Heart Rate Variability to Assess Autonomic Function Phyllis K. Stein, Ph.D. Research Assistant Professor of Medicine and Director, HRV Lab Washington University.

Heart Rate Variability to Assess Autonomic Function

Phyllis K. Stein, Ph.D.Research Assistant Professor of Medicine and Director, HRV LabWashington University School of

Medicine,St. Louis, MO

PART I

Understanding ECGs and How the Heart Works

Overview of Blood

Circulation

The Heartbeat

Valves

Valves

Electrical Pathways

Action Potential Basics

1 2 3 4 5Resting voltage

Resting voltage

Cardiac Action Potential

Components of the ECG

ECG Measurements

Autonomic Nervous System Effects on the Heart

Parasympathetic Nervous System (PNS),

inhibits cardiac action potentials

Sympathetic Nervous System (SNS),

stimulates cardiac action potentials

Single Channel Normal ECG

p wave

QRS complex

t wave

A Normal 12 Lead ECG

Atrial Premature Contraction (APC)

Abnormal p wave

Early QRS

Atrial Bigeminy

Atrial Fibrillation (AF)

Normal ECG with Ventricular Premature Contractions (VPCs)

VPCs

Right Bundle Block (RBB)

Wide QRS peak

Dangerously Abnormal ECGS

Ventricular Tachycardia (VT)

Ventricular Fibrillation (VF)

Keywords

• Atrium• Ventricle• SA node• AV node• ECG Components• P wave• QRS complex• T wave • Sympathetic Nervous

System

• Parasympathetic Nervous System

• Vagal• APC or SVE• Bigeminy• VPCs• VT• VF

PART IIHolter and Other Continuous ECG

Data

Patient wearing a Holter device.

Heart Rate Variability (HRV) Lab Analyzes Data from Continuous

Electronically-Stored ECGs

Holter Monitor2 or 3 channels of SimultaneousECG signals

Cassette Tape

Flash Card

Continuous ECG Data Also Obtained from Overnight Sleep Studies

• Sleep studies have many channels of data including ECG

• Data stored on a hard disk and file exported to a CD

• One channel is ECG

Analysis of Stored ECG Signals

• Continuous ECG signal is digitized and loaded on the Holter scanner

• Holter scanner is a computer with special commercial software that can process ECGs

• Many other computer algorithms exist that can display and measure things from ECGs

The Job of the Holter Scanner

• Read and display the stored ECG

• Identify the peak of each beat

• Accurately label each beat as normal, APC or VPC

• Measure the time between the peaks of each beat

• Create a report describing the recording

• Export the results as a “beat file”

The QRS File

• MARS scanner exports “QRS” files.

• QRS file is a list of every detected event on the tape, with the time after the next event.

• Events can be normal beats, APCs, VPCs or just noise.

• QRS file is in binary format, so we need to convert it to something we can read.

Digitized ECG Format

• .MIT Format– Binary format– Consists of a .HDR file and .SIG file

• .RAW file– Binary format– Does not contain any header info– Can be reloaded onto MARS like tape

• .NAT file– Actual file on MARS– Can be reloaded into MARS “slot” and restore all original

data and analyses

The .MIB file• QRS file from the MARS scanners are

saved to “HRV.”• “HRV” is the name of the Sun computer

that does all HRV calculations.• QRS file is converted to MIB file and stored

on “HRV.”• .MIB= machine-independent beatfile• Heart rate variability is calculated from

the .MIB file

Example of the Beginning of a .MIB File

• # 13:46:03.726• Study code=8050MJP OK,1• Record number code=8050MJP1• Start time=13:41:00• First beat=13:46:03.726• Start date=02-May-03• Samples per second=128• Marquette conversion date=Thu Jun 10 13:19:17 2004• Marquette hardware revision=508 833 523 4.00 0.25• End header• Q0.000000000• Q687.500000000• Q617.187500000• Q656.250000000• Q656.250000000• Q656.250000000• Q648.437500000• Q656.250000000• Q656.250000000• Q687.500000000• Q625.000000000• Q656.250000000• Q656.250000000• Q656.250000000• Q656.250000000

header

Files Generated from the .MIB File

• All heart rate variability calculations are made and exported to an EXCEL spreadsheet with one row per subject

• Heart rate tachograms -beat-by-beat plots of heart rate vs. time

• HRV power spectral plots - graphical representation of HRV

• HRV Poincaré plots - graphical representations of HR patterns

Part of an HRV Spreadsheet

ID avnnT avnnD avnnN pnn50T pnn50D pnn50N

1A36181 1010.034 988.613 1043.868 5.559 6.188 4.36

1A49681 999.295 988.617 1016.784 1.295 2.018 0.586

1A75451 846.611 849.501 836.082 0.482 0.4 0.572

1B74381 810.154 813.078 780.171 9.725 10.264 4.494

1B74391 725.69 710.065 777.362 6.451 5.553 12.008

1B74401 866.626 821.987 930.132 15.402 8.237 35.138

1B76181 674.383 703.628 646.714 0.933 1.38 0.398

1B76191 817.108 826.079 789.545 2.274 3.173 1.034

• x-axis = time in minutes (0-10 minutes)

• y-axis for each 10-min plot is H (0-100 bpm in 5 cm)

• “x-axis” is mean HR for that 10-min segment

Heart Rate Tachogram

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10

Tim e (M in.)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10

00:09:00

00:19:00

00:29:00

00:39:00

00:49:00

00:59:00

0-100 bpm

“x-axis”

Hourly HRV Power Spectral Plots (much reduced in size)

Hourly Poincaré plots(much reduced in size)

Keywords

• Holter• Scanner• Beat file• QRS File• Binary• .MIB• Header

• Recognize:– Tachograms– Power spectral plots– Poincaré plots

Part III

HRV in Detail

Background (HRV)

• Decreased heart rate variability

• Abnormal heart rate variability

• Identify patients with autonomic abnormalities who are at increased risk of arrhythmic events.

Simplified Model of Cardiovascular Autonomic

Control

Renin angiotensinsystem

Heart Rate Cardiac outputBlood pressure

Parasympathetic Nervous system

SympatheticNervous system

How HRV Reflects the Effect of the Autonomic Nervous System

of the Heart

HR Fluctuations

• Fluctuations in HR (HRV) are mediated by sympathetic (SNS) and parasympathetic (PNS) inputs to the SA node.

• Rapid fluctuations in HR usually reflect PNS control only (respiratory sinus arrhythmia).

• Slower fluctuations in HR reflect combined SNS and PNS + other influences.

Rapid Fluctuations in HR Are Vagally Mediated

• “Rapid” fluctuations in HR are at >10 cycles/min (respiratory frequencies)

• Vagal effect on HR mediated by acetylcholine binding which has an immediate effect on SA node.

• If HR patterns are normal, rapid fluctuations in HR are vagally modulated

Acetylcholine Binding

The Acetylcholine Neurotransmitter binds to a receptor on a muscle once released from a

neuron.

Slower Fluctuations in HR Reflect Both SNS and Vagal Influences

• “Slower” fluctuations in HR are <10 cycles per min.

• SNS effect on HR is mediated by norepinephrine release which has a delayed effect on SA node

• Both SNS and vagal nerve traffic fluctuate at >10 cycles/min, but the time constant for changes in SNS tone to affect HR is too long to affect HR at normal breathing frequencies.

NE blinds to the beta-receptor (Alpha subunit of G-protein).

After binding, G protein links to second messenger (adenyl cyclase) which converts ATP to cAMP. cAMP activates protein kinase A which breaks ATP to ADP+phosphate which phosphorylates the pacemaker channels and increases HR

Sympathetic activation takes too long to affect RSA

Assessment of HRV

Approach 1

•Physiologist’s Paradigm

HR data collected over short period of time (~5-20 min), with or without interventions, under carefully controlled laboratory conditions.

Approach 2

Clinician’s/Epidemiologists’s Paradigm

Ambulatory Holter Recordings usually collected over 24-hours or less, usually on outpatients.

Assessment of HRV

Approaches 1 and 2 can be combined

Longer-term HRV-quantifies changes in HR over periods of >5min.

Intermediate-term HRV-quantifies changes in HR over periods of <5 min.

Short-term HRV-quantifies changes in HR from one beat to the next

Ratio HRV-quantifies relationship between two HRV indices.

HRV Perspectives

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

Ways to Quantify HRV

Approach 1: How much variability is there?Time Domain and Geometric Analyses

Approach 2: What are the underlying rhythms? What physiologic process do they represent? How much power does each underlying rhythm have?

Frequency Domain Analysis

Approach 3: How much complexity or self-similarity is there?

Non-Linear Analyses

Time Domain HRV

• SDNN-Standard deviation of N-N intervals in msec (Total HRV)

• SDANN-Standard deviation of mean values of N-Ns for each 5 minute interval in msec (Reflects circadian, neuroendocrine and other rhythms + sustained activity)

Longer-term HRV

• SDNNIDX-Average of standard deviations of N-Ns for each 5 min interval in ms (Combined SNS and PNS HRV)

• Coefficient of variance (CV)-

SDNNIDX/AVNN. Heart rate

normalized SDNNIDX.

Time Domain HRV

Intermediate-term HRV

Time Domain HRV

• rMSSD-Root mean square of successive differences of N-N intervals in ms

• pNN50-Percent of successive N-N differences >50 ms

Calculated from differences between successive N-N intervals

Reflect PNS influence on HR

Short-term HRV

Geometric HRV

HRV Index-Measure of longer-term HRV

From Farrell et al, J am Coll Cardiol 1991;18:687-97

Examples of Normal and AbnormalGeometric HRV

Frequency Domain HRV

• Based on autoregressive techniques or fast Fourier transform (FFT).

• Partitions the total variance in heart rate into underlying rhythms that occur at different frequencies.

• These frequencies can be associated with different intrinsic, autonomically-modulated periodic rhythms.

What are the Underlying Rhythms?

One rhythm5 seconds/cycle or12 times/min

5 seconds/cycle= 1/5 cycle/second

1/5 cycle/second= 0.2 Hz

What are the Underlying Rhythms?

Three Different Rhythms

High Frequency = 0.25 Hz (15 cycles/minLow Frequency = 0.1 Hz (6 cycles/min)Very Low Frequency = 0.016 Hz (1 cycle/min)

Ground Rules for Measuring Frequency Domain HRV

• Only normal-to-normal (NN) intervals included• At least one normal beat before and one normal beat

after each ectopic beat is excluded• Cannot reliably compute HRV with >20% ectopic

beats

• With the exception of ULF, HRV in a 24-hour recording is calculated on shorter segments (5 min) and averaged.

Longer-Term HRV

• Total Power (TP)

Sum of all frequency domain components.

• Ultra low frequency power (ULF)

At >every 5 min to once in 24 hours. Reflects circadian, neuroendocrine, sustained activity of subject, and other unknown rhythms.

Frequency Domain HRV

Intermediate-term HRV

• Very low frequency power (VLF)

At ~20 sec-5 min frequencyReflects activity of renin-angiotensin system, vagal activity, activity of subject.Exaggerated by sleep apnea. Abolishedby atropine

• Low frequency power (LF)

At 3-9 cycles/minBaroreceptor influenceson HR, mediated by SNS and vagal

influences. Abolished by atropine.

Frequency Domain HRV

Short-term HRV

• High frequency power (HF)

At respiratory frequencies

(9-24 cycles/minute, respiratory sinus arrhythmia but may also include non-respiratory sinus arrhythmia). Normally abolished by atropine.

Vagal influences on HR with normal patterns.

Frequency Domain HRV

Frequency Domain HRV

• LF/HF ratio-may reflect SNS:PNS balance under some conditions.

• Normalized LF power= LF/(TP-VLF)-correlates with SNS activity under some conditions.

• Normalized HF power=HF/(TP-VLF)-proposed as a measure of relative vagal control of HR. Increased for abnormal HRV.

Ratio HRV

0.20 Hz 0.40 Hz0

LF peak

HF peak

24-hour average of 2-min power spectral plots in a healthy adult

Relationship of Time and Frequency Domain HRV

SDNN Total Power

SDANN Ultra Low Frequency Power

SDNNIDX Very Low Frequency Power Low Frequency Power

pNN50 High Frequency PowerrMSSD

Non-Linear HRV• Non-linear HRV characterize the structure

of the HR time series, i.e., is it random or self-similar.

• Increased randomness of the HR time series is associated with worse outcomes in cardiac patients.

• Non-linear HRV measures are not available from commercial Holter systems.

• Most commonly used measure of randomness is the short-term fractal scaling exponent (DFA1 or α1). Decreased DFA1 increased randomness of the HR.

• Another index is power law slope, a measure of longer term self-similarity of HR. Decreased slope worse outcome.

• Normal DFA1 is about 1.1. DFA1<0.85 is associated with higher risk.

Non-Linear HRV

Detrended Fluctuation Analysis (DFA)

Power Law Slope

Comparison of Normal and Highly Random HRV Plots