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HEART RATE VARIABILITY AND UNDERLYING CARDIOVASCULAR
PERFORMANCE DURING SPONTANEOUS AND CONTROLLED
BREATHING
by
CHRISTOPHER YUNG
A thesis submitted to the
Graduate School-New Brunswick
Rutgers, The State University of New Jersey
and
The Graduate School of Biomedical Sciences
UMDNJ – Robert Wood Johnson Medical School
in partial fulfillment of the requirements
for the degree of
Master of Science
Graduate Program in Biomedical Engineering
written under the direction of
Professor John K-J. Li
and approved by
________________________
________________________
________________________
New Brunswick, New Jersey
January, 2011
ABSTRACT OF THE THESIS
Heart Rate Variability and Underlying Cardiovascular Performance During
Spontaneous and Controlled Breathing
Christopher Yung
Thesis Director: Professor John K-J. Li
The autonomic nervous system plays an important role in modulating cardiovascular
function. Previous studies have shown that impairment of the autonomic regulation is a
marker of cardiovascular diseases, but none has simultaneously quantified underlying
cardiac and vascular changes. We hypothesized that by tracking multiple variables
underlying autonomic control, performance of the heart and the vascular system, a more
comprehensive view of cardiovascular regulation can be obtained.
The protocol involved five minutes of spontaneous breathing, followed by five minutes
of intermittent breathing on experimental subjects free from existing cardiac and
respiratory ailments (n=7, males 25-38 years-old). Time and frequency domain heart rate
variability (HRV) analyses were performed to probe overall cardiovascular regulation
during the two breathing patterns. Rate pressure product (RPP) to assess cardiac
performance and estimates of arterial compliance (C) to assess vascular function were
also obtained.
Time domain results showed dissimilar variations in heart rate between the two breathing
patterns (p < 0.01). A shift in power from high frequency components to low frequency
components was observed in the power spectrum which indicates a shift from
parasympathetic to sympathetic activity. Differences in RPP for the two breathing
patterns were not significantly significant, indicating overall myocardial oxygen
ii
consumption was unaffected by the controlled breathing protocol. However, components
contributing to RPP, i.e. mean HR and systolic blood pressure, both slightly increased
during intermittent breathing. Variations in beat to beat arterial compliance showed that
compliance increased during breath holding and decreased during release, but mean beat
to beat arterial compliance values of the two breathing patterns was not statistically
different.
Thus, we were able to track multiple variables underlying autonomic control, cardiac
performance and vascular function. We conclude that that short-term perturbation in
controlled breathing can provide an effective means to probe overall cardiovascular
regulation. In normal subjects, such perturbations affect primarily autonomic control and
less so in affecting energy consumption of the heart or vascular function.
iii
ACKNOWLEDGEMENT
I would like to thank my advisor Dr. John K-J Li for his help, guidance, and
encouragement all these years.
Thank you to my closest friends Dr. Hongjun Zhang and Hiro Ono for their friendship
and support, without them this would have been a difficult journey.
I would also like to thank Dr. Panos Georgopolous, Dr. Sastry Isukapalli, and everyone
else at CCL. I have learned a lot from them over the years.
iv
DEDICATION
To my parents and the rest of my family for their advice.
v
TABLE OF CONTENTS
ABSTRACT OF THE THESIS…………………………………………………………...ii
ACKNOWLEDGEMENTS………………………………………………………………iv
DEDICATION…………………………………………………………………………….v
LIST OF TABLES…………………………………………………..…….…...……….viii
LIST OF FIGURES……………………………...………………………..….…………..ix
LIST OF DIAGRAMS…………………………………………………..….………..…..xi
1.
INTRODUCTION……………………………………………………….………..1
1.1
Heart and Heartbeat Coordination……………………………….………..1
1.2
Heart Rate……………………………….……………….………………..4
1.3
Blood Pressure………………………………….…………..……………..7
1.4
Regulation of the Cardiovascular System…….…………..…….………..10
1.5
Variations in Heart Rate and Blood Pressure…..………………………..13
1.6
Components and Definitions of Heart Rate Variability and
Blood Pressure….……………………….…..…………………………...15
1.7
2.
3.
Clinical Significance of Heart Rate and Blood Pressure Measurements...17
1.7.1
Hypertension... …………………………………………………..17
1.7.2
Left Ventricular Hypertrophy…………………………………....20
AIMS AND SIGNIFICANCE……………………………………………...…....21
2.1
Aims………………………………………………………………...…....21
2.2
Proposed Approach………………………………………………....…....22
2.3
Significance………………………………………………………....…....23
METHODS………………………………………………………………....…....24
vi
3.1
Heart Rate Analysis Procedure…………………………..………....…....24
3.1.1 ECG Signal Detection……………………………………....…....24
3.2
4.
3.1.2
Digital Filtering…………………………..………………....…....24
3.1.3
QRS Peak Detection…………..………..……………..…....…....27
3.1.4
HRV Analysis…………..………..……………..……..…....…....28
Blood Pressure Analysis Procedure……..……………..……...…....…....28
3.2.1
Blood Pressure Measurements…..……………..……...…....…....28
3.2.2
Blood Pressure Signal Detection ..……………..……...…...........29
3.2.3
Digital Filtering ..……………..……..............................…...........29
3.2.4
Blood Pressure Waveform Delineation..........................…...........31
3.2.5
Blood Pressure Analysis..........................….... .....…. .....….........33
3.3
Experimental Procedure for Biological Signal Recordings….......33
RESULTS..........................….... .....…. .....…......... .....…. .... .....…. .....….........34
4.1
4.2
Heart Rate Variability Analysis .....…......... .....…. .... .....…. .....….........34
4.1.1
Time Domain Analysis .....…......... .....…. .... .....…. ......….........34
4.1.2
Frequency Domain Analysis: Power Spectrum.....…......….........35
Blood Pressure Analysis.....…......….........…......….......…......……….....37
4.2.1 Myocardial Oxygen Consumption…......….......…......….…….....37
4.2.2
5.
Arterial Compliance…......….......…......….………………….......38
DISCUSSION…......…......…......….…………...….………….…….……….......85
5.1
Comparison of Present Findings to Others……..………...………….......85
5.2
Directions for Future Research……..………...…..……………..….........88
REFERENCES…......…......…......….……………….………….…….……….…….......90
vii
LIST OF TABLES
Table 1.1 Frequency components of heart rate variability ….....….......…......…...…….16
Table 1.2 Systolic and Diastolic Blood Pressure Range Classifications.…......…..….…19
Table 4.1 Subject Demographics and Vital Statistics………………………………...…42
Table 4.2 Time-domain results comparing spontaneous and intermittent breathing…....43
Table 4.3 Frequency-domain results utilizing the FFT method to compare
spontaneous and intermittent breathing…………………...………………….44
Table 4.4 Frequency-domain results utilizing the AR method to compare
spontaneous and intermittent breathing. ………………...…………….…….45
Table 4.5 Rate pressure product comparing spontaneous to intermittent breathing…….46
Table 4.6 Arterial compliance obtained from measured values using:
……...…47
Table 4.7 Beat to beat mean arterial compliance obtained using:
………......….48
Table 4.8 Beat to beat mean arterial compliance obtained using:
………...……49
viii
LIST OF FIGURES
Figure 1.1 Propagation of action potentials throughout the heart………………………...3
Figure 1.2 Basic features of a typical electrocardiogram…………………...……………6
Figure 1.3 Blood pressure measurements made by the auscultation method……...……..9
Figure 1.4 Baroreceptor response to a stimulus from an increase in arterial pressure….12
Figure 3.1 Samples of filtered ECG signals...……………………………………...……26
Figure 3.2 Samples of filtered blood pressure waveform signals...…………………......30
Figure 4.1 Tachograms of subject 1....……………...……………...………….…….…..50
Figure 4.2 Tachograms of subject 2………………………………………………...…...51
Figure 4.3 Tachograms of subject 3…………………………………………..…......…..52
Figure 4.4 Tachograms of subject 4…………………………………………..…......…..53
Figure 4.5 Tachograms of subject 5…………………………………………..…......…..54
Figure 4.6 Tachograms of subject 6…………………………………………..…......…..55
Figure 4.7 Tachograms of subject 7…………………………………………..…...….....56
Figure 4.8 HRV power spectra calculated with Fourier Transform for subject 1.….......57
Figure 4.9 HRV power spectra calculated with Fourier Transform for subject 2.….......58
Figure 4.10 HRV power spectra calculated with Fourier Transform for subject 3.….....59
Figure 4.11 HRV power spectra calculated with Fourier Transform for subject 4.….....60
Figure 4.12 HRV power spectra calculated with Fourier Transform for subject 5.….....61
Figure 4.13 HRV power spectra calculated with Fourier Transform for subject 6..........62
Figure 4.14 HRV power spectra calculated with Fourier Transform for subject 7..........63
Figure 4.15 HRV power spectra calculated with AR model for subject 1....….........…..64
Figure 4.16 HRV power spectra calculated with AR model for subject 2...…..........…..65
ix
Figure 4.17 HRV power spectra calculated with AR model for subject 3...…................66
Figure 4.18 HRV power spectra calculated with AR model for subject 4...…................67
Figure 4.19 HRV power spectra calculated with AR model for subject 5...…................68
Figure 4.20 HRV power spectra calculated with AR model for subject 6...…................69
Figure 4.21 HRV power spectra calculated with AR model for subject 7...…................70
Figure 4.22 Plots displaying the inverse of pulse pressure (PP) for subject 1...…...........71
Figure 4.23 Plots displaying the inverse of pulse pressure (PP) for subject 2...…...........72
Figure 4.24 Plots displaying the inverse of pulse pressure (PP) for subject 3...…...........73
Figure 4.25 Plots displaying the inverse of pulse pressure (PP) for subject 4...…...........74
Figure 4.26 Plots displaying the inverse of pulse pressure (PP) for subject 5...…...........75
Figure 4.27 Plots displaying the inverse of pulse pressure (PP) for subject 6...…...........76
Figure 4.28 Plots displaying the inverse of pulse pressure (PP) for subject 7...…...........77
Figure 4.29 Histograms displaying the distribution of RR intervals for subject 1...........78
Figure 4.30 Histograms displaying the distribution of RR intervals for subject 2...........79
Figure 4.31 Histograms displaying the distribution of RR intervals for subject 3...........80
Figure 4.32 Histograms displaying the distribution of RR intervals for subject 4...........81
Figure 4.33 Histograms displaying the distribution of RR intervals for subject 5...........82
Figure 4.34 Histograms displaying the distribution of RR intervals for subject 6...........83
Figure 4.35 Histograms displaying the distribution of RR intervals for subject 7...........84
x
LIST OF DIAGRAMS
Diagram 3.1 Block diagram displaying the procedure taken for HRV analysis…….......24
Diagram 3.2 Flowchart displaying the processing steps for QRS detection.….…….......28
Diagram 3.3 Procedure for blood pressure analysis…………………………………….28
Diagram 3.4 Flowchart showing processes for blood pressure delineation …………….32
xi
1
CHAPTER 1. INTRODUCTION
1.1 Heart and Heartbeat Coordination
The human heart is a muscular organ that is composed of four chambers, the left atria,
right atria, left ventricle and the right ventricle. The left and right atria receive blood from
the pulmonary and systemic venous system respectively, while the left ventricle pumps
blood to the systemic arterial system and the right ventricle pumps blood to the
pulmonary arterial system. The pumping of blood is caused by the repeated rhythmic
contractions of the muscular fibers that make up the walls of the heart (myocardium) as a
result of autonomic physiologic electrical stimulus. Blood travels from the low pressure
venous system to the high pressure arterial system to deliver nutrients to the body and
coordinate gas exchange of oxygen and carbon dioxide.
The myocardium contracts in a coordinated fashion caused by the depolarization and
propagation of action potentials from cardiac cells through the electrical conduction
system of the heart. The electrical conduction system of the heart is composed of the
sinoatrial node (SA node), atrioventricular node (AV node), left posterior bundle, right
bundle, Bachmann’s bundle, bundle of His, and the Purkinje fibers. Electrical activity in
the heart caused by the depolarization and propagation of action potentials first initiated
at the SA node. The SA node typically serves as the cardiac pacemaker that propagates
action potentials to the Bachmann’s bundle and causes the atrium to contract,
concurrently, action potentials travel to the AV node through the internodal pathways.
Since the conduction velocity through the AV node is rather slow, the delay time allows
the atria complete activation and contraction before action potentials propagate toward
2
the ventricles. As the action potentials continues into the ventricles, the signal propagates
through the bundle of His into the bundle branches and then to the Purkinje fibers.
Finally, the signal travels from the Purkinje fibers to the endocardium through the
ventricular myocardium which causes the ventricles contract following depolarization.
Each heart beat causes a depolarization of action potentials that travels in this sequence.
3
Figure 1.1 This image displays the propagation of action potentials throughout the heart.
(Costanzo, 2007).
4
1.2 Heart Rate
The heart rate is described as the number of heartbeats per unit time and is typically
expressed as beats per minute (bpm). Depending on the physical activity that the body is
performing, heart rate will vary to coordinate gas exchange required by the tissues. For
example, more oxygen is absorbed and more carbon dioxide is excreted when a person is
sitting than when the same person is jogging in a marathon. Medical professionals
typically measure heart rate as a criteria factor in diagnosing and tracking a medical
condition. A modern method that displays the heart rate as the electrical activity during
each heart beat is the electrocardiogram (ECG).
An analogy for the ECG is that action potentials of myocardial cells can be thought of as
a battery that generates currents that move throughout the fluids in the body. These
charges can be detected on the surface of the skin with electrodes connected to a holter
monitor that displays the electrical activity of the heart as the ECG. Each of the wave
deflections in an ECG waveform corresponds to the spread of action potentials
throughout the heart.
Atrial depolarization is the P wave which appears as the first wave deflection in the ECG
waveform. The PR interval indicates the amount of time for an action potential to spread
through the atria and the AV node. Ventricular depolarization is called the QRS complex;
it is the second wave deflection and appears as the tallest spike. Following the QRS
complex is the ST segment, during this phase the membrane potential is zero because the
atrial cells are at resting phase and the ventricular cells are at the plateau phase of their
action potential. Ventricular repolarization is represented by the T wave. Medical
5
professionals typically analyze the interval between consecutive R peaks in an ECG time
series to provide a diagnosis of possible cardiovascular conditions. The interval between
consecutive R peaks is called the R-R interval. Fluctuation between R-R intervals is
called heart rate variability (HRV).
6
Figure 1.2 Image displaying the basic features of a typical electrocardiogram.
(Mohrman and Heller, 2006).
7
1.3 Blood Pressure
As the heart pumps, blood that circulates through the arterial circuit exerts pressure onto
the walls of the blood vessels. With each heartbeat, the pressure in the blood vessels
varies between maximum (systolic) pressure and a minimum (diastolic) pressure of the
heart. During the cardiac cycle, systolic pressure is the maximum arterial pressure during
peak ventricular ejection and the diastolic pressure is the minimum arterial pressure just
before ventricular ejection begins. The units of blood pressure are typically expressed as
systolic pressure/diastolic pressure in units of mmHg.
Arterial blood pressures can be measured with various invasive devices or non-invasive
alternatives. Invasive blood pressure measurements involve direct measurement of
arterial pressure by inserting the needle into an artery. A commonly used device for
invasive measurements is the needle-pressure transducer system which can accurately
record pulsatile blood pressure waveforms. Although complications associated with
invasive blood pressure measurement devices are infrequent, complications associated
with bleeding, infection, and thrombosis could occur.
Advantages of using noninvasive methods include less pain for the patient, virtually
nonexistent complications, and the expertise required to operate noninvasive
measurement devices are low. However, the main disadvantage of using a noninvasive
method is that the blood pressure measurements can yield results that are somewhat less
accurate. Some commonly used noninvasive blood pressure measurement methods is the
auscultatory method and the pressure pulse method with a tonometer, which are the
noninvasive measurement methods used in this analysis.
8
The auscultatory method is a simple measurement technique that will allow the systolic
and diastolic pressures to be determined. This method involves placing an inflatable cuff
around the upper arm which is inflated until the artery under the arm is occluded. As the
cuff is allowed to deflate, blood flows through the artery generating a vascular sound
called the Korotkoff sound. The first sound is at the systolic blood pressure and the fifth
sound where no sound can be heard is at the diastolic pressure. The auscultatory method
is the most commonly used method used in blood pressure measurement in a clinical
setting (Pickering et al, 2005).
In 1963, the noninvasive arterial tonometer was invented to measure intra-arterial blood
pressure (Pressman et al, 1963; Drzewiecld et al, 1983). The arterial tonometer is based
on the pressure pulse method that is dependent on the relationship between arterial
pressure, contact stress and deformation stress. This approach involves applying pressure
from an external object onto an area of the body where an artery against a bone. The
tonometer is a force transducer that will record the applied pressure required to keep the
artery flattened. The pulsatile blood pressure waveforms recorded from the tonometer can
be used to determine BPV.
9
Figure 1.3 Blood pressure measurements made by the auscultation method.
(Mohrman and Heller, 2006).
10
1.4 Regulation of the Cardiovascular System
Control over the vascular system is required to maintain homeostasis by allowing
adequate perfusion to reach the tissues and organs throughout the body. The autonomic
nervous system is part of the peripheral nervous system that plays an important role in the
regulation of cardiovascular system. The autonomic system is divided into two
subsystems, the sympathetic nervous system and the parasympathetic nervous system.
The behavior of these two autonomic subdivisions are complementary to each other and
each function opposite to the other and receive output from the medullary cardiovascular
system to perform functions to meet the requirements of the body.
The general action of the sympathetic nervous system is to mobilize the body’s resources
under stress which has been termed the flight-or-fight response. While the
parasympathetic nervous system is responsible for the stimulation of the body’s activities
while the body is at rest. Adjustments to heart rate by the sympathetic and
parasympathetic subdivisions involve inputs to the SA node. The output from the
sympathetic and parasympathetic nerves to the SA node is important because the SA
node is responsible for sinus rhythm and serves as the pacemaker of the heart.
The balancing action between the sympathetic and parasympathetic systems controls the
variation in heart rate activity. An increase in sympathetic activity will increase heart rate
and enhance blood flow to the skeletal muscles. The increase in heart rate caused by the
sympathetic nerves on the SA node is achieved by the rate and force of contraction of the
heart. The opposite effect on heart rate and blood pressure is observed when there is an
11
increase in parasympathetic activity. In addition to the autonomic system, there are
various receptors in the body that also regulate the cardiovascular system.
The arterial baroreceptors are located in the aortic arch and the carotid sinuses which
sense and monitor blood pressure. The stimulation of baroreceptors in the arteries is
caused by pressure changes which in effect will cause a response that will increase or
decrease heart rate based on the needs of the body. For example, when pressure is low in
the arteries, the perturbation will stimulate the carotid sinus. Once the carotid sinus is
activated, the baroreceptors will direct signals to the sympathetic system to increase heart
rate and vasoconstriction which will cause blood pressure to increase to a normal level.
Arterial chemoreceptors are located in the aortic arch and the carotid sinus and detect the
level of carbon dioxide, oxygen, and pH in the blood. An increase in carbon dioxide,
decrease in oxygen, and reduction in pH cause an increase in discharge frequency from
chemoreceptors. The effect of an increase in discharge frequency from chemoreceptors
will cause an increase in vasoconstriction and a slowing of the heart rate. The increase in
vasoconstriction will cause an increase in blood pressure, which will stimulate the
baroreceptor reflex.
12
Figure 1.4 Baroreceptor response to a stimulus from an increase in arterial pressure.
(Costanzo, 2007).
13
1.5 Variations in Heart Rate and Blood Pressure
The term “heart rate variability” (HRV) has become a conventionally accepted term that
describes the oscillations between consecutive R-R intervals (RRI). The time delay
between heartbeats has also been called RR variability, heart period variability, cycle
length variability, and heart period variability. During the early 1970s, studies conducted
by Luczak (Luczak, 1973) and Sayers (Sayers, 1973) analyzed HRV the existence of
physiological rhythms within the signal. In 1977, Wolf et al (Wolf et al, 1978) first
showed the correlation between the reduced HRV and a higher risk of mortality
following myocardial infarction.
In 1981, the evaluation of cardiovascular control using the power spectral analysis of
HRV was introduced by Akselrod et al (Akselrod et al, 1981). With the introduction of
power spectral analysis, frequency analysis helped researchers understand autonomic
control over HRV (Pomeranz et al, 1985; Pagani et al, 1986) as well as BPV (Pagani et
al, 1986). Studies conducted in the late 1980s confirmed that a reduction in HRV is an
independent predictor of mortality after acute myocardial infarction (Kleiger et al, 1987;
Malik et al, 1989; Bigger et al, 1992) and associated with an increased risk of sudden
cardiac death (Kleiger et al, 1987b; Hartikainen et al, 1996). Today, there are many
devices that are commercially available that make automated measurements of HRV in
research and clinical studies (Dreifus et al, 1993).
ECG devices used in HRV analysis is an established tool in determining a patient’s
cardiovascular health, however, there is still the potential for a medical professional to
give an incorrect diagnosis based on incorrect conclusions or extrapolations. Therefore, it
14
would be appropriate to measure additional physiological measurement in conjunction
with HRV in diagnosing a patient’s cardiovascular health. Measurements of blood
pressure variability (BPV) used in conjunction with HRV could be worthwhile.
Studies conducted by Musini et al (Musini et al, 2009), have shown that the increase in
BPV increases with blood pressure, associated with target-organ damage. In a more
recent study, Rothwell et al (Rothwell et al, 2010) has shown that visit to visit BPV used
as an independent variable can be used as a strong predictor of stroke. One of the major
reasons why visit to visit BPV measurements aren’t used more often for hypertension is
because it requires many visits (Lagro et al, 2010). To overcome this drawback, one
option would be to perform noninvasive continuous HRV and BPV measurement over
some period of time (Dawson et al, 2000).
15
1.6 Components and Definitions of Heart Rate Variability and Blood Pressure
Ever since the introduction of power spectral analysis by Akselrod et al (Akselrod et al,
1981), there have been various studies that have utilized spectral analysis of HRV and
BPV to understand autonomic control (Pagani et al, 1986; Hayano et al, 1990a; Malliani
et al, 1991; Akselrod et al, 1985; Hayano et al, 1990b). Analysis of BPV and HRV in the
frequency domain will allow the researcher to determine the variance or “power” at each
specific frequency and provide insight into the mechanism of cardiovascular control
(Parati et al, 1996; Jenkins et al, 1968; Kay et al, 1981). There have been some early
studies of HRV and BPV that suggest that divided them into their frequency components
using spectral analysis (Kawase et al, 2002; Pagani et al, 1986; Malliani et al, 1991;
Pagani et al, 1985; DeBoer et al, 1987).
The low-frequency (0.04-0.15 Hz) component of HRV is modulated by both the
sympathetic and parasympathetic nervous system while the high-frequency (0.15-0.4 Hz)
component of HR is only modulated by the parasympathetic nervous system (Rimoldi et
al, 1990; Akselrod et al 1985; Malliani et al 1991; Japundzic et al 1990). The ratio of
LF/HF ratio in HRV is taken to assess a shift in the sympatho-parasympathetic balance.
Whereas the low-frequency component of BPV (Mayer waves) is associated with the
sympathetic nervous system (Akselrod et al 1985; Akselrod et al 1981; Hughson et al,
1995; Japundzic et al, 1990) and the high-frequency component of BPV (Traube-Hering
waves) is associated with the mechanical effect of respiration (Rimoldi et al 1990;
Akselrod et al 1985; Japundzic et al 1990; Pagani et al 1986).
16
Table 1.1 Frequency components of heart rate variability
Frequency Component
Frequency (Hz)
Origins
Ultra low frequency (ULF)
0.0001 ULF < 0.003
Multiple determinants
Very low frequency (VLF)
0.003 VLF < 0.04
Thermoregulatory or
plasma renin activity
Low frequency (LF)
0.04 LF < 0.15
Sympathetic and
Parasympathetic pathway
High frequency (HF)
0.15 HF < 0.4
Respiration and
Parasympathetic pathway
17
1.7 Clinical Significance of Heart Rate and Blood Pressure Measurements
1.7.1 Hypertension
Hypertension is generally diagnosed on the persistence of high blood pressure based on
diastolic and systolic blood pressure. The ranges for blood pressure in various levels in
relation to hypertension classifications are shown in Table 1. Hypertension is a has been
shown to be a risk factor associated with arterial aneurysm, chronic kidney failure, heart
failure, myocardial infarction, and stroke (Pierdomenico et al 2009) and is the number
one attributable risk for death in the world (World Health Report 2002). It has been
indicated from the National Health and Nutrition Examination (NHANES) that 50
million Americans require some sort of treatment for high blood pressure (Burt et al,
1995; Hajjar et al, 2003). It has been estimated that there are approximately 7.1 million
deaths worldwide are attributed to hypertension (World Health Report 2002).
The dysregulation of the autonomic nervous system has been implicated in the
development of hypertension, and has been implicated by studies that associated
hypertensive individuals with decreased HRV (Singh et al, 1998; Parati et al, 1996;
Shroeder et al, 2003; Fagard et al, 2001; Liao et al, 1996; Singh et al, 1998). In addition,
the relation between HRV and blood pressure exists for a wide range of blood pressure
levels (Fagard et al, 2001; Lucini et al, 2002).
In a study conducted by Schroeder et al (Shroeder et al, 2003), temporal analysis of
between hypertension, blood pressure, and heart rate variability was examined in 11,061
individuals aged 45 to 54. Their results showed that R-R intervals, standard deviation of
normal-to-normal R-R intervals (SDNN), and root mean square of successive differences
18
of normal-to-normal R-R intervals (RMSSD) were lower among hypertensives than
normotensives. The differences among calculated R-R intervals, SDNN, and RMSSD
persisted even after adjustments for demographic and socioeconomic backgrounds.
Spectral analysis of short term BP and HR recordings by Guzzetti et al (Guzzetti et al,
1988) in hypertensive to normotensive individuals have been characterized by a greater
LF and a smaller HF power of R-R intervals during supine rest.
Blood pressure measurements at systolic and diastolic levels in classifying individuals as
hypertensive or normotensive have been invaluable. The benefit of lowering mean blood
pressure to prevent cardiovascular events has been important. Besides lifestyle changes,
at times it may be necessary for individuals to different classes of drugs to reduce blood
pressure. Until recently, mean blood pressure had been the focus in hypertensives and
stroke, however, several promising new studies have been indicated a reduction in BPV
for hypertensive individuals decreases the likelihood of stroke (Rothwell et al, 2010a;
Rothwell et al, 2010b; Webb et al, 2010; Gorelick, 2010).
19
Table 1.2 Systolic and Diastolic Blood Pressure Range Classifications. Adapted from
Chobanian et al (2003)
Classification Systolic pressure (mmHg) Diastolic pressure (mmHg)
Normal
90–119
60–79
Prehypertension
120–139
80–89
Stage 1
140–159
90–99
Stage 2
160
100
20
1.7.2 Left Ventricular Hypertrophy
Left ventricular hypertrophy (LVH) is a condition where the myocardium in the left
ventricle increases in thickness. The typical thickness of the left ventricular myocardium
is 0.6 to 1.1 cm and a thickness greater than 1.1 cm could be diagnosed as LVH. LVH
itself is not a disease, however, it can be used as a marker for possible cardiovascular
diseases in patients. For instance, LVH has been linked as a secondary condition to
hypertension and aortic valve disease (Acharya et al, 2006). Diagnosis is important, and
studies of patients with LVH have shown a significant reduction in HRV (Alter et al,
2006; Galinier et al, 2001). LVH and strain pose a significant health risk because it has
been associated with morbidity and mortality.
HRV studies have shown that the peaks of respiratory sinus arrhythmia correlate to
baroreflex sensitivity, which is caused by the influence of the arterial baroreflex on the
cardiac vagal nerve. Typically hypertensive patients with LVH, display a reduction in
amplitude of respiratory sinus arrhythmia (Acharya et al, 2006). Temporal analysis of RR intervals in control patients and patients with LVH, has shown a reduction in SDNN in
patients with LVH (Alter P, 2006). In addition, power spectrum analysis of HRV in 195
hypertensive patients conducted by Galinier M et al (Galinier et al, 2001) has shown a
lower low frequency power than patients with normal left ventricular geometry. Methods
describing the relationship between BPV and HRV have been analyzed in various studies
in the frequency domain (Parati et al, 1995;Akselrod et al 1995; de Boer et al 1995).
21
CHAPTER 2. AIMS AND SIGNIFICANCE
2.1 Aims
Heart rate variability remains one of the most fundamental noninvasive tools in studying
the relationship between the autonomic nervous system and the cardiovascular system for
the last three decades. Time domain methods provide information describing the beat-tobeat changes in heart rate, however, these methods lack the ability to discriminate
sympathetic and parasympathetic contributions of heart rate variability. On the other
hand, frequency domain analysis provides information on how various components of a
signal is distributed as a function of frequency. As mentioned previously, the LF and HF
components correspond to autonomic modulation on heart rate. In addition, comparing
the LF/HF ratio can be used to understand the shift between the sympathetic and
parasympathetic nervous systems.
To complement the information obtained through HRV analysis, blood pressure, and
arterial compliance should be analyzed. The analysis of myocardial oxygen consumption
and arterial compliance will provide information regarding performance of the heart and
vascular function. Autonomic cardiovascular regulation is a truly complicated process
since many inputs affect both heart rate and blood pressure.
The aim of this study is to explore the sympathetic and parasympathetic influences on
cardiovascular regulation induced by respiratory control. In this experiment, subjects will
perform spontaneous breathing and intermittent breath holding. The specific aims of this
thesis are:
22
I. Develop a procedure that utilizes and device setup consisting of an ECG
circuit to record heart rate waveforms and a tonometer to record pulse
pressure waveforms.
II. Once heart rate and blood pressure have been recorded, employ analysis
techniques on heart rate and blood pressure waveforms to obtain a
quantitative understanding of the cardiovascular regulation.
III. Analyze the results obtained from heart rate and blood pressure analyses
to investigate the control mechanisms involved in cardiovascular
regulation.
2.2 Proposed Approach
Meeting the objectives of this thesis in order to reach reasonable conclusions depend on
the procedure used to obtain measurements, how biological signals are processed, and
employing informative analysis techniques on heart rate and blood pressure signals.
Obtaining reasonable heart rate and blood pressure signals will largely depend on
literature review as well as trial and error. Signal processing will depend on which filters
to apply without losing biological information. Finally, selection of which cardiac and
vascular analysis techniques to employ is important because any one technique by itself
should not be used to give a prognosis.
23
2.3 Significance
Most clinicians focus on variation in heart rate and mean blood pressure as part of the
prognosis of their patients. Traditionally, cardiovascular control is described as the
regulation of heart rate and blood pressure to maintain homeostasis. Blood pressure tends
to fluctuate back to a set reference point even when it is perturbed by an outside stimulus.
Therefore, variations in blood pressure can be a rich source of information describing
cardiovascular control mechanisms (Mancia et al, 1983; Appel et al, 1989; Parati et al,
1992).
Hales and von Haller were the first to describe the variations in blood pressure and heart
rate to respiratory activity. It wasn’t until 80 years later when Ludwig confirmed their
observations (Koepchen, 1984). It was the discoveries of Mayer that suggested that BPV
is related to vasomotor activity (Koepchen, 1984; Mayer, 1876; Penaz, 1976). Since then,
there have been various techniques employed to ascertain information obtained from
changes in heart rate, changes in blood pressure, and respiratory activity. Which methods
to employ as well as appropriate interpretation of results are still unclear (Di Rienzo et al,
1992). Most studies focus on the effects of autonomic regulation by analyzing just the
heart or the vascular system. The purpose of this thesis is to utilize proper techniques to
provide a clear picture of cardiovascular regulation, cardiac performance, and vascular
function as it relates to heart rate, blood pressure, and respiratory activity.
24
CHAPTER 3. METHODS
3.1 Heart Rate Analysis Procedure
The procedure for heart rate waveform measurements, processing, detection, and analysis
are shown in Diagram 3.1.
Diagram 3.1 Block diagram displaying the procedure taken for HRV analysis.
3.1.1. ECG Signal Detection
Electrodes were placed on each of the subjects to record ECG signal from the body
surface, however, signals obtained from the skin are weak approximately 1mV. Therefore
the hardware used in heart rate detection implements an instrumentation amplifier to
improve the common-mode-rejection ratio. ECG signals were recorded and stored using
AcqKnowledge version 3.7.3 at a sample rate of 200 samples/second.
3.1.2. Digital Filtering
Digital filters were applied to ECG signals using AcqKnowledge version 3.7.3 and
exported into a format readable in MatLab. A digital FIR bandpass filter (blackman -61
dB) was applied to the heart rate waveforms to remove baseline drift and high frequency
noise. As described by Ruha et al, a bandpass filter set at 0.5-35Hz can be used to detect
heart rate with 1 msec accuracy (Ruha et al, 1997). Therefore the filter was applied
25
between 0.5-35Hz. The digital filter used 1600 coefficients based on the following
equation:
—„‡”‘ˆ‘‡ˆϐ‹…‹‡–•ൌͶൈ
ƒ’Ž‡ƒ–‡
‘™‡•–”‡“—‡…›
(3.1)
Therefore:
ͳ͸ͲͲൌͶൈ
ʹͲͲ
ͲǤͷ
(3.2)
The plots displayed in Figure 3.1 show the ECG signals plotted in MatLab after filtering
in BioPac.
26
(a)
(b)
Figure 3.1 Samples of filtered ECG signals obtained from subject 1 during (a)
spontaneous breathing and (b) intermittent breathing.
27
3.1.3. QRS Peak Detection
The Pan-Tompkins algorithm written in MatLab by G. Clifford ([email protected]) was
utilized in the detection of QRS complexes. The algorithm was first developed by Pan
and Tompkins in 1985 (Pan and Tompkins, 1985) on 24 hour MIT/BIH arrythmia
database with a 99.3 percent success rate. The algorithm uses the digital analysis of
amplitude, slope, and width of QRS complexes for successful detection. The algorithm
implements a bandpass filter, a derivative, signal amplitude squaring, and a moving
window integrator.
The bandpass filter consists of a low-pass filter and high-pass filter that functions
together to reject noise. The derivative is used to calculate slope of the R wave, it is a
popular signal feature used for QRS detection (Ahlstrom and Tompkins, 1983). The
purpose of the squaring process intensifies the slope and restricts false positives caused
by T waves. Finally, the moving window integrator produces a signal that contains the
slope and width of the QRS complex. Adaptive thresholds are implemented to discern the
location of the QRS complexes.
When the Pan-Tompkins algorithm was applied to the heart rate data, the detection
threshold coefficient was set to 0.189. In a study conducted by Hamilton and Tompkins
for QRS detector performance, the best detector had a detection threshold coefficient of
0.189 (Hamilton and Tompkins, 1986).
28
Diagram 3.2 Flowchart displaying the processing steps for QRS detection.
3.1.4. HRV Analysis
Heart rate variability analysis was performed with MatLab 2007a, Kubios HRV version
2.0 (Tarvainen et al, 2009), and Microsoft Excel 2007.
3.2 Blood Pressure Analysis Procedure
The procedure for blood pressure measurements, processing, detection, and analysis are
shown in Diagram 3.3.
Diagram 3.3 Procedure for blood pressure analysis.
3.2.1 Blood Pressure Measurements
A blood pressure monitor from Becton Dickinson Consumer Products (Model: BD-A30)
was used to measure each subject's systolic and diastolic pressure. An inflatable cuff was
placed around the left arm where the bicep and tricep is located. Each subject was in a
seated position with their arm extended while blood pressure measurements were
recorded. Mean systolic and diastolic pressures were measured.
29
3.2.2 Blood Pressure Signal Detection
A tonometer was used for measuring pulse pressure from each volunteer’s radial artery.
The device utilized an instrumentation amplifier to improve the common-mode-rejection
ratio. Similar to ECG signal detection, the blood pressure waveform signals were
recorded and stored using AcqKnowledge version 3.7.3 at a sample rate of 200
samples/second.
3.2.3 Digital Filtering
Digital filters applied to the blood pressure waveform were the same as the process
applied to ECG signals using AcqKnowledge version 3.7.3 and exported into a format
readable in MatLab. Baseline drift and high frequency noise was diminished using a
digital FIR bandpass filter (blackman -61 dB) with a range set at 0.5-35Hz and 1600
coefficients. Similar to heart rate waveforms, the number of coefficients was determined
using equations 3.1 and 3.2. The plots displayed in Figure 3.2 show the blood pressure
signals plotted in MatLab after filtering in BioPac.
30
(a)
(b)
Figure 3.2 Samples of filtered blood pressure waveform signals obtained from subject 1
during (a) spontaneous breathing and (b) intermittent breathing.
31
3.2.4 Blood Pressure Waveform Delineation
The algorithm used for onset, systolic peak, and dicrotic notch detection was based on the
delineator proposed by Li et al (Li, 2010). A MatLab script implementing this algorithm
is posted by the authors on the MatLab Central website, and was applied on the blood
pressure waveform data. Their algorithm has been applied to three databases with a 99.45
percent success rate in detecting beats and a 96.64 percent success rate in detecting
dicrotic notches (Li, 2010). Multiple processing steps are used for waveform delineation.
The algorithms consist of a low pass filter, derivative, zero crossing detection, beat
evaluation, inflection detection, and dicrotic notch evaluation.
The low pass filter is used to suppress noise and artifacts. Threshold estimation is
determined adaptively for the amplitude and the interval. The inflection and zero crossing
points are determined using the derivative. The zero crossing point before maximal
inflection is determined to be the onset of an arterial blood pressure waveform and the
zero crossing point after maximal inflection is the systolic peak. Finally, inflection
detection is used to determine dicrotic notches.
32
Diagram 3.4 Flowchart showing processes for blood pressure delineation (Li, 2010).
33
3.2.5 Blood Pressure Analysis
Analysis of blood pressure and arterial compliance was performed in MatLab 2007a and
Microsoft Excel 2007.
3.3 Experimental Procedure for Biological Signal Recordings
Heart rate and blood pressure measurements were obtained from seven normal male
volunteers age ranged from 25 to 38. Each volunteer was in the sitting position with five
minute measurements for intermittent and spontaneous breathing. For the first five
minutes, each patient sat in a relaxed position while spontaneous breathing was recorded.
Once spontaneous breathing measurements were taken, each subject would take a short
break of approximately three to five minutes before intermittent measurements were
taken. After the break, each subject would perform five minutes of 20 second breathing
followed by 40 second inspiration and breathe holding.
34
CHAPTER 4. RESULTS
4.1 Heart Rate Variability Analysis
Once experimental recordings were obtained time domain and frequency domain analysis
was applied to the datasets. Common techniques used in heart rate variability analysis
consisted of determining the mean RR interval and spectral analysis techniques such as
the power spectrum. A more detailed explanation of the techniques that were performed
is described in the following section for
4.1.1 Time Domain Analysis
തതതത) was determined from the number of R peaks detected in each
The mean heart rate (‫ܴܪ‬
one minute recording and averaged for the five minute interval. The mean RR interval
തതതത) was determined from the differences from successive R peaks and averaged for
(ܴܴ
each RR interval. The standard deviation (STDNN) of the RR intervals was calculated
with the following equation:
ܵܶ‫ ܰܰܦ‬ൌ ට
ଵ
ேିଵ
തതതത ଶ
σே
௝ୀଵሺܴܴ௝ െ ܴܴ ሻ
(4.1)
The number of RR intervals was standardized to 300 beat RR intervals because the total
variance of HRV recordings increases with the length of recordings (Saul et al, 1988).
That’s why it would be inappropriate to compare STDNN measurements from different
length recordings. STDNN is an important variable that reflects the variability among all
the cyclic components both short term and long term for the RR interval series (Task
Force, 1996). The root mean squared of successive differences of RR intervals (RMSSD)
is calculated to provide information regarding rapid changes in heart rate:
35
ܴ‫ ܦܵܵܯ‬ൌ ට
ଵ
ேିଵ
ଶ
σே
௝ୀଵሺܴܴ௝ െ ܴܴ௝ିଵ ሻ
(4.2)
Other statistical measurements performed include the number of interval differences of
successive RR intervals greater than 50 ms (NN50) and its relative value in relation to the
total amount of RR intervals (pNN50). The equation for pNN50 is calculated as the
following:
‫ܰܰ݌‬ͷͲ ൌ ேேହ଴
ேିଵ
ൈ ͳͲͲΨ
(4.3)
4.1.2 Frequency Domain Analysis: Power Spectrum
There have been various spectral methods applied to tachogram data since the late 1960s
(Task Force, 1996). The power spectrum provides information describing how the power
of the signal varies as a function of frequency. Methods that are used in calculating the
Power spectrum are classified as either parametric or nonparametric. The advantages of
using nonparametric methods such as the fast Fourier transform (FFT) include simplicity
of the algorithm used and computational speed. The advantages of using parametric
methods such as the AR method include smoother spectral components and an accurate
estimation of the Power spectrum even if the number of samples is low.
I.
FFT – Nonparametric Method
For a finite sampled sequence ‫ݔ‬௡ , where ݊ ൌ Ͳǡ ǥ ǡ ܰ െ ͳ, from a waveform ‫ݔ‬௧ the
formula for the FFT is defined as the following:
36
೙
ି௜ଶగ௞ಿ
ܺሺ‫ݓ‬௞ ሻ ൌ σேିଵ
௡ୀ଴ ‫ݔ‬௡ ݁
‫ݓ‬௞ ൌ ݇
௪ೞ
(4.4)
݇ ൌ Ͳǡ ǥ ǡ ܰ െ ͳ
ே
where ws is the sampling frequency. The output of the FFT, ܺሺ‫ݓ‬௞ ሻ, is a sequence of
complex numbers that represent the frequency components of a sampled signal. To
estimate the Power spectral density (PSD) of ‫ݔ‬௡ from the FFT is defined as the following
equation:
ܵሺ‫ݓ‬௞ ሻ ൌ ο்
ே
ȁܺሺ‫ݓ‬௞ ሻȁଶ
(4.5)
In this experiment, to reduce the amount of noise in the data, spectral averaging is
performed after Fourier transformation. The Power spectrum with averaging is
commonly referred to as an average periodogram. For this particular analysis, the Welch
method is applied to the heart rate waveforms. The data is split up into 256 points with 50
percent overlap, and windowing is performed to reduce spectral leakage.
II.
AR Method – Parametric Method
The AR method is performed in addition to the FFT method because it has improved
resolution for short lengths of data. In addition, it is advantageous to have two sets of
spectrums to compare from. The AR method is a parametric model,an infinite impulse
response filter with an input of white noise. The equation governing the AR method with
order p given as the following:
௣
ܺሺ݊ሻ ൌ െ σ௜ୀଵ ܽሺ݅ሻ‫ݔ‬ሺ݊ െ ݅ሻ ൅ ‫ݓ‬ሺ݊ሻ
(4.6)
37
where ܽሺ݅ሻ are autoregression coefficients and ‫ݓ‬ሺ݊ሻ is Gaussian white noise. A major
aspect of using this method is the selection of the order p. There has been much research
in determining optimal order (Akaikie, 1969, 1974) and Broadman et al (Broadman et al,
2002) has shown that an order 16 can be taken, which was the value used in this study.
The Burg method is used to get the AR model parameter over a length of data by
minimizing the sum of squares forward and backward errors. The Power spectrum of a p
th order AR model is given by the following:
஻௎ ሺ݂ሻ
ܲ௫௫
ൌ
ா෠
మ
೛
หଵାσ೔సభ ௔ෞ೛ ௘ షೕమഏ೑ೖ ห
(4.7)
where ‫ܧ‬෠ is the total least square error.
4.2 Blood Pressure Analysis
Measurements and analysis of blood pressure was performed to gain an understanding of
autonomic regulation and its effects on the vascular system. Areas of analysis included
myocardial oxygen consumption (MVO2) measurement and arterial compliance (C).
4.2.1 Myocardial Oxygen Consumption
Myocardial oxygen consumption (MVO2) has been used by cardiologists to understand
the total energy utilization of the heart. The heart requires energy in the form of ATP and
consumes oxygen during aerobic metabolism to contract. The original equation used to
calculate MVO2 is based on Fick’s principle:
ଶ ൌ ሺ‫ܱܽܥ‬ଶ െ ‫ܱݒܥ‬ଶ ሻ ൈ ‫ܨܤܥ‬
(4.8)
38
where ‫ܱܽܥ‬ଶ is the oxygen content in the arteries, ‫ܱݒܥ‬ଶ is the oxygen content in the
coronary sinus, and ‫ ܨܤܥ‬is coronary blood flow. However, methods used to calculate
MVO2 using Fick’s principle tend to be invasive and other calculations using noninvasive
methods have been suggested:
ܴܲܲ ൌ ‫ܲݔܴܪ‬௦ (4.9)
In equation 4.9, heart rate is ‫ܴܪ‬, systolic pressure is ܲ௦ , and rate pressure product (RPP)
is the product of ‫ ܴܪ‬and ܲ௦ . A higher value of RPP indicates a higher amount of MVO2 is
required of the heart, however, the main limitation is if ‫ ܴܪ‬and ܲ௦ deviate away from each
other.
4.2.2 Arterial Compliance
Typical estimates of C from beat to beat measurements are measured as the ratio of
pressure and volume variations (Talts et al, 2006) and accurate measurement is still the
object of discussion. Presented are two methods used to estimate C from beat to beat
pulse pressure measurements.
I.
Estimating arterial compliance using: ‫ ܥ‬ൌ ୗ୚
௉௉
Arteries provide an extremely important function in the vascular system, by acting as
pressure reservoirs that maintain blood flow through the tissues during diastole. The
amount of pressure exerted inside the arteries depend on the volume of blood being
pumped from the ventricle of the heart and how easily the arterial walls stretch. Arterial
compliance is clinically significant because it has been shown to be an independent risk
39
factor for cardiovascular disease (Rowe, 1987; Hodes et al, 1995). Arterial compliance
can be calculated from the following equation:
‫ ܥ‬ൌ
୼୚
୼୔
(4.10)
where ȟ is the change in pressure in the arteries and ȟ is the change in pressure. This
relation means that the higher the compliance in the arteries, the easier it is for the artery
to stretch. The variable ȟ can be thought of as pulse pressure ሺܲܲሻ, the difference in
blood pressure in the arteries at systole and diastole:
ܲܲ ൌ ܲ௦ െ ܲௗ (4.11)
where systolic pressure (ܲ௦ ) and diastolic pressure (ܲௗ ). The variable ȟ can be thought
of as stroke volume (ܸܵ), the volume of blood pumped out of the left ventricle after one
beat.
ܸܵ ൌ ‫ ܸܦܧ‬െ ‫ܸܵܧ‬
(4.12)
where ‫ ܸܦܧ‬is the end-diastolic volume and ‫ ܸܵܧ‬is the end-systolic volume. However,
‫ ܸܦܧ‬and ‫ ܸܵܧ‬are not values that can be measured based on the equipment available in
the lab, therefore ܸܵ was calculated from the relationship with cardiac output ሺ‫ܱܥ‬ሻ and
heart rate ሺ‫ܴܪ‬ሻ:
ܸܵ ൌ
஼ை
ுோ
(4.13)
Substituting equation 4.9 and 4.11 into 4.8, we obtain the following relationship:
‫ ܥ‬ൌ
ୗ୚
௉௉
ൌ
஼ை
ுோൈሺ௉ೞ ି௉೏ ሻ
(4.14)
40
Equation 4.12 was used to calculate compliance, ‫ܱܥ‬was set at a constant value of
5L/minute and the other parameters were obtained through measurements from the ECG
and blood pressure waveform.
II.
Estimating arterial compliance using: ‫ ܥ‬ൌ ఛ
ோ௦
The total arterial compliance can be calculated with the Windkessel model of the arterial
system. The compliance from a linear model is computed from:
‫ ܥ‬ൌ
ఛ
(4.14)
ோೞ
where ߬ is the aortic pressure decay time constant and ܴ௦ is the total peripheral resistance.
The term ߬ is commonly calculated using the equation for diastolic aortic pressure decay:
ܲௗ ൌ ܲ௘௦ ݁ ି௧ௗȀఛ
(4.15)
where ܲ௘௦ is end systolic pressure, ܲௗ is end diastolic pressure and td is the delay between
ܲ௘௦ and ܲௗ . Setting equation 4.14 equal to ߬ and in terms of measured pressure:
߬ ൌ
௧ௗ
ು೐ೞ
ሻ
ು೏
୪୬ሺ
(4.16)
Substituting equation 4.16 into 4.14 yields the following equation:
‫ ܥ‬ൌ
௧ௗ
ୖ౩ ୪୬ሺ
ು೐ೞ
ሻ
ು೏
(4.17)
All of the parameters in equation 4.17, except ୱ were determined using measured
values. Parameter ୱ was calculated with the following relationship:
ܴ௦ ൌ ெ஺௉
஼ை
(4.18)
41
CO was given a constant value of 5 mL/min and MAP was calculated from measured
values.
42
Table 4.1 Subject Demographics and Vital Statistics
Subjects
Subject 1
Subject 2
Subject 3
Subject 4
Subject 5
Subject 6
Subject 7
Age
27
26
26
38
32
26
25
Sex
Male
Male
Male
Male
Male
Male
Male
Systolic
Pressure
(mmHg)
117
113
122
105
108
124
115
Diastolic
Pressure
(mmHg)
79
71
63
71
75
81
62
Pulse
Pressure
(mmHg)
38
42
59
34
33
43
53
End
Systolic
Pressure
(mmHg)
97
87
106
88
90
104
101
43
Table 4.2 Time-domain results comparing spontaneous and intermittent breathing.
(a)
Subjects
Subject 1
Subject 2
Subject 3
Subject 4
Subject 5
Subject 6
Subject 7
Average
p
Significant
Difference
Mean HR
(1/min):
Spon.
Inter.
62.39
62.53
84.12
81.92
67.76
74.43
78.61
75.63
67.82
72.67
63.96
60.91
69.10
77.68
70.54
72.25
0.390
Mean RR
(ms):
Spon.
Inter.
964.11 966.07
719.67 746.33
887.90 812.63
765.87 799.12
886.54 833.11
941.50 990.83
870.90 777.54
862.36 846.52
0.492
Spon.
Inter.
48.17
76.87
67.16
99.56
44.96
72.37
43.92
67.16
41.01
77.54
56.20
74.21
46.20
63.34
49.66
75.86
7.18041E-05
Not Significant
Not Significant
p < 0.01
STDNN (ms):
(b)
Subjects
Subject 1
Subject 2
Subject 3
Subject 4
Subject 5
Subject 6
Subject 7
Average
p
Significant
Difference
RMSSD (ms):
Spon.
Inter.
50.81
36.44
69.19
66.88
36.43
58.74
61.77
63.32
38.47
34.79
30.91
52.90
36.01
43.49
46.23
50.94
0.395
NN50 (count):
Spon.
Inter.
117.00 57.00
204.00 169.00
60.00 111.00
201.00 172.00
61.00
51.00
31.00
70.00
61.00
95.00
105.00 103.57
0.933
pNN50 (%):
Spon.
Inter.
37.86
18.51
49.28
42.36
17.91
30.33
51.67
46.11
18.15
14.29
9.81
23.33
17.84
24.80
28.93
28.53
0.932
Not Significant
Not Significant
Not Significant
44
Table 4.3 Frequency-domain results utilizing the FFT method to compare spontaneous
and intermittent breathing.
(a)
Absolute
powers
Subjects
VLF (ms2):
LF (ms2):
HF (ms2):
Total (ms2):
Spon.
Inter.
Spon.
Inter.
Spon.
Inter.
Spon.
Inter.
5356.75
490.43
1255.87
1211.89
307.43
2159.90
6920.05
6796.72
903.66
3560.96
1081.79
1442.20
3439.90
11799.89
Subject 3
457.58
1454.4
5
467.43
1645.51
820.69
1949.86
479.29
1617.46
1767.41
5212.83
Subject 4
144.16
1946.34
218.54
1097.26
588.81
554.89
951.52
3598.50
Subject 5
4513.15
361.48
1634.10
372.92
293.70
954.19
6440.96
1413.06
605.01
2838.61
329.32
1003.93
3918.47
5255.60
Subject 7
219.79
2984.1
4
418.43
1570.47
917.34
1462.19
451.59
698.41
1787.35
3731.07
Average
878.00
3320.29
616.74
1971.27
645.09
845.43
2139.82
6136.99
Subject 1
Subject 2
Subject 6
p
0.042
0.004
0.442
0.005
Significant
Difference
p < 0.05
p < 0.01
Not Significant
p < 0.01
(b)
Normalized
powers
Subjects
Spon.
Inter.
Spon.
Inter.
Spon.
Inter.
Subject 1
28.81
80.33
71.19
19.67
0.40
4.09
Subject 2
45.51
71.17
54.49
28.83
0.84
2.47
Subject 3
63.13
54.66
36.87
45.34
1.71
1.21
Subject 4
27.07
66.41
72.93
33.59
0.37
1.98
Subject 5
49.22
84.77
50.78
15.24
0.97
5.56
Subject 6
64.75
73.87
35.25
26.13
1.84
2.83
Subject 7
67.01
67.68
32.99
32.32
2.03
2.09
Average
49.36
71.27
50.64
28.73
1.17
2.89
LF (ms2):
HF (ms2):
LF/HF ratio:
p
0.039
0.039
0.048
Significant
Difference
p < 0.05
p < 0.05
p < 0.05
45
Table 4.4 Frequency-domain results utilizing the AR method to compare spontaneous and
intermittent breathing.
(a)
Absolute
powers
VLF (ms2):
LF (ms2):
HF (ms2):
Total (ms2):
Subjects
Spon.
Inter.
Spon.
Inter.
Spon.
Inter.
Spon.
Inter.
Subject 1
522.64
3789.98
484.51
1271.34
1074.82
390.84
2081.97
5452.16
Subject 2
1344.83
4614.86
777.91
2646.83
1294.67
1530.34
3417.41
8792.02
Subject 3
552.72
1613.29
844.84
2065.67
486.74
1024.34
1884.30
4703.30
Subject 4
232.62
1308.86
259.04
1548.45
648.99
634.61
1140.66
3491.93
Subject 5
439.40
3558.57
751.24
1676.20
482.49
317.92
1673.13
5552.69
Subject 6
1999.19
2332.60
638.16
1885.22
341.12
924.75
2978.48
5142.57
Subject 7
635.55
1375.96
857.59
1707.64
528.84
645.33
2021.97
3728.94
Average
818.14
2656.30
659.04
1828.76
693.95
781.16
2171.13
5266.23
p
0.010
0.000158301
0.616
0.001
Significant
Difference
p < 0.025
p < 0.01
Not Significant
p < 0.01
(b)
Normalized
powers
Subjects
Spon.
Inter.
Spon.
Inter.
Spon.
Inter.
Subject 1
31.072
76.49
68.93
23.51
0.45
3.25
Subject 2
37.53
63.36
62.47
36.64
0.61
1.73
Subject 3
63.45
66.85
36.55
33.15
1.74
2.02
Subject 4
28.53
70.93
71.47
29.07
0.40
2.44
Subject 5
60.89
84.06
39.11
15.94
1.56
5.27
Subject 6
65.17
67.09
34.83
32.91
1.87
2.04
Subject 7
61.86
72.57
38.14
27.43
1.62
2.65
Average
49.78
71.62
50.22
28.38
1.18
2.77
LF (ms2):
HF (ms2):
LF/HF ratio:
p
0.017
0.017
0.019
Significant
Difference
p < 0.025
p < 0.025
p < 0.025
46
Table 4.5 Rate pressure product comparing spontaneous to intermittent breathing
Mean HR
(beats/min)
Subjects
Mean Beat to Beat
Systolic Pressure
(mmHg)
RPP (beats * mmHg
/min)
Inter.
62.53
Spon.
Inter.
Spon.
Inter.
Subject1
Spon.
62.39
117.41
118.83
7325.81
7430.52
Subject2
84.12
81.92
113.14
113.82
9517.22
9324.75
Subject3
67.76
74.43
122.06
121.32
8270.07
9029.84
Subject4
78.61
75.63
105.64
105.19
8304.18
7955.24
Subject5
67.82
72.67
107.28
108.10
7276.32
7855.41
Subject6
63.96
60.91
121.96
122.32
7800.73
7450.15
Subject7
69.10
77.68
114.78
114.92
7930.74
8927.12
Average
70.54
72.25
114.61
114.93
8060.72
8281.86
p
0.390
0.302
0.333
Significant
Difference
Not Significant
Not Significant
Not Significant
47
Table 4.6 Arterial compliance obtained from measured values using: ‫ ܥ‬ൌ Arterial Compliance
(mL/mmHg)
Subjects
Spon.
Inter.
Subject 1
2.109
2.104
Subject 2
1.415
1.453
Subject 3
1.251
1.139
Subject 4
1.871
1.945
Subject 5
2.234
2.085
Subject 6
1.818
1.909
Subject 7
1.365
1.214
Average
1.643
1.604
p
Significant
Difference
0.471
Not Significant
ௌ௏
௉௉
48
Table 4.7 Beat to beat mean arterial compliance obtained using: ‫ ܥ‬ൌ Mean Arterial
Compliance
(mL/mmHg)
Standard Deviation
of Arterial
Compliance
(mL/mmHg)
Subjects
Spon.
Inter.
Spon.
Inter.
Subject 1
2.116
1.976
0.135
Subject 2
1.410
1.431
Subject 3
1.250
Subject 4
ௌ௏
௉௉
Mean Pulse
Pressure (mmHg)
0.139
Spon.
38.02
Inter.
40.66
0.033
0.045
42.19
42.69
1.153
0.011
0.017
59.02
58.26
1.802
1.930
0.063
0.041
35.34
34.27
Subject 5
2.249
2.134
0.168
0.261
32.94
32.74
Subject 6
2.056
1.972
0.248
0.161
38.60
41.93
Subject 7
1.362
1.215
0.015
0.015
53.15
52.98
Average
1.749
1.687
0.096
0.097
42.75
43.36
p
0.156
0.970
0.380
Significant
Difference
NS
NS
NS
49
Table 4.8 Beat to beat mean arterial compliance obtained using: ‫ ܥ‬ൌ Mean Arterial
Compliance
(mL/mmHg)
Subjects
Standard Deviation
of Arterial
Compliance
(mL/mmHg)
ఛ
ோ௦
Mean Arterial
Pressure (mmHg)
Subject 1
Spon.
2.466
Inter.
2.638
Spon.
0.284
Inter.
0.351
Spon.
92.11
Inter.
92.23
Subject 2
2.129
2.139
0.215
0.399
85.32
85.25
Subject 3
1.267
1.064
0.145
0.191
82.77
82.78
Subject 4
2.079
2.208
0.117
0.328
82.51
82.34
Subject 5
2.963
2.516
0.549
0.480
85.67
85.95
Subject 6
1.851
2.096
0.211
0.249
95.00
95.45
Subject 7
1.342
1.144
0.177
0.190
79.48
79.80
Average
2.014
1.972
0.243
0.312
86.12
86.26
p
0.673
0.107
0.166
Significant
Difference
Not Significant
Not Significant
Not Significant
50
(a)
(b)
Figure 4.1 Tachograms of subject 1 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
51
(a)
(b)
Figure 4.2 Tachograms of subject 2 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
52
(a)
(b)
Figure 4.3 Tachograms of subject 3 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
53
(a)
(b)
Figure 4.4 Tachograms of subject 4 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
54
(a)
(b)
Figure 4.5 Tachograms of subject 5 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
55
(a)
(b)
Figure 4.6 Tachograms of subject 6 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
56
(a)
(b)
Figure 4.7 Tachograms of subject 7 (a) spontaneous breathing and (b) intermittent
breathing. Greater variations in RR intervals can be observed for intermittent breathing.
57
(a)
(b)
Figure 4.8 HRV power spectra calculated with Fourier Transform for subject 1 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
58
(a)
(b)
Figure 4.9 HRV power spectra calculated with Fourier Transform for subject 2 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
59
(a)
(b)
Figure 4.10 HRV power spectra calculated with Fourier Transform for subject 3 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
60
(a)
(b)
Figure 4.11 HRV power spectra calculated with Fourier Transform for subject 4 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
61
(a)
(b)
Figure 4.12 HRV power spectra calculated with Fourier Transform for subject 5 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
62
(a)
(b)
Figure 4.13 HRV power spectra calculated with Fourier Transform for subject 6 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
63
(a)
(b)
Figure 4.14 HRV power spectra calculated with Fourier Transform for subject 7 (a)
spontaneous breathing and (b) intermittent breathing. A shift into the low frequency
range is observed for intermittent breathing.
64
(a)
(b)
Figure 4.15 HRV power spectra calculated with AR model for subject 1 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
65
(a)
(b)
Figure 4.16 HRV power spectra calculated with AR model for subject 2 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
66
(a)
(b)
Figure 4.17 HRV power spectra calculated with AR model for subject 3 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
67
(a)
(b)
Figure 4.18 HRV power spectra calculated with AR model for subject 4 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
68
(a)
(b)
Figure 4.19 HRV power spectra calculated with AR model for subject 5 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
69
(a)
(b)
Figure 4.20 HRV power spectra calculated with AR model for subject 6 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
70
(a)
(b)
Figure 4.21 HRV power spectra calculated with AR model for subject 7 (a) spontaneous
breathing and (b) intermittent breathing. A shift into the low frequency range is observed
for intermittent breathing.
71
(a)
(b)
Figure 4.22 Plots displaying the inverse of pulse pressure (PP) for subject 1 (a)
spontaneous breathing and (b) intermittent breathing.
72
(a)
(b)
Figure 4.23 Plots displaying the inverse of pulse pressure (PP) for subject 2 (a)
spontaneous breathing and (b) intermittent breathing.
73
(a)
(b)
Figure 4.24 Plots displaying the inverse of pulse pressure (PP) for subject 3 (a)
spontaneous breathing and (b) intermittent breathing.
74
(a)
(b)
Figure 4.25 Plots displaying the inverse of pulse pressure (PP) for subject 4 (a)
spontaneous breathing and (b) intermittent breathing.
75
(a)
(b)
Figure 4.26 Plots displaying the inverse of pulse pressure (PP) for subject 5 (a)
spontaneous breathing and (b) intermittent breathing.
76
(a)
(b)
Figure 4.27 Plots displaying the inverse of pulse pressure (PP) for subject 6 (a)
spontaneous breathing and (b) intermittent breathing.
77
(a)
(b)
Figure 4.28 Plots displaying the inverse of pulse pressure (PP) for subject 7 (a)
spontaneous breathing and (b) intermittent breathing.
78
(a)
(b)
Figure 4.29 Histograms displaying the distribution of RR intervals for subject 1 (a)
spontaneous breathing and (b) intermittent breathing.
79
(a)
(b)
Figure 4.30 Histograms displaying the distribution of RR intervals for subject 2 (a)
spontaneous breathing and (b) intermittent breathing.
80
(a)
(b)
Figure 4.31 Histograms displaying the distribution of RR intervals for subject 3 (a)
spontaneous breathing and (b) intermittent breathing.
81
(a)
(b)
Figure 4.32 Histograms displaying the distribution of RR intervals for subject 4 (a)
spontaneous breathing and (b) intermittent breathing.
82
(a)
(b)
Figure 4.33 Histograms displaying the distribution of RR intervals for subject 5 (a)
spontaneous breathing and (b) intermittent breathing.
83
(a)
(b)
Figure 4.34 Histograms displaying the distribution of RR intervals for subject 6 (a)
spontaneous breathing and (b) intermittent breathing.
84
(a)
(b)
Figure 4.35 Histograms displaying the distribution of RR intervals for subject 7 (a)
spontaneous breathing and (b) intermittent breathing.
85
CHAPTER 5. DISCUSSION
Analyses performed in this study form the basis as important diagnostics tools that can be
used to quantify cardiovascular function under normal and diseased conditions. The
present investigation established the applicability in assessing cardiac and vascular
changes as well as autonomic control through controlled breathing perturbation
technique.
5.1 Comparison of Present Findings to Others
Heart rate variability analysis from present experimental results clearly showed heart rate
variation associated with respiration. Respiratory sinus arrhythmia is the term that refers
to the respiratory modulation in the RR time series. The tachograms displayed in Figures
4.1 to 4.7 displays the variation in beat-to-beat RR intervals of the seven subjects during
spontaneous and intermittent breathing. Spontaneous breathing yielded high frequency
small amplitude oscillations. In contrast, intermittent breathing had the same high
frequency small amplitude oscillations due to the 20 second breathing but lower
frequency patterns during the 40 second breath holding. These frequency changes are
observed in the power spectra of these breathing patterns and can be explained in detail
later. It has been said that the amplitudes observed in the tachograms is modulated by
vagal tone (Kollai M et al, 1990) and breathing frequency (Womack BF, 1971).
The time domain variables listed in Table 4.1 compare the variation in RR intervals
during spontaneous and intermittent breathing. Among the time domain variables, the
variable STDNN had a significant increase (p < 0.01) for average intermittent breathing
at 75.86 ms versus spontaneous breathing at 49.66 ms. The increase seen in STDNN of
86
intermittent breathing was mostly due to the large shifts between breath holding and
regular breathing. This is explained by the fact that STDNN reflects both the short-term
and long-term variation of the RR series. As for the other time domain variables, there
were no significant changes between spontaneous and intermittent breathing for Mean
HR, Mean RR, RMSSD, NN50, or pNN50. No significant changes between the other
time domain variables were expected and the results matched a similar study conducted
by Xiao (1999). Histograms displaying the frequency distribution of RR intervals
between spontaneous and intermittent breathing are in Figures 4.29 to 4.35. Spontaneous
breathing patterns show a higher frequency of RR intervals concentrated around the mean
RR interval than intermittent breathing. This is in agreement with the larger STDNN
values shown in Table 4.1 that the variation is heart rate increased during intermittent
breathing.
The power spectra calculated with the Fourier transform are shown in Figures 4.8 to 4.14
that display a shift in the power peaks from high frequency to low frequency when
comparing spontaneous to intermittent breathing. Figure 4.8 clearly shows a high peak
for spontaneous breathing around 0.2 Hz to 0.25 Hz related to the respiratory frequency
relating to respiratory sinus arrhythmia. It has been noted that the respiratory sinus
arrhythmia typically fluctuates with heart rate and ranges from 0.15 to 0.4 Hz (Daly et al,
1986; Lorenzi-Filho et al, 1999; Tarvainen et al, 2009; Xiao, 1999). However, the peak
corresponding to respiratory sinus arrhythmia disappears in the power spectra for
intermittent breathing. The power spectra calculated with the AR method are shown in
Figures 4.15 to 4.21. Results from the AR model matched the results from the Fourier
transform. The power spectra from the AR model displayed a shift in power from high
87
frequency to low frequency when spontaneous breathing was compared to intermittent
breathing. The analysis of the power spectra of spontaneous and intermittent breathing
patterns was consistent with the conclusions drawn from patterns observed in the
tachograms.
The frequency domain variables calculated with the FFT transform are shown in Table
4.3. There is a shift in power from HF to LF verifying the observations seen in the
figures. The LF/HF ratio commonly used to describe the relationship between
sympathetic (LF) and vagal activity (HF). The ratio of LF/HF for intermittent breathing
of 2.89 as larger than the LF/HF ratio for spontaneous breathing 1.17 (p < 0.05). The
frequency domain variables calculated with the AR method in Table 4.4 yielded similar
results. An explanation for this observation is that heart rate variability is normally
regulated by vagal activity during spontaneous breathing, and the effect of CO2 retention
and hypoxia on the arterial chemoreceptors stimulates sympathetic activity and decreases
vagal activity. These results were consistent with observations mentioned by Leuenberger
et al (1995) and Morgan et al (1993).
Myocardial oxygen consumption calculated as RPP from the beat to beat mean systolic
pressure and heart rate is shown in Table 4.5. A few studies have investigated the effects
of stressors such as breath holding on the effects of cardiovascular regulation, blood
pressure, but none has studied myocardial oxygen consumption during HRV. The effect
on heart rate tends to vary based on the type of stressor but it usually decreases (Balogun
et al, 2002; Herd, 1991). In our experiment, measurements for spontaneous breathing for
Mean HR, Mean Beat to Beat Systolic Pressure, and RPP were 70.54 beats/min, 114.61
mmHg, and 8060.72 (beats x mmHg)/min. In contrast, measurements for intermittent
88
breathing was 72.25 beats/min, 114.93 mmHg, and 8281.86 (beats x mmHg)/min. It was
hypothesized that hypoxia, CO2 retention, and breath holding would increase the need for
oxygen in the blood which would result in a greater value for RPP. Values were all
slightly larger for intermittent versus spontaneous breathing, but there was no significant
difference among these values. It appears intermittent breathing did not have enough of a
short-term effect to significantly alter myocardial oxygen consumption.
In terms of arterial compliance, based on the relationship of ୗ୚
௉௉
, we see that PP is
inversely proportional to C. This relationship was plotted against each pulse wave for 300
heart beats in Figures 4.22 to 4.28. It can be seen that PP and C oscillate with respiration,
which is similar to the results seen in the tachograms. Analysis of the intermittent
breathing figures, it shows that compliance slightly increases during inhalation and
decreases during exhalation. There was however, no significant difference observed in
calculated beat to beat C values utilizing both methods shown in Tables 4.6 to 4.8. This is
probably because the arterial wall is slow to adapt to mechanical changes that are of short
duration. Such vascular change in terms of HRV analyzed here has not been studied by
previous investigators.
5.2 Directions for Future Research
The purpose of this study was to investigate short-term perturbations of respiratory
breathing on autonomic control and underlying cardiac and vascular performances.
Results from the analysis demonstrated that the controlled breathing perturbation was an
effective method to investigate autonomic control, although myocardial oxygen
consumption and vascular compliance were less significantly affected during the short-
89
term perturbation. Several improvements can be made for future studies. For instance,
the tachograms patterns seen in Figures 4.3 and 4.7 were not consistent. This is possibly
attributed to the leakage of air during the breath holding periods for intermittent
breathing. Perhaps a nose clip and a mouth piece will be helpful. Also, changes in
experimental protocols such as deep ventilation or controlled breathing rates that actively
modulate tidal volume and vital capacity could provide additional insights.
Mean beat to beat C did not change much, and the variation between beat to beat C was
not significant. It would probably require long term mechanical changes in the arterial
walls to have a great enough impact to truly effect C. Since compliance changes were
due to pulse pressure changes, the patterns seen of C over time were mainly due to
variations in pulse pressure. Also, compliance is a function of blood pressure, or so-called
the pressure-dependent compliance (Li, 2000, 2004). Thus, future study could include
spectral analysis of blood pressure variability. This would provide additional information
regarding cardiovascular regulation. Previous studies have shown that the power spectra
of blood pressure at HF are affected by the mechanical effects of respiration (Saul et al,
1991). However, there have been conflicting results, and one study has shown heart rate
and cardiac output determine power for blood pressure at HF (Saul et al, 1991). Thus,
resolving the confounding effects of blood pressure and heart rate variability, together
with identifying underlying cardiac and vascular changes may provide a more
comprehensive evaluation of overall cardiovascular regulation.
90
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