number of pairs of adjacent NN interval differing by more than 50 ms in the entire recording
pNN50	%	NN50 count divided by the total number of all NN intervals

Frequency Domain Methods

Various spectral methods for the analysis of the tachogram have been applied since the late 1960s.

1. Power spectral density (PSD) analysis provides the basic information of how power (variance) distributes as a function of frequency.
2. Independent of the method used, only an estimate of the true PSD of the signal can be obtained by proper mathematical algorithms.

Nonparametric and Parametric Methods

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 nonparametric methods are:

1. The simplicity of the algorithm used [fast Fourier transform (FFT)] in most of the cases.
2. The high processing speed.

The advantages of parametric methods are:

1. Smoother spectral components that can be distinguished independent of preselected frequency bands.
2. Easy post processing of the spectrum with an automatic calculation of low- and high-frequency power components with easy identification of the central frequency of each component.
3. An accurate estimation of PSD even on a small number of samples on which the signal is supposed to remain stationary.

The basic disadvantage of parametric methods is the need for verification of the suitability of the chosen model and of its complexity (that is, the order of the model).

Spectral Components of Frequency Domain

Short-term Recordings

Three main spectral components are distinguished in a spectrum calculated from short term recordings of 2 to 5 minutes: VLF, LF and HF components (Table 35.2). The distribution of the power and the central frequency of LF and HF are not fixed but may vary in relation to changes in autonomic modulations of heart period. The physiological explanation of the VLF component is much less defined and the existence of a specific process attributable to these changes might even be questioned. The non-harmonic component, which does not have coherent properties and is affected by algorithms of baseline or trend removal, is commonly accepted as a major constituent of VLF. Thus VLF assessed from short-term recordings (≤ 5 minutes) is a dubious measure and should be avoided when the PSD of short-term ECG is interpreted.

1. The measurement of VLF, LF and HF power components is usually made in absolute values of power (milliseconds squared).
2. LF and HF may also be measured in normalized units, which represent the relative value of each power component in proportion to the total power minus the VLF component.
3. The representation of LF and HF in normalized units (LFnu and HFnu) emphasizes the controlled and balanced behavior of the two branches of the autonomic nervous system. Moreover, the normalization tends to minimize the effect of the changes in total power on the values of LF and HF components.
4. Nevertheless, normalized units should always be quoted with absolute values of LF and HF power in order to describe completely the distribution of power in spectral components.
5. LF-HF ratio provides a better indicator of spectral powers.

Long-term Recordings

Spectral analysis also may be used to analyze the sequence of NN intervals of the entire 24-hour period. The result then includes an ultra-low frequency (ULF) component, in addition to VLF, LF and HF components. The slope of the 24-hour spectrum also can be assessed on a log-log scale by linear fitting the spectral values. Frequency domain measures are summarized below.

Table 35.2: Selected frequency domain measures of HRV

Variable	Units	Description analysis of short-term recordings (5 mins)	Frequency range
Total power (5 min)	ms²	The variance of NN intervals over the temporal segment	Approximately ≤ 0.4 Hz
VLF	ms²	Power in very low-frequency range	0–0.04 Hz
LF	ms²	Power in low frequency range	0.04 – 0.15 Hz
LF norm	nu	LF power in normalized units LF/(Total Power – VLF) x 100
HF	ms²	Power in high frequency range	0.15–0.4 Hz
HF norm	nu	HF power in normalized units HF/(Total Power – VLF) x 100
LF/HF		Ratio LF (ms²)/HF (ms²)

(TP: total power; nu: normalized unit)

Technical Aspects

The basic principle is that beat-to-beat variation in SA nodal discharge as recorded by ECG is computed and analyzed by the software for determination of spectral indices of HRV.

Brief methodology: There are two types of HRV recordings: the short-term 5 min HRV recording and daynight long-term HRV recording. Though long-term HRV recording is the ideal one, short-term HRV recording is usually performed for research and clinical investigations. We shall briefly describe the procedure of short-term recording as depicted in the Task Force Report on HRV.

1. The subject is asked to lie down comfortably in supine position in the laboratory, and ECG electrodes are connected for Lead II ECG recording.
2. After 5 minutes of supine rest, ECG signals are acquired at a rate of 1000 samples/second during supine rest using data acquisition system, such as BIOPAC MP 100 (BIOPAC Inc., USA) (minimum 250 Hz sampling rate). The raw ECG signal and the RR intervals are acquired on a moving time base.
3. Data from BIOPAC are transferred to a windows-based PC loaded with software for HRV analysis, such as Acknowledge software version 3.8.2. Ectopics and artifacts are removed from the recorded ECG.
4. RR tachogram is extracted from the edited 256 sec ECG using the R wave detector in the Acknowledge software and saved in ASC-II format, which is later used offline for short-term HRV analysis (RR tachogram should have minimum 288 RR intervals) (Fig. 35.2).
5. HRV analysis is performed by using the HRV analysis software version 1.1 (Bio-signal Analysis group, Finland).

Calculation of Time Domain Indices

In a continuous ECG record, each QRS complex is detected, and the so-called normal to-normal (NN) intervals (i.e. all intervals between adjacent QRS complexes resulting from sinus node depolarizations) or instantaneous heart rate is determined. Simple time domain variables that are calculated include:

1. The mean RR
2. Standard deviation of normal-to-normal interval (SDNN)
3. Square root of the mean squared differences of successive normal-to-normal intervals (RMSSD) of HRV.
4. NN50
5. pNN50

Calculation of Frequency Domain Indices

Frequency domain variables that are usually calculated include total power (TP), low frequency (LF) component, LF component expressed as normalized unit (LFnu), high frequency (HF) component, HF component expressed as normalized unit (HFnu) and LF/HF ratio (Table 35.2). Normalizing spectral powers are calculated by the formula as follows:

1. LF nu = LF x 100 (TP – VLF)
2. HF nu = HF x 100 (TP – VLF)
3. LF/ HF ratio = Ratio of LF to HF spectral powers

Importance Of HRV Analysis

Physiological Significance

HRV analysis is used to precisely assess the efficiency of vagal control of the individual, as it reflects the heart rate variability that occurs mainly due to sinus arrhythmia.

1. Due to inspiratory inhibition of the vagal tone, the heart rate shows fluctuations with a frequency similar to the respiratory rate.
2. The inspiratory inhibition is evoked primarily by central irradiation of impulses from the medullary respiratory to the cardiovascular center.
3. Respiratory sinus arrhythmia can be abolished by atropine or vagotomy as it is parasympathetically mediated.

HRV Analysis for Assessment of Sympathovagal Balance

HRV, that is, the degree of heart rate fluctuations around the mean heart rate, can be used as a mirror of the cardiorespiratory control system. It is a valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system. SA nodal activity at any particular time is determined by the balance between vagal activity, which slows it, and sympathetic activity, which accelerates it. Generally, if the rate is lower than the intrinsic rate of the pacemaker, it implies predominant vagal activity, while high heart rates are achieved by increased sympathetic drive.

1. The HF component of HRV indicates the cardiac vagal drive of the individual. Increased HF power (or more specifically, increased HFnu) represents increased vagal drive and decreased HF power (decreased HF nu) represents decreased vagal drive to the heart.
2. The LF component of HRV mainly indicates the cardiac sympathetic drive of the individual. Increased LF power (or more specifically, increased LFnu) represents increased sympathetic drive while decreased LF power (decreased LFnu) represents decreased sympathetic drive.
3. The sympathovagal balance is assessed by the LF–HF ratio. Increased LF–HF ratio reflects increased sympathetic activity, while decreased LF–HF ratio indicates increased parasympathetic and decreased sympathetic activity.

Physiological basis: The relationship between vagal stimulation frequency and the resulting change in heart rate is hyperbolic, with changes in frequency at low heart rates having a much greater effect that does not directly control the heart rate, but which regulates the interval between successive beats.

1. The effect of vagal stimulation is rapid. Vagal stimulation releases the neurotransmitter acetylcholine, which inhibits the pacemaker potentials.
2. Sympathetic responses differ from vagal effects in that they develop much more slowly. Hence, responses with longer latency are likely to be mainly sympathetic.
3. Peripheral vascular resistance exhibits intrinsic oscillations with a low frequency. These oscillations can be influenced by thermal skin stimulation and are thought to arise from thermoregulatory peripheral blood flow adjustments.
4. The fluctuations in peripheral vascular resistance are accompanied by fluctuations with the same frequency in blood pressure and heart rate and are mediated by the sympathetic nervous system.
5. Hence, analysis of HRV also indicates the tone of sympathetic outflow and, therefore, reflects the individual’s state of sympathetic function and susceptibility to sympathetic dysfunction.

Importance of LF-HF Ratio and Sympathovagal Balance

1. The HF component of HRV, which indicates the cardiac vagal drive to the heart, represents parasympathetic activity. The LF component of HRV, which mainly indicates the cardiac sympathetic drive, represents sympathetic activity, though parasympathetic drive also contributes to it.
2. In healthy individuals, HF constitutes about 60%, and LF constitutes about 40% of the total power (TP) of HRV.
3. Therefore, LF-HF ratio less than 1 indicates good cardiovascular health. However, LF-HF ratio in normal population varies from 0.5 to 1.5.
4. The sympathovagal balance is assessed by the LF-HF ratio.
5.  Increased LF-HF ratio reflects increased sympathetic activity (Figs. 35.3A and B) that is invariably associated with decreased TP.
6. Decreased LF-HF ratio indicates increased parasympathetic and decreased sympathetic activity that is invariably associated with increased TP (Fig. 35.4).

Clinical Application

Though there is considerable discussion regarding the physiology of HRV, it is well correlated and studied in many physiological and pathological conditions:

1. Total power (TP) of HRV indicates the magnitude of heart rate variability.
2. Decreased TP (decreased overall cardiac vagal modulation) has been implicated with future adverse cardiovascular (CV) morbidities and mortalities.
3. Decreased HRV (decreased total power of HRV) is observed in many cardiovascular disease conditions and generally indicated poor prognosis in these conditions.
4. Much before the onset of clinical symptoms of the cardiovascular disease, alterations are observed in HRV, indicating that HRV could be used as sensitive tool in the prediction of CV risks. However, more research is required to establish the predictive value of HRV in CV dysfunctions.
5. Presently, HRV is used as a prognostic tool in conditions like postmyocardial infarction and cardiac transplantation.
6. The most important application of HRV analysis is the surveillance of postinfarction and diabetic patients.
7. HRV gives information about the sympathetic-parasympathetic autonomic drives, and used as tool for assessment of autonomic imbalance.
8. As HRV analysis is used to assess the state of sympathovagal balance of the individual, it can be used to determine the individual’s susceptibility to developing autonomic dysfunctions in conditions like prehypertension and hypertension.
9. Decreased HRV is well correlated with the risk of sudden cardiac death in patients with heart disease.
10. Improvement in HRV and CV health is observed in interventions, like exercise, yoga and relaxation exercises. Hence, this can be used in future research works for improvement of holistic health.

The clinical applicability is still limited for lack of established normative data of HRV for different ages, genders and ethnic groups due its demanding technical and mathematical comprehensibility. However, with increasing use of automation and computers in medicine, the clinical applicability of HRV is bound to be appreciated by researchers and clinicians.