Chiru seminar 2

Heart rate variabilityStandards of measurement, physiological

interpretation, and clinical use

E. Chiranjeevi Kumar

Tutor / Demonstrator

Department of Physiology

AIIMS - Bhopal

Based on a Review article by Task Force of The European Society of Cardiology

Inspiring Quote

• 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

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.

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.

Effect of ANS on intrinsic HR

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.

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

Basis of HRV

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 1965 - 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

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.

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.

Stress And Autonomic Nervous System

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

The Meaning Of Depressed HRV

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

METHODOLOGY AND TERMINOLOGY OF HRV ANALYSIS

• All the standard methods normally used in HRV analysis are derived from the RR tachogram

HRV Analysis

Non - Linear

Frequency Domain

Time Domain

Linear

POP Analysis

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.

Statistical methods

• Simple Time Domain Measures

– Mean RR interval: e.g. 1000 ms

– Mean heart rate: e.g. 60000 ms / 1000 ms = 60 bpm

– Minimum RR interval: e.g. 700 ms

– Maximum RR interval: e.g. 1200 ms

– Difference between RRmin and RRmax: 500ms

• The mean value of RR intervals (ms) and Mean Heart Rate (bpm), defined by Equations

• where N is total number of all RR intervals.

Statistical methods• Complex Time Domain Measures

• SDNN 24 hour HRV Recording

• SDANN

• SDNNindex

• SDSD

• RMSSD

• NN50

• PNN50%

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.

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.

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

Statistical methods

• SDSD (ms) - Standard deviation of successive RR interval differences.

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

Geometrical methods

Histogram of RR Intervals

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

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.

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 0.003 – 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

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

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.

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

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

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.

Importance of rMSSD

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.

Summary of Time domain methods

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.

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.

INTERPRETATION

• Frequency Domain Parameters

• Very Low Frequency (VLF) (ms2) – Very Low Frequency is a

band of power spectrum range between 0.0033 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

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.

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.

HF and RSA

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.

Importance of LF – HF ratio

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

• 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

Summary of Frequency domain methods

Short term and Long term HRV Time and Frequency Domain Measures

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

Correlation between time and frequency domain measures

Interpretation

• Non linear Methods

• Point care plot analysis

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 ofthe European Society of Cardiology and North American Societyof Pacing Electrophysiology provided initial normative values ofstandard measures of HRV

Reference values

USAGE OF HRV IN DAILY CLINICAL PRACTICE

HRV AND DISEASES

Clinical and physiological importance of HRVanalysis

Nenad Lakusic et al., 2015

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

Shemaila Saleem et al., 2012

Shemaila Saleem et al., 2012

H Kudat et al.,2006

R Virtanen et al,. 2003

Krishnan Muralikrishnan et al., 2013

Raj Kumar Yadav et al., 2012

HRV and Age

Donald H. Singer et al,. 1998

HRV and Posture

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

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.

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

Summary

Summary

“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 whichit finds itself.”