Transcript Methods (Heart Rate Variability, Heart Rate Turbulence

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
PART I
Understanding ECGs and How
the Heart Works
Overview
of Blood
Circulation
The
Heartbeat
Valves
Valves
Electrical
Pathways
Action Potential Basics
Resting
voltage
1
2
3
4
5
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
QRS complex
p wave
t wave
A Normal 12 Lead ECG
Atrial Premature Contraction
(APC)
Early QRS
Abnormal p wave
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
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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 II
Holter and Other Continuous ECG
Data
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
Patient wearing a Holter device.
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
header
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# 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
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
Heart Rate Tachogram
0-100 bpm
• x-axis = time in
minutes (0-10
minutes)
• y-axis for each
10-min plot is H
(0-100 bpm in
5 cm)
0 0.5
1
1.5
2
2.5
3
3.5
4
0
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
4.5 5 5.5
Time (Min.)
6
6.5
7
7.5
8
8.5
9
9.5 10
00:59:00
00:49:00
00:39:00
00:29:00
00:19:00
• “x-axis” is mean
HR for that
10-min
segment
00:09:00
0.5
“x-axis”
Hourly HRV Power
Spectral Plots (much
reduced in size)
Hourly Poincaré plots
(much reduced in size)
Keywords
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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
Parasympathetic
Nervous system
Heart Rate
Cardiac output
Blood pressure
Renin angiotensin
system
Sympathetic
Nervous 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.
Sympathetic activation takes too long to affect RSA
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
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.
Assessment of HRV
Approach 2
Clinician’s/Epidemiologists’s
Paradigm
Ambulatory Holter Recordings usually
collected over 24-hours or less,
usually on outpatients.
Approaches 1 and 2 can be combined
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.
Sources of Heart Rate Variability
• Extrinsic
– Activity
– Mental Stress
– Physical Stress
- Sleep Apnea
- Smoking
• Intrinsic Periodic Rhythms
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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 selfsimilarity is there?
Non-Linear Analyses
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)
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.
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
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 Abnormal
Geometric 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, autonomicallymodulated periodic rhythms.
What are the Underlying Rhythms?
One rhythm
5 seconds/cycle or
12 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/min
Low 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.
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.
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.
Frequency Domain HRV
Short-term HRV
• High frequency power (HF)
At respiratory frequencies
(9-24 cycles/minute, respiratory sinus
arrhythmia but may also include nonrespiratory sinus arrhythmia). Normally
abolished by atropine.
Vagal influences on HR with normal
patterns.
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.
LF peak
HF peak
0
0.20 Hz
0.40 Hz
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
rMSSD
High Frequency Power
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.
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 1.1. DFA1<0.85 is
associated with higher risk.
Detrended Fluctuation Analysis
(DFA)
Power Law Slope
Comparison of Normal and Highly
Random HRV Plots