1. Heart rate variability Standards of measurement, physiological interpretation, and clinical use
Based on a Review article by Task Force of The European Society of Cardiology
E. Chiranjeevi Kumar
Tutor / Demonstrator
Department of Physiology
AIIMS - Bhopal

2. Inspiring Quote

3. HRV, ITS BACKGROUND INFORMATION
Definition
Generation Of HRV
History Of HRV
About The Task Force
Stress And Autonomic Nervous System
The Meaning Of Depressed HRV
METHODOLOGY AND TERMINOLOGY OF HRV ANALYSIS
Time Domain parameters
Frequency Domain Parameters
INTERPRETATION
CLINICAL VALUE OF HRV ANALYSIS
Usage Of HRV In Daily Clinical Practice
HRV and Diseases or Symptoms

4. Definition of HRV
Heart rate variability (HRV) refers to the beat-to-beat alterations in heart rate.
It is a measurement of central autonomic drive to the myocardium.
It depends on a balance between sympathetic and parasympathetic drives to myocardium.
In contrast to many conventional tests to evaluate autonomic nervous system, HRV analysis stands as a non invasive method of detecting an early autonomic impairment of heart.
Other terms used include: "cycle length variability", "RR variability" and "heart period variability".

5. HRV is mirroring the regularity of heart beats.
Regularity of heartbeats is derived from number of beats equal to the times elapsed between successive heartbeats.
They are named as R - R intervals and are measured in millisecond (ms).
R-R intervals are obtained from ECG or plethysmogram.

7. This variation in time between beats is caused by a “tug of war” between the sympathetic nervous system speeding the heart up and the parasympathetic slowing it down
A high HRV indicates a good cardiac adaptability while a lower HRV often indicates an abnormal and insufficient adaptability of the autonomic nervous system and is associated with a high risk of cardiovascular events.
HRV is also a good predictor for risk incidence and progression of focal coronary atherosclerosis

8. Generation of HRV
The intrinsic heart rate (HR) generated by the sinoatrial node (SA node) in the absence of any neural or hormonal influence is about 100 to 120 beats per minute (BPM).
However in healthy individual resting HR would never be that high.
In a health individual(adult), the HR is ranging between 60 and 90 beats per minute(BPM).
This represents the net effect of the parasympathetic (vagus) nerves, which slow HR, and the sympathetic nerves, which accelerate it.

9. Effect of ANS on intrinsic HR
Effect of ANS on intrinsic HR The heart response time to sympathetic stimulation is relatively slow. It takes about 5 seconds to increase HR after the actual onset of sympathetic stimulation and almost 30 seconds to reach its peak steady level. The heart response time to parasympathetic stimulation is almost instantaneous. Depending on the actual phase of heart cycle it takes just one or two heartbeats before the heart slows down to its minimum proportional to the level of stimulation.

10. This figure illustrates the variability in HR is due to the synergistic action of the two branches of the autonomic nervous system which acts in balance through neural, mechanical, humoral and other cardiovascular parameters in their optimal ranges and to react optimally to changing external or internal conditions.
The physiological origins of HRV are the fluctuations of the activity of vasomotor and cardio- vagal centers in brain.
Normally these fluctuations are a result of blood pressure oscillation; respiration; thermoregulation and circadian biorhythm.
All these factors can influence the length of beat-to-beat intervals, named R-R intervals.
The actual balance between their activities is constantly changing in attempt to achieve optimum considering all internal and external stimuli.
At rest, both sympathetic and parasympathetic nerves are active with the vagal effects dominant.

11. Sources of Heart Rate Variability
Extrinsic
Activity - Sleep Apnea
Mental Stress - Smoking
Physical Stress
Intrinsic Periodic Rhythms
Respiratory sinus arrhythmia
Baroreceptor reflex regulation
Thermoregulation
Neuroendocrine secretion
Circadian rhythms
Other, unknown rhythms
Sleep apnea – brief breathing interruptions during sleep.
Baroreceptor reflex regulation – short-term blood pressure regulation
Theremoregulation – body temperature regulation
Circadian rhythms – biological processes that take around 24 hours to complete

12. Basis of HRV

13. History of HRV
18th Century Albrecht von Haller noticed heart beat is not regular
The clinical relevance of heart rate variability was first appreciated in Hon & Lee noticed that the beat to beat interval changes are the first alteration before fetal distress occurs. R-R change precedes HR change.
1971 Sayers and others focused on rhythm imbedded in beat-to-beat HR
1972 Ewing et al devised a number of simple bedside tests of short-term RR differences to detect autonomic neuropathy in diabetic patients

14. History of HRV
1977 Wolf et al showed association of HR to sudden death post MI
1981 Akselrod introduced Power Spectral Analysis (PSD)
Late 1980’s HRV confirmed strong predictor of mortality after an acute MI
1996 Task Force published Standards of Measurement for HRV.
At present with the availability of new, digital, high frequency, 24-h multi-channel electrocardiographic recorders, HRV has the potential to provide additional valuable insight into physiological and pathological conditions to enhance risk stratification.

15. About The Task Force
The task force was established by the Board of the European Society of Cardiology and co-sponsored by the North American Society of Pacing and Electrophysiology.
The specific goals: 1. To standardize nomenclature and develop definitions of terms 2. To define physiological and pathophysiological correlates 3. To describe currently appropriate clinical applications 4. To identify areas for future research
The standards of measurement, physiological interpretation, and clinical use was the major goal of the task force.

16. Stress And Autonomic Nervous System

17. Stress And Autonomic Nervous System
Allostasis which means "maintaining stability, or homeostasis, through change."
The body actively copes with a challenge by expending energy and attempting to put things right.
Most of the time it succeeds but the real problems arise when…. (1) The systems involved in allostasis don't shut off when not needed or don't become active when they are needed. (2) The balance between SNS and PNS can be disturbed and either SNS or PNS can predominate over the other leading to stress related health problems. (3) The body doesn't return to a state of rest after an emergency state
When the body is challenged by something or anything that happens to us, from getting out of bed in the morning or running up a flight of stairs or having to stand up and give a talk, the brain activates the autonomic nervous system (ANS).
ANS, the involuntary system of nerves which controls and stimulates the output of two hormones, cortisol from the adrenal cortex and adrenalin from the adrenal medulla. These two hormones and the activity of the ANS help us cope: the ANS and the adrenalin keep us alert by increasing our heart rate and blood pressure and quickly mobilizing energy reserves.
In contrast, cortisol works more slowly, helps replenish energy supplies and, at the same time, helps us to remember important things. For example, cortisol readies our immune system to handle any threat -- bacterial/viral or injury.
All of these adaptive responses are described by the term "allostasis" which means "maintaining stability, or homeostasis, through change."

18. The Meaning Of Depressed HRV
Depressed HRV primarily means that heart rate is monotonously regular.
And it means -ability of the ANS’s regulatory function is low
-ability to keep the homeostasis, cope with the internal and external stressors is low
-lowered resistance to disease or recover in proper time.

19. CAUTIONS FOR ACCURATE MEASUREMENT & ANALYSIS
Environmental requirement
Measurement time
Appropriate environment
Before the measurement
Caffeine
Meal
Adjustment time
During the measurement
Position
Breathing
Body and eye movements
Environmental requirement  Please try to keep the measurement time since the HRV is known to have circadian rhythm due to changing of ANS balance (morning/evening)  The appropriate environment: Avoid too bright light or noise Maintain proper room temperature
Before the measurement  Avoid caffeine, smoking at least 2 hours before the measurement  Avoid the measurement right after the meal (2 hours after the meal)  Time for patient to adjust the new environment and resting state is needed.  Remove the manicure while being measured with finger sensor.
During the measurement  Maintain comfortable sitting position.  Don’t move or talk.  Don’t close the eyes or fall asleep.  Respiration: In normal and resting state. Don’t control the breathing intentionally

20. METHODOLOGY AND TERMINOLOGY OF HRV ANALYSIS
All the standard methods normally used in HRV analysis are derived from the RR tachogram
HRV Analysis
Linear
Non - Linear
Frequency Domain
Time Domain
POP Analysis

21. Time Domain parameters
Since the time domain methods are applied directly to the RR interval series they are the simplest ones to compute.
These methods are further divided into statistical and geometrical methods.
The geometrical measures are derived from the RR interval histogram and are relatively insensitive to the analytical quality of the RR series.
The current geometric methods are inappropriate to evaluate short term recordings.
However, while the available time domain methods can be applied to short and long term recordings, the current geometric methods are inappropriate to evaluate short term recordings.

22. Statistical methods Simple Time Domain Measures
Mean RR interval: e.g ms
Mean heart rate: e.g ms / 1000 ms = 60 bpm
Minimum RR interval: e.g. 700 ms
Maximum RR interval: e.g ms
Difference between RRmin and RRmax: 500ms

23. The mean value of RR intervals (ms) and Mean Heart Rate (bpm), defined by Equations
where N is total number of all RR intervals.

24. Statistical methods Complex Time Domain Measures
SDNN hour HRV Recording
SDANN
SDNNindex
SDSD
RMSSD
NN50
PNN50%

25. Statistical methods
SDNN (ms) - Standard deviation of the RR intervals, which reflects all the cyclic components responsible for variability in the period of the recording.
The actual values of SDNN depend on the length of recording - the longer recording is, the higher SDNN values are.

26. Statistical methods
SDANN (ms) - Standard deviation of the averages of NN intervals in all 5 min segments of the entire recording.
It estimates the changes in HR due to cycles longer than 5 min.
where N is total number of 5 minute sections RR intervals in selected segment,
RR5 is mean of RR intervals in 5 minute section,
RR is mean of all means of RR intervals in all 5 minute sections.

27. Statistical methods
SDNNindex (ms) - Mean of the standard deviations of all RR intervals for all 5 min segments of the entire recording (24h).
it measures the variability due to cycles shorter than 5 min.
where N is total number of all 5 minute sections of RR intervals in selected segment

28. Statistical methods
SDSD (ms) - Standard deviation of successive RR interval differences.

29. Statistical methods
RMSSD (ms) - Root mean square of successive differences in RR intervals
This measure estimate high-frequency variations in heart rate in short term RR recordings
NN50 - number of successive differences between R-R intervals greater than 50 ms
It’s used for classification of the segment longer or at least 5 minutes.
PNN50% - Percentage of NN50

30. Histogram of RR Intervals
Geometrical methods
Histogram of RR Intervals
To perform geometrical measures on the NN interval histogram, the sample density distribution D is constructed which assigns the number of equally long NN intervals to each value of their lengths. The most frequent NN interval length X is established, that is Y=D(X) is the maximum of the sample density distribution D.

31. Geometrical methods
HRV triangular index - Total number of all NN intervals divided by the height of the histogram of all NN intervals
HRVindex = (total number of all NN intervals)/Y
(Y = D(X)).
TINN - Triangular interpolation of NN interval
Baseline width of the minimum square difference triangular interpolation of the highest peak of the histogram of all NN intervals
T INN = M − N
Geometrical methods are less affected by the quality of the recorded data and may provide an alternative to less easily obtainable statistical parameters. However, the time duration of recording should be at least 20 minutes, which means that short-term recordings cannot be assessed by geometric methods.

32. Frequency Domain Methods
Frequency domain method of analysis is one of the key factors of HRV analysis
Various spectral methods for the analysis of the tachogram have been applied since the late 1960s.
The most common power spectral estimation technique used in HRV analysis is the Power Spectral Density (PSD) analysis.
PSD estimation provides the basic information of how the power of the signal (i.e., its variance) distributes as a function of frequency.
This technique separates the heart rate spectrum into various components and quantifies sympathetic and vagal influences on the heart.
This estimation can be made with two different type of methods: non-parametric and parametric.
The non-parametric method is computationally simpler, faster and the results obtained are very similar to the parametric results
Fast Fourier Transform (FFT) computation, based on Welch’s periodogram or Autoregressive Modeling (ARM), is the basis of the non-parametric PSD analysis.

33. PSD estimation provides the basic information of how the power of the signal (i.e., its variance) distributes as a function of frequency

34. Frequency Domain Methods
Absolute Measures
TP – Total power 0 to 0.4 Hz
ULF – ultra low frequency band <0.003 Hz
VLF – very low frequency band – 0.04 Hz
LF – low frequency band 0.04 – 0.15 Hz
HF – high frequency band 0.15 – 0.4 Hz
Relative Measures
The normalization minimizes the effect of changes of Total Power (TP) on LF and HF
Lfnorm = LF / (TP-VLF), unit n.u.
Hfnorm = HF/(TP-VLF), unit n.u.
LF/HF - Ratio of low to high frequency power
The distribution of LF and HF is not fixed and varies with autonomic modulation of the heart rate The energy in HF is vagal mediated The energy in LF and VLF are due to both sympathetic and parasympathetic systems

35. Nonlinear methods for HRV analysis
In this context, nonlinear methods, based on chaos theory and fractal analysis, have been applied in order to better understand the nonlinear phenomena underlying HRV
These methods includes
Poincar´e plot
Detrended Fluctuation Analysis (DFA)
Shannon entropy
Approximate entropy (ApEn)
Sample entropy (SampEn)
Fractal dimension
Considering that the heart has a complex control system and that HR fluctuations have an irregular behavior, there is a need to implement methods that could describe such complexity and irregularity.

36. INTERPRETATION TIME DOMAIN ANALYSIS (5 minutes) MEAN HRT (BPM)
Mean HRT is the average heart rate during 5 minutes.
Tachycardia: If the HRT is faster than 100 beats per minute.
Bradycardia: If the HRT is slower than 50 beats per minute.
 Tachycardia
Normal response to the exercise and acute emotional change (anxious and frightened)  Stress and anxiety  Hyperthyroid, Dehydration  Anemia  Weakened general health, Lack of sleep  Effects of caffeine, alcohol, nicotine and other drugs  ANS dysregulation  Functional/anatomical heart problem
Bradycardia
Physically healthy individual who exercise hard and regularly  Hypothyroid, Hypothermia  Functional/anatomical heart problem (Heart failure/Heart arrest)  Drug side effect

37. INTERPRETATION TIME DOMAIN ANALYSIS (5 minutes)
SDNN (MS) - it is the most representative parameter of HRV
It is the estimate of overall HRV and reflects the heart’s intrinsic ability to respond to hormonal influences
SDNN is a global index of HRV and reflects all long-term components and circadian rhythms responsible for variability in the recording period. 
Lower SDNN indicates lower HRV, which primarily indicates reduction in dynamic complexity and it is associated with sudden cardiac death
Thus, low age-adjusted values predict morbidity and mortality. For example, patients with moderate SDNN values ( milliseconds) have a 400% lower risk of mortality than those with low values (0-50 milliseconds) in 24-hour recordings
Causes of low HRV
 Weakened ANS’s ability to keep homeostasis against to internal/external environmental challenges  Lowered coping ability to various emotional/physical stressors  General weakness of health

38. INTERPRETATION TIME DOMAIN ANALYSIS (5 minutes)
SDANN (ms) - SDANN is an index of the variability of the average of 5-minute. Thus, it provides long-term information.
It is a sensitive index of low frequencies like physical activity, changes in position, circadian rhythm.
SDNNindex (ms) - The Index is believed to be a measure primarily of autonomic influence on heart rate variability.
SDSD (ms)-  is generally considered to reflect the day/night changes of HRV

39. INTERPRETATION TIME DOMAIN ANALYSIS (5 minutes)
RMSSD (ms) - This measure estimate high-frequency variations in heart rate in short term NN recordings .
It reflects an estimate of parasympathetic regulation of the heart.
This parameter is associated with the electrical stability of heart influenced by the PNS’s activity.
Decrease in RMSSD (RMSSD: below 10) accompanying lowered SDNN (below 20) is related to high risk of cardiac disease development.

40. Importance of rMSSD

41. INTERPRETATION TIME DOMAIN ANALYSIS (5 minutes)
NN50 & pNN50% - pNN50 along with RMSSD are the most common parameters based on interval differences.
These measurements correspond to short-term HRV changes and are not dependent on day/night variations.
They reflect alterations in autonomic tone that are predominantly vagally mediated.
Compared to pNN50, RMSSD seems to be more stable and should be preferred for clinical use.

42. Summary of Time domain methods

43. INTERPRETATION Geometric Methods
Includes HRV triangular index and TINN
Both these measures express overall HRV measured over 24 h and are more influenced by the lower than by the higher frequencies
The major advantage of geometric methods lies in their relative insensitivity to the analytical quality of the series of NN intervals
The major disadvantage is the need for a reasonable number of NN intervals to construct the geometric pattern. In practice, recordings of at least 20 min (but preferably 24 h) should be used to ensure the correct performance of the geometric methods.
The current geometric methods are inappropriate to assess short-term changes in HRV.

44. Frequency Domain Parameters
INTERPRETATION
Frequency Domain Parameters
Total Power (TP) (ms2) – Total Power is a short-term estimate of the total power of power spectral density in the range of frequencies between 0 and 0.4 Hz.
This measure reflects overall autonomic activity where sympathetic activity is a primary contributor.
5 minute total power mainly reflects the level of the autonomic nervous activities (bothPNS & SNS) while 24h TP includes the humoral (hormonal) effects and circadian rhythm as well as ANS’s activity.
Generally decrease in TP is observed in individual under chronic stress or with disease. The clinical meaning of TP in frequency domain is similar with that of SNDD in time domain.

45. INTERPRETATION Frequency Domain Parameters
Very Low Frequency (VLF) (ms2) – Very Low Frequency is a band of power spectrum range between and 0.04 Hz.
it indicates overall activity of various slow mechanisms of sympathetic function.
In short term analysis, VLF doesn’t provide much meaning since this band often reflect meaningless noise signals .
But many references shown that, the VLF band provides an additional indicator of sympathetic function.
Role in obstructive sleep apnea syndrome
Patients with obstructive sleep apnea syndrome have been shown to have an increase in the VLF band during the apnea, this increased VLF synchronized with the episodes of respiratory arrest of hypoxemia.
In other words, power in the VLF band increases when there is an absence of air exchange. It has been suggested that the VLF band originates from self-oscillations in the vasomotor part of the baroreflex loop and that the VLF augmentation during apnea is a direct result of sympathetic stimulation.
Also, some observations on patients with fatigue and arrhythmias suggest that these individuals commonly have low VLF power and a low SDNN Index while their SDNN and ULF power is often relatively normal.

46. INTERPRETATION Frequency Domain Parameters
Low Frequency (LF) (ms2) - Low Frequency is a band of power spectrum range between 0.04 and 0.15 Hz.
This measure reflects both sympathetic and para-sympathetic activity.
Generally it is a strong indicator of sympathetic activity.
Parasympathetic influence is represented by LF when respiration rate is lower than 7 breaths per minute or during taking a deep breath.
Thus, when subject is in the state of relaxation with a slow and even breathing, the LF values can be very high indicating increased parasympathetic activity rather than increase of sympathetic regulation.
So it’s important to educate patient before the measurement not to intentionally control his breathing.
For more accurate analysis, natural breathing without any conscious respiratory manipulation is highly required.
LF is highly associated with the SNS’s activity which enables the energy supply and loss of energy can be predicted through lowered LF with fatigue.
Also in patients who display exaggerated sympathetic activity, as occurs in patients with migraines, LF fluctuations are much stronger that in healthy subjects and can be reduced by propranolol, clearly demonstrating the beta-sympathetic mediation in this band.

47. INTERPRETATION Frequency Domain Parameters
High Frequency (HF) - High Frequency is a band of power spectrum range between 0.15 and 0.4 Hz.
This measure reflects parasympathetic (vagal) activity.
HF is also known as a ‘respiratory’ band because it corresponds to the NN variations caused by respiration (this phenomena is known as respiratory sinus arrhythmia (RSA)).
Heart rate is increased during inhalation and dropped during exhalation.
Generally increase in HF accompanies the increase in HRV.
This band reflects parasympathetic or vagal activity of ANS, which was confirmed after a large number of studies showed that total vagal blockade essentially eliminates the HF oscillations and reduces the power in the LF range.
Reduced PNS activity has been found in a number of cardiac pathologies in patients under stress or suffering from panic, anxiety or worry.
Lowered PNS activity is also believed to account for much of the reduced HRV in aging.
And it is known that some people suffering from pain have an increase in the HF band at night above and beyond their normal increase at night when they are not in pain.

48. HF and RSA

49. INTERPRETATION Frequency Domain Parameters
LF/HF Ratio - This is the ratio between the power of Low Frequency and High Frequency bands.
This measure indicates overall balance between sympathetic and parasympathetic systems.
Higher values reflect domination of the sympathetic system, while lower ones reflects domination of the parasympathetic system.
This ratio can be used to quantify the overall balance between the sympathetic and parasympathetic systems.

50. Importance of LF – HF ratio

51. INTERPRETATION Frequency Domain Parameters
Normalized Low Frequency (LF Norm) (%) - is the ratio between absolute value of the Low Frequency and difference between Total Power and Very Low Frequency.
This measure minimizes an effect of changes in Very Low Frequency power and emphasizes changes in sympathetic regulation.
LF norm= LF/(TP-VLF)*100= LF/(LF+HF)*100

52. Frequency Domain Parameters
Normalized High Frequency (HF Norm) (%) - is the ratio between absolute value of the High Frequency and difference between Total Power and Very Low Frequency.
This measure minimizes an effect of changes in Very Low Frequency power and emphasizes changes in parasympathetic regulation.
HF norm= HF/(TP-VLF)*100= HF/(LF+HF)*100
Both LF Norm and HF Norm emphasizes the balance between the sympathetic and parasympathetic arms of the ANS.
It is interesting to note that the ANS is able to maintain a close balance even during large changes in either sympathetic or parasympathetic activation.

55. This figure shows a typical HRV recording over a 15-minute period during resting conditions in a healthy individual. The top trace shows the original HRV waveform. Filtering techniques were used to separate the original waveform into VLF, LF, and HF bands as shown in the lower traces. The bottom of the figure shows the power spectra (left) and the percentage of power (right) in each band.

57. Summary of Frequency domain methods

59. Short term and Long term HRV Time and Frequency Domain Measures

60. Short term and Long term HRV Time and Frequency Domain Measures
Shows balanced behavior of the 2 branches of the autonomic nervous system
Normalized units should be quoted with absolute values to describe the distribution of spectral components
Short term recording (5 minutes)
VLF is not reliable
Long term recording (24 hours)
Results include ULF also
Problem is the heart period modulation responsible for LF and HF is not stationary during 24 h

61. Correlation between time and frequency domain measures

63. Interpretation Non linear Methods Point care plot analysis
One of the nonlinear techniques that is getting a significantly popularity in the scientific community is the Poincar´e plot, due to its simple visual interpretation and its proven clinical applicability.
It reflects the graphical correlation between consecutive RR intervals.
Synonyms  Scatter plot; scattergram  Return map; phase delay map  Lorenz plot

64. The Poincar´e plot, a nonlinear method is created by plotting by all RR intervals in two dimensional system. Method is described by following formula: Two adjacent RR intervals represent one point in the plot. The first RR interval (RRi) represents the x-coordinate, the second interval (RRi+1) represents y-coordinate.

65. The Poincar´e plot is characterized by a number of descriptors.
SD1 and SD2 are two standard Poincar´e plot descriptors
A common strategy to statistically characterize the shape of the plot is to use an ellipse fitting technique, where an ellipse is adjusted to the plot and oriented according to the line-of-identity.
The dispersion of points perpendicular to the line-of-identity mirrors the level of short-term HRV (SD1 - standard deviation of the points perpendicular to the line-of identity) which is mainly caused by Respiratory Sinus Arrhythmia (RSA).
On the other hand, the dispersion along the line-of-identity is thought to reflect the level of long-term HRV (SD2 - standard deviation along the line-of-identity).
It can be proved that short and long-term dispersion are related to the time-domain measures SDSD and SDNN.
PPA provides both summary and detailed beat-to-beat information on the behavior of HR and it is thought to express autonomic modulation of HR

67. One can observe that the lowest SD1/SD2 ratio value is for the healthy subject.

68. Normal values of standard measures of HRV
Even though that there are no widely accepted standard values for HRV that can be used for clinical purposes, The Task Force of the European Society of Cardiology and North American Society of Pacing Electrophysiology provided initial normative values of standard measures of HRV

69. Reference values

70. USAGE OF HRV IN DAILY CLINICAL PRACTICE
 To find out the early signs of development of pathological processes or the presence of a functional disorder  To assess the level of physical fitness and stress coping ability  To evaluate the treatment effectiveness and prognosis  To confirm the effect of stress relaxation program (massage, exercise, meditation, light therapy and others)  To get guideline in selecting drugs, doses, therapy
In the field of psychophysiogy, 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 state anxiety.

72. HRV AND DISEASES

73. Clinical and physiological importance of HRV analysis
Slide showing heart rate variability values in cardiac diseases.

74. Nenad Lakusic et al., 2015
Depressed HRV after MI may reflect a decrease in vagal activity directed to the heart. HRV in patients surviving an acute MI reveal a reduction in total and in the individual power of spectral components.
Patients surviving acute myocardial infarction (MI) are at risk of sudden cardiac death. Extensive research has focused on the association of sudden cardiac death after MI with various clinical tests of heart function. Sympathetic overactivity associated with low parasympathetic activity is an autonomic dysfunction (AD) which increases cardiac mortality.

75. CHF, MI and Time domains
In patients with chronic heart failure (CHF) and acute myocardial infarction (AMI) it is accepted that the best prognostic information is provided by two methods in the time domain: SDNN and pNN50 .
Although the Task Force recommends rMSSD better than pNN50 because its higher mathemathical robustness.
A SDNN value of less than 50 ms or pNN50 lower than 3% is considered indicative of high risk; a SDNN of between 50 and 100 indicates moderate risk, while a value of over 100 ms or a pNN50 greater than 3% is considered normal 

76. Sh a Saleem et al., 2012
In Diabetics AMI, Autonomic nervous system dysfunction with attenuated parasympathetic activity or aggravated sympathetic activity, exhibited by decreased HRV, triggers ventricular arrhythmias and sudden cardiac death and symbolize an autonomous risk factor for mortality in patients with AMI

77. Sh a Saleem et al., 2012

78. H Kudat et al.,2006
Diabetes mellitus can cause cardiovascular autonomic neuropathy and is associated with increased cardiovascular deaths. Patients with diabetes had lower values of heart rate variability parameters than healthy controls, and among diabetes patients those with microvascular complications had the lowest heart rate variability parameters.

79. R Virtanen et al,. 2003
This study shows that heart rate variability is uniformly reduced in mild to moderate untreated hypertension. Higher heart rate, advancing age, higher blood pressure, female gender and higher PRA were independent determinants of reduced heart rate variability. This reduced heart rate variability identifies hypertensive subjects with increased risk of cardiac mortality.

80. Krishnan Muralikrishnan et al., 2013
Decreased PSNS activity or increased SNS activity will result in reduced HRV.
Reduced HRV seen in obese individuals clearly indicates that cardiac vagal effects (i.e. vagal modulation of RR intervals) are diminished and sympathetic activity seems to be increased.

81. Raj Kumar Yadav et al., 2012
Cardiovascular complications may lead to mortality in patients with rheumatoid arthritis (RA)
SDNN, SDSD, RMSSD, NN50, LF and HF power and total power (ms x ms) were significantly lower in patients with RA versus healthy controls (P<0.001 HRV was significantly altered in these patients with RA and independently associated with disease activity.

82. HRV and Age Donald H. Singer et al,. 1998
The study age dependent 24-h time domain HRV of healthy subjects proves HRV (by all measures) decreases with aging.
These phenomena presumably reflect age-dependent differences in underlying autonomic or other regulatory mechanisms.
Normal aging can lower HRV, as determined by SDNN index, rMSSD and pNN50, to below levels associated with increased risk of mortality, particularly at 65 years.

83. Gender also influences HRV
Gender also influences HRV. The differences are most pronounced in subjects of 30 years, HRV of young male subjects being significantly greater than that of age-matched female subjects. Differences disappear by age 50.

84. HRV and Posture

85. General Conclusions HRV and heart rate Differential Index HRV and MI
HRV and Cardio – Vascular mortality
HRV and Diabetes Mellitus
HRV and Drug therapy
The effect of thrombolysis on HRV
Exercise training and HRV
Biofeedback
Hrv is inversely related to HR and there was a significant decrease in hrv with increasing age and time. Women displayed a greater hrv reflecting cardiac parasympathetic activity.
Differential Index is a simple geometric method used for the evaluation of HRV. It mainly reflects the parasympathetic control and correlates well with both time and frequency domain measures.
An acute MI was associated with signs of increased sympathetic and reduced parasympathetic activity which has been reflected by lower HRV values.
A low HRV in Both time and frequency domain predicts cardiovascular mortality, cardiac autonomic imbalance and arrhythmic crdiovascular events.
DM is associated with lower HRV as assessed by both time and frequency domain and confers a worsened prognosis in patients with stable angina pectoris.
Treatment with beta adrenergic receptor blocker (metoprolol) or calcium antagonist (verapamil) affected HRV differently, but did not appear to carry any prognostic information concerning cardiac events. Low-dose muscarinic receptor blockers, such as atropine and scopolamine, may produce a paradoxical increase in vagal effects on the heart, as suggested by a decrease in heart rate. In addition, scopolamine and low dose atropine can markedly increase HRV
It was reported in 95 patients with acute MI where HRV was higher 90 minutes after thrombolysis in the patients with patency of the infarct-related artery.
Exercise training may decrease cardiovascular mortality and sudden cardiac death. Regular exercise training is also thought to modify cardiac autonomic control. Individuals who exercise regularly have a 'training bradycardia' (i.e., low resting heart rate) and generally have higher HRV than sedentary individuals.
The technique called resonant breathing biofeedback teaches how to recognize and control involuntary heart rate variability. A randomized study by Sutarto et al. assessed the effect of resonant breathing biofeedback and found that depression, anxiety and stress significantly decreased.

86. The Future of HRV
It is believed that Heart Rate Variability (HRV) will soon become as common in patient charts as pulse, blood pressure or temperature.
The connection between health and the amount of variation in the heart rate was noted as early as the third Century AD.
In the last ten years, more than two thousand published articles have been written about HRV, which is being used as a screening tool for many diseases.
Lowered HRV is associated with aging, decreased autonomic activity, hormonal tonus and specific types of autonomic neuropathies.
HRV has been proven to be predictive of the likelihood of future diabetes and heart disease events; a reduced HRV is considered a primary indicator of cardiovascular autonomic diabetic neuropathy.
Measuring HRV provides useful information regarding inflammatory changes in the body. Additionally, a reduced HRV was found that among high-risk patients with cardiovascular diseases. During rehabilitation of patients with cardiovascular disease, a significantly higher mortality rate has been shown in post-infarction patients with reduced HRV compared to patients with normal HRV; it is well documented that low HRV is an independent risk factor for several negative cardiovascular outcomes.

87. Future possibilities Development of HRV measurement
The currently available time–domain methods predominantly used to assess the long-term profile of HRV are probably sufficient at present.
The contemporary non-parametric and parametric spectral methods are probably sufficient to analyse short-term ECGs without transient changes of heart period modulations.
Improvements are possible, especially in terms to develop numerically robust techniques suitable for fully automatic measurement.
The geometrical methods are only one possibility in this direction

88. Summary

89. Summary
Heart rate variability (HRV), the changes in the beat-to-beat heart rate calculated from the electrocardiogram (ECG), is a key indicator of an individual’s cardiovascular condition.
The HRV tachogram presents the fluctuation (up and down) of heart rate during 5 minutes influenced by constant ANS regulation on heart.
Poincaré plot analysis (PPA) is a nonlinear method that helps in recognizing the autonomic control of heart rate (HR).PPA is a quantitative visual technique compared to conventional fast Fourier transform indices. PPA provides both summary and detailed beat-to-beat information on the behavior of HR and it is thought to express autonomic modulation of HR.
PSD is power spectrum of a 5-minute heart rate variability waveform of tachogram. PSD analysis information of how power is distributed in each frequency- section, VLF, LF and HF. X scale indicates frequency (Hz) and Y scale indicates power (msec2/Hz).
In histogram, X-scale indicates RR-interval(ms) while Y scale indicates the number of heart rate (bpm) recorded. Lowered HRV is likely to draw a histogram with narrow width and high peak while higher HRV accompanies that with flattish shape without prominent peak.

91. “According to Darwin’s Origin of Species, it is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is best able to adapt and adjust to the changing environment in which it finds itself.”
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