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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: ϐ ൌͶൈ (3.1) Therefore: ͳͲͲൌͶൈ 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 ିଶగಿ ܺ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 ൌ െ σୀଵ ܽ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 หଵାσసభ ෞ షೕమഏೖ 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 ୗ ൌ ை ுோൈሺೞ ି 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). 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