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

2. Understanding ECGs and How the Heart Works
PART I
Understanding ECGs and How the Heart Works

3. Overview of Blood Circulation
Red –oxygenated blood
Blue – deoxygenated blood
Pulmonary system –where blood flows through the arteries and veins of the lungs to pick up oxygen
Arterial system
- distributes blood
- Muscular
- high pressure
- aorta acts is an auxiliary pump
Capillaries - exchange of nutrients and gases
Venous system - blood reservoir, low pressure
Heart - 4 chambers, two pumps; pulmonary and systemic

4. The Heartbeat Valves Valves
All four chambers relaxed-blood passively entering all chambers - diastole.
Contraction of the left atrium and right atrium at the same time (atrial contraction or “atrial kick”) causes blood from the atria to enter the ventricles (Adding to the blood from passive filling. Maximum amount of blood ventricles will have) – atrial systole.
Pressure in the ventricles greater than in the atria, which closes the valves so no blood can back flow into the atriums.
Ventricles contract which opens the outflow valves so the blood can go to either the pulmonary or systemic system – ventricular systole.
Pressure in ventricles decreases, valves close to protect against back flow.
Valves

5. Electrical Pathways SA node - pacemaker of the heart.
- “fires” an action potential 60 – 80 times a minute spontaneously.
- firing rate = heart rate (HR) and changes with needs of the body.
- regulated by the autonomic nervous system.
- initiates atrial contraction via the posterior internodal tract, middle internodal tract, and Bachmann’s Bundle.
- “activates” AV node
AV node
- delays SA signal before passing to the ventricles.
- initiates ventriclular contraction via conduction pathways.
- abnormal pathways result in abnormal-looking electrocardiogram

6. Action Potential Basics
Resting voltage
Resting voltage
Important ions - can be positive or negative (small circles)
- Sodium (Na+)
- Potassium (K+)
- Calcium (Ca++)
- Chloride (Cl-)
Cell membranes
- allows passage of ions into and out of cell (gray circle)
- regulates ion passage through different type of channels
Membrane potential - measured by looking at the inner cell membrane voltage compared with outside voltage
Resting (1)
- the cell has more negative ions inside than outside
- membrane potential is negative
Depolarization (2)
- channels open to allow positive ions flow into cell by electrical attraction and diffusion
- cell membrane potential rises
Peak (3)
- channels close
- the cell now has more positive ions
- cell membrane potential is positive
Repolarization (4)
- different set of channels open to allow positive ions to flow out of cell by electrical attraction and diffusion
- cell membrane potential falls
Resting (5)
- cell membrane potential returns to resting

7. Cardiac Action Potential
Action potentials (AP) in the heart
- an electrocardiogram (ECG) is the AVERAGE of all the action potentials
- when group of cells depolarizes: chain reaction, rest of heart depolarizes, unless cells have not yet recovered from last depolarization (refractory period: limit to peak heart rate)
- normal AP starts at AP node
0) Na+ channels open; K+ channels close
Na+ channels close; Ca++ channels open
Ca++ close; some K+ channels open
K+ channels open
And many, many other channels.

8. Components of the ECG ECG = electrocardiogram
- electrodes placed on the body record electrical signals from the heart.
Depolarization – positively increases in voltage from resting voltage.
Repolarization – decrease in voltage back to the resting voltage.

9. ECG Measurements

10. Autonomic Nervous System Effects on the Heart
Autonomic nervous system is the “automatic” part of the central nervous system.
-regulates all body functions including heart rate and blood pressure.
Two arms of the autonomic nervous system.
-Sympathetic=“fight or flight” responses
-Parasympathetic=relaxation and recovery
-Parasympathetic travels on vagus nerve, so parasympathetic also called vagal.
Parasympathetic Nervous System (PNS),
inhibits cardiac action potentials
Sympathetic Nervous System (SNS),
stimulates cardiac action potentials

11. Single Channel Normal ECG
QRS complex
ECG recordings can have one view (single channel) or many.
t wave
p wave

12. A Normal 12 Lead ECG 12 lead ECG - use 10 electrodes
- allows 12 different views, from different directions, of the heart’s electrical activity
- standard clinical ECG

13. Atrial Premature Contraction (APC)
Early QRS
APC = Supraventricular ectopy (SVE)
- is early
- p-wave usually abnormal
- QRS is usually abnormal
- cardiac output reduced because less time to fill
Abnormal p wave

14. Atrial Bigeminy
Atrial Bigeminy – each beat is followed by an atrial premature beat

15. Atrial Fibrillation (AF)
- Atria contracting at random; P wave all over the place
- SA node is not controlling HR
- Usually fast, totally irregular rhythm but normal QRS
- can lead to stroke; patients need blood thinners
- 25% cardiac output because no “atrial kick”

16. Normal ECG with Ventricular Premature Contractions (VPCs)
- VPCs are also called ventricular beats and are not under the control of the SA node
- abnormal shape because order of depolarization in heart is abnormal
- no p-wave; depolarization starts in the ventricle
- low cardiac output because heart muscle does not contract in the correct sequence
- can occur in healthy people

17. Right Bundle Block (RBB)
RBB - has a wide QRS complex
LBB (Left Bundle Block) - also has a wide QRS complex
Wide QRS peak

18. Dangerously Abnormal ECGS

19. Ventricular Tachycardia (VT)
- two VPCs in a row = “a couplet”
- three or more VPCs in a row is called non-sustained ventricular tachycardia (NSVT).
- A series of abnormal beats= a “run.”
- if the VPCs continue it is called ventricular tachycardia (VT)
- VT often very fast (>120 bpm)
- In VT heart contraction is totally abnormal and little blood is pumped.
- Can “degenerate” to Ventricular Fibrillation

20. Ventricular Fibrillation (VF)
- the heart beats in a nearly random way.
- usually fatal (sudden cardiac death).
VT and VF
- Defibrillators are designed to identify when happening and shock the heart back into a normal rhythm.

21. 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

22. Holter and Other Continuous ECG Data
PART II
Holter and Other Continuous ECG Data

23. Heart Rate Variability (HRV) Lab Analyzes Data from Continuous Electronically-Stored ECGs
Cassette Tape
Holter Monitor
2 or 3 channels of Simultaneous
ECG signals
Flash Card
Electronic ECGs are obtained using ambulatory monitors called “Holter Recorders”
ECGs can be stored on cassette tapes or flash cards
Patient wearing a Holter device.

24. 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

25. 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

26. 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”

27. 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.
Binary format – uses ‘0’ and ‘1’ to store the data

28. Digitized ECG Format .MIT Format .RAW file .NAT file Binary format
Consists of a .HDR file and .SIG file
.RAW file
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
Filenames are the same as the ID of the subject
.HDR file
- header file
- text file similar to .MIB header (example on next slides)
.SIG file – contains the ECG signal

29. 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

30. 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:
Marquette hardware revision=
End header Q Q Q Q Q Q
header
Header
- beginning of the file
- contains information about the recording
Q data
- Q means normal beat
- V means VPC (not shown)
- A means APC (not shown)
- Z means noise (not shown)
- number is time from the last beat in milliseconds

31. 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

32. Part of an HRV SpreadsheetID
avnnT
avnnD
avnnN
pnn50T
pnn50D
pnn50N
1A36181
5.559
6.188
4.36
1A49681
1.295
2.018
0.586
1A75451
0.482
0.4
0.572
1B74381
9.725
10.264
4.494
1B74391
725.69
6.451
5.553
12.008
1B74401
15.402
8.237
35.138
1B76181
0.933
1.38
0.398
1B76191
2.274
3.173
1.034

33. Heart Rate Tachogram x-axis = time in minutes (0-10 minutes)
0-100 bpm
“x-axis”
x-axis = time in minutes (0-10 minutes)
y-axis for each min plot is H (0-100 bpm in cm)
“x-axis” is mean HR for that min segment

34. Hourly HRV Power Spectral Plots (much reduced in size)

35. Hourly Poincaré plots
(much reduced in size)

36. Keywords Holter Scanner Beat file QRS File Binary .MIB Header
Recognize:
Tachograms
Power spectral plots
Poincaré plots

37. Part III
HRV in Detail

38. Background (HRV) Decreased heart rate variability
Abnormal heart rate variability
Identify patients with autonomic abnormalities who are at increased risk of arrhythmic events.
Arrhythmic Events = VT or VF
HRV = Heart Rate Variability

39. Simplified Model of Cardiovascular Autonomic Control
Renin angiotensin
system
Heart Rate
Cardiac output
Blood pressure
Parasympathetic
Nervous system
Sympathetic
The feedback loops are responsible for determining the actual heart rate.

40. How HRV Reflects the Effect of the Autonomic Nervous System of the Heart

41. 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.
Autonomic nervous system
- sympathetic nervous system (SNS) and parasympathetic nervous system (PNS)
- responsible for bringing about changes in the body in response to external changes through mostly involuntary actions.
SNS - “fight or flight” response by releasing epinephrine (also known as adrenaline).
PNS - returns the body to normal by releasing acetylcholine.
Respiratory Sinus Arrythmia (RSA) - a natural cycle of speeding up and slowing down of heart rate caused by breathing

42. 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
Vagus nerve
- one of the 12 pairs of nerves originating in the brain
- can directly stimulate the sinoatrial (SA) node.
The release of acetycholine onto the SA node results in a change in channel properties, which then decreases the inward current so the action potential cycle last longer (slower HR)
Cycles/min – think of a sinusoidal wave; how many complete “cycles” (such as peak to peak) are occurring per minute

43. Acetylcholine Binding
Acetylcholine – directly binds to the receptor in the SA node which causes an immediate change in heart rate.
The Acetylcholine Neurotransmitter binds to a receptor on a muscle once released from a neuron.

44. 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.

45. Sympathetic activation takes too long to affect RSA
NE – norepinephrine
RSA - respiratory sinus arrhythmia
There are many more steps here compared with acetylcholine receptor binding. NE takes longer to increase heart rate than acetylcholine does to decrease heart rate.
*Don’t need to know details, just know this takes longer!
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

46. 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.
Physiologist - biologist that specializes in the study of the processes that occur within living organisms.
HR = heart rate

47. Assessment of HRV Approach 2 Clinician’s/Epidemiologists’s Paradigm
Ambulatory Holter Recordings usually collected over 24-hours or less, usually on outpatients.
Clinician - directly involved with the care and treatment of patients.
Epidemiologist - studies the presence of disease in populations.
Approaches 1 and 2 can be combined

48. HRV Perspectives
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.

49. 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

50. Time Domain and Geometric Analyses
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

51. Time Domain HRV Longer-term 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)
N-N - normal-to-normal
To calculate a standard deviation of a series of numbers:
Find the average
Calculate the differences between each number and the average
Square each difference (so they don’t cancel out)
Sum
Divide by number of datapoints
Take square root

52. Time Domain HRV Intermediate-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.

53. Reflect PNS influence on HR
Time Domain HRV
Short-term 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
Root mean square – average change from one beat to the next

54. Geometric HRV HRV Index-Measure of longer-term HRV
Wider triangle – more HRV
Narrower triangle – less HRV
HRV Index-Measure of longer-term HRV
From Farrell et al, J am Coll Cardiol 1991;18:687-97

55. Examples of Normal and Abnormal
Geometric HRV

56. 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.
FFT – transforms data from the time domain into the frequency domain (next slide shows an example)

57. What are the Underlying Rhythms?
One rhythm
5 seconds/cycle or
12 times/min
In the time domain, the plot can be drawn using one sinusoidal wave with a frequency of 0.2 Hz. Therefore, in the frequency domain, there is a peak at 0.2 Hz.
5 seconds/cycle=
1/5 cycle/second
1/5 cycle/second=
0.2 Hz

58. What are the Underlying Rhythms?
Three Different Rhythms
High Frequency = 0.25 Hz (15 cycles/min
Low Frequency = 0.1 Hz (6 cycles/min)
Very Low Frequency = Hz
(1 cycle/min)

59. 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.
ULF – Ultra Low frequency

60. Frequency Domain HRV 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.

61. Frequency Domain HRV Intermediate-term HRV
Very low frequency power (VLF)
At ~20 sec-5 min frequency
Reflects activity of renin-angiotensin
system, vagal activity, activity of subject.
Exaggerated by sleep apnea. Abolished
by atropine
Low frequency power (LF)
At 3-9 cycles/min Baroreceptor influences on HR, mediated by SNS and vagal influences. Abolished by atropine.
Renin-angiotensin system – regulates blood pressure via the kidney
Atropine – inhibits acetylcholine actions.

62. 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.

63. Frequency Domain HRV Ratio 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.
Fundamental assumption:
- a reciprocal relationship between sympathetic and parasympathetic nervous systems.
- sometimes it “works” and sometimes it doesn’t.

64. 24-hour average of 2-min power spectral plots in a healthy adult
LF peak
HF peak
Spectral plots – graphical technique for examining cyclical structure in frequency domain.
0.20 Hz
0.40 Hz
24-hour average of 2-min power spectral plots in a healthy adult

65. 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 Power
rMSSD

66. 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.

67. Non-Linear HRV
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 DFA1<0.85 is associated with higher risk.

68. Detrended Fluctuation Analysis (DFA)
slope of the line
- is the scaling coefficient (alpha)
- used to determine correlated randomness
See Appendix article for more information

69. Power Law Slope

70. Comparison of Normal and Highly Random HRV Plots