analysis of heart rate variability and blood pressure...

Analysis of heart rate variability andblood pressure variability in changinggravity levels

Wouter Aerts

Thesis voorgedragen tot het behalenvan de graad van Master of Science

in de ingenieurswetenschappen:biomedische technologie

Promotor:Prof. dr. ir. Sabine Van Huffel

Assessoren:Prof. dr. ir. J. Van Humbeeck

Dr. ir. S. VandeputEm. prof. dr. A. E. Aubert

Begeleiders:Ir. D. Widjaja

Ir. C. Varon

Academiejaar 2011 – 2012

c© Copyright KU Leuven

Without written permission of the thesis supervisor and the author it is forbiddento reproduce or adapt in any form or by any means any part of this publication.Requests for obtaining the right to reproduce or utilize parts of this publicationshould be addressed to Faculteit Ingenieurswetenschappen, Kasteelpark Arenberg 1bus 2200, B-3001 Heverlee, +32-16-321350.

A written permission of the thesis supervisor is also required to use the methods,products, schematics and programs described in this work for industrial or commercialuse, and for submitting this publication in scientific contests.

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Preface

I would like to thank ESA for the oppurtunity to perform the data acquisition duringthe parabolic flight campaign. I would also like to thank everbody who helpedpreparing and performing the experiments, especially Joachim Taelman, StevenVandeput, André Aubert and Sabine Van Huffel. My sincere gratitude also goes toDevy Widjaja and Carolina Varon for their help and constructive feedback a wholeyear long. I would also thank my family and my girlfriend for their support.

Wouter Aerts

i

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Samenvatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

List of Abbreviations and Symbols . . . . . . . . . . . . . . . . . . . . . . . . . ix

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Data aquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Biomedical signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

ECG signal 7, Blood pressure 8, Respiration 10

3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.1 Data pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Acceleration 15, ECG 16, Blood pressure 203.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Data selection 24, Time domain measures 24, Frequencydomain measures 25

3.3 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.4 Cardiovascular model . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Implementation 29, Gravity dependency 32

4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.1 Blood pressure variability . . . . . . . . . . . . . . . . . . . . . . . . 394.2 Heart rate variability . . . . . . . . . . . . . . . . . . . . . . . . . . . 414.3 Cardiovascular model . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Model validation 45, Model results 48

5 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . 59

ii

Contents

A Number of reliable segments . . . . . . . . . . . . . . . . . . . . . . . . . . 65

B Interstitial blood volume loss . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

iii

Abstract

Postflight orthostatic intolerance is a phenomenon from which many astronauts sufferon their return to a normal gravitational environment. Effective countermeasuresare needed to prevent this disorder of the autonomic nervous system (ANS) andcardiovascular system (CVS). This study has the purpose of gaining insight in theresponse of the CVS to gravitational stress during parabolic flights, simulatingMoon (0.16g), Mars (0.38g) and zero (0g) gravity conditions. The sympathovagalmodulation of the CVS is examined via the heart rate variability (HRV) and bloodpressure variability (BPV). During the Joint European Partial-G parabolic flightcampaign, taking place in June 2011, blood pressure and electrocardiogram (ECG)were measured continuously and non-invasively in six healthy subjects. First, the HRVand BPV are assessed using classical time and frequency domain indices calculatedin a 20 seconds interval during the reduced gravity phase. The mean blood pressureshows a significant monotonic increasing trend as a function of gravity and themean pulse pressure shows a slightly decreasing trend toards higher gravities. Nosignificant differences are observed in the mean heart rate. The parasympathetic andsympathetic modulation of the heart rate and blood pressure is found to increase withdecreasing gravity, although the sympathovagal balance is found to be sympatheticpredominant during reduced gravity. Secondly, a model is implemented to simulatethe cardiovascular response to parabolic flights. This model is based on lumpedparameter models simulating the cardiovascular response to head-up tilt tests. Thesimulated results correspond well with the measured data and the time and frequencydomain indices show a similar trend as a function of gravity, found for the measureddata. However, a delay between the simulated and the measured response is found,indicating that the ANS reacted before the parabola actually started, for exampledue to the announcement of the beginning of the each parabola by the pilots. Besidesthe blood pressure and heart rate, the model provides an accurate estimation of thestroke volume (SV) and the cardiac output (CO). Mean SV and mean CO showa decrasing trend as a function of increasing gravity. This study shows that theproposed model is able to reproduce the main components of the observed heart rateand blood pressure and it represents a first step towards a model-based interpretationof the HRV and BPV during parabolic flights.

iv

Samenvatting

Orthostatische intolerantie is een fenomeen waar menig astronaut mee te makenheeft bij hun terugkomst in de normale zwaartekracht omgeving. Efficiënte tegen-maatregelen zijn nodig om deze stoornis van het autonoom zenuwstelsel (AZS) enhet cardiovasculair systeem (CVS) te vermijden. Deze studie heeft daarom als doelom inzicht te verwerven in de reactie van het CVS op de gravitationele veranderingentijdens paraboolvluchten, welke de zwaartekracht simuleren van Maan (0.16g), Mars(0.38g) and gewichtloosheid (0g). De sympathovagale modulatie van het CVS wordtbestudeerd via de hartritmevariabiliteit (HRV) en de bloeddrukvariabiliteit (BDV).Tijdens de ’Joint European Partial-G’ paraboolvluchten campagne, plaatsgevondenin juni 2011, werden bloeddruk en electrocardiogram (ECG) gemeten bij zes gezondeproefpersonen. Als eerste zijn de HRV en BDV bestudeerd, gebruikmakende vanklassieke tijds- en frequentiedomein indices, berekend in een interval van 20 secondentijdens de verminderde zwaartekrachtsfase. De gemiddelde bloeddruk toont eensignificant stijgende trend als functie van de zwaartekracht. De gemiddelde polsdruktoont een licht dalende trend. Geen significante verschillen werden waargenomenvoor de gemiddelde hartslag. De parasympathische en sympathische modulatie vande hartslag en de bloeddruk stijgen met dalende zwaartekracht, hoewel er gevon-den is dat er een sympathetisch overheersing is tijdens verminderde zwaartekracht.Als tweede werd een model geïmplementeerd om het cardiovasculaire gedrag op deparaboolvluchten te simuleren. Het is gebaseerd op lumped-parameter modellenvoor het simuleren van de cardiovasculaire reactie bij tilt tests. De gesimuleerderesultaten vertonen grote overeenkomsten met de opgemeten data en de tijds- enfrequentiedomein indices tonen gelijkaardige trends. Toch is er een vertraging tebemerken tussen de gesimuleerde en opgemeten reactie, wat er op wijst dat hetAZS in staat is om te reageren voor de parabool werkelijk start, bijvoorbeeld doorde aankondiging van de parabool door de piloten. Naast de bloeddruk en hartslagverstrekt het model ook een schatting voor het slagvolume en het hartdebiet. Hetgemiddelde slagvolume en hartdebiet tonen een dalende trend als functie van dezwaartekracht. Deze studie toont aan dat het vooropgesteld model in staat is omde belangrijkste componenten van de geobserveerde hartslag en bloeddruk tijdensparaboolvluchten te reproduceren. Dit is daarbij een eerste stap tot een modelgebaseerde interpretatie van de HRV en BDV tijdens paraboolvluchten.

v

List of Figures

2.1 Acquisition equipment and recorded signals . . . . . . . . . . . . . . . . 52.2 Example of a flight profile during a weigthlessness parabola . . . . . . . 62.3 Einthoven triangle for a 6-lead configuration . . . . . . . . . . . . . . . . 82.4 Comparison between 1 and 6-lead configuration . . . . . . . . . . . . . . 92.5 A 2.5 seconds ECG signal for each subject . . . . . . . . . . . . . . . . . 92.6 Schematic view of blood pressure measurement . . . . . . . . . . . . . . 102.7 Example of the continuous blood pressure signal. . . . . . . . . . . . . . 112.8 Electrical circuit of high pass filter and amplifier . . . . . . . . . . . . . 122.9 Magnitude response of the high pass filter . . . . . . . . . . . . . . . . . 132.10 Example of raw respiration signal . . . . . . . . . . . . . . . . . . . . . . 13

3.1 Example of matching acceleration from Novespace to the accelerationobtained by KU Leuven . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 The intermediate steps and the output of the Pan-Tompkins algorithm . 183.3 Example of the peak detection on the Pan-Tompkins output and ECG

signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.4 Example of the pre-processing of the tachogram with a 20% filter . . . . 213.5 Example of the detection of systolic values . . . . . . . . . . . . . . . . . 223.6 Example of interpolation of a calibration period . . . . . . . . . . . . . . 233.7 Pre-processing steps for the PSD calculation . . . . . . . . . . . . . . . 263.8 Example of a PSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.9 Circuit representation of the lumped parameter model . . . . . . . . . . 303.10 Enlargement of circuit representation of one compartment . . . . . . . . 313.11 Arterial baroreflex and cardiopulmonary reflex mechanism . . . . . . . . 333.12 Characterisation of the simplified, piecewise linear function of the

gravitational constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.13 Simplified, piecewise linear function of the gravitational constant during

the Moon, Mars and zero gravity parabolas . . . . . . . . . . . . . . . . 343.14 Buttock-knee length as defined in [34] . . . . . . . . . . . . . . . . . . . 363.15 RC-circuit to model the interstitial blood volume loss . . . . . . . . . . 363.16 Transcapillary blood flow and interstitial blood volume loss . . . . . . . 37

vi

List of Figures

3.17 Intra-thoracic pressure changes as response to the changing gravity . . . 38

4.1 Mean Pmean and PP and SD and RMSSD Pmean . . . . . . . . . . . . 404.2 SD and RMSSD of the PP and RMSSD/SD of Pmean and PP . . . . . 414.3 Statistical results of the time domain measures (mean - SD - RMSSD -

RMSSD/SD) of the RR intervals as a function of gravity . . . . . . . . . 424.4 Mean RR for each subject as a function gravity level . . . . . . . . . . . 434.5 Statistical results of the frequency domain measures (HF - LF - HFnu -

HF/LF) of the RR intervals as a function of gravity . . . . . . . . . . . 444.6 Example of the simulated blood pressure and heart rate together with

the measured data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.7 Mean error of mean arterial pressure (Pmean), pulse pressure (PP) and

heart rate (HR) for each gravity level . . . . . . . . . . . . . . . . . . . 474.8 Autocorrelation function (ACF) of Pmean, PP and HR for each gravity

level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.9 Example of the simulated blood pressure and heart rate during Mars

gravity for subject 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.10 Mean and RMSSD/SD of the mean arterial pressure (Pmean) and the

pulse pressure (PP) for the simulated data . . . . . . . . . . . . . . . . . 504.11 Comparison of the mean arterial pressure (Pmean) for zero and moon

gravity parabola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.12 Mean, RMSSD/SD, HF and LF/HF of the heart rate (HR) for the

simulated data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.13 Example of stroke volume (SV), heart rate (HR) and cardiac output

(CO) during one parabola . . . . . . . . . . . . . . . . . . . . . . . . . . 534.14 Mean and RMSSD/SD of the stroke volume (SV) and the cardiac output

(CO) for the simulated data . . . . . . . . . . . . . . . . . . . . . . . . . 534.15 End-systolic compliance of left and right ventricle during one parabola . 544.16 Arteriolar resistances and zero pressure filling volumes (ZPFV) of the

four systemic circulation compartements during one parabola . . . . . . 544.17 Statistical results for the delay between simulated and measured mean

arterial blood pressure and heart rate. . . . . . . . . . . . . . . . . . . . 564.18 Schemetic representation of the blood flow in the bent arm during

transition to hypergravity . . . . . . . . . . . . . . . . . . . . . . . . . . 57

B.1 RC-circuit to model the interstitial blood volume loss . . . . . . . . . . 67B.2 Simplified gravity profile . . . . . . . . . . . . . . . . . . . . . . . . . . . 68B.3 Simulated interstitial blood volume and transcapillary flow during

stand-supine test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71B.4 Change in percentage of the plasma volume after standing-supine

experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

vii

List of Tables

3.1 Threshold values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2 Mean duration and range of the different phases. . . . . . . . . . . . . 243.3 Overview of the different statistics . . . . . . . . . . . . . . . . . . . . . 283.4 Parameters of piecewise linear gravity function . . . . . . . . . . . . . . 35

A.1 Number of reliable segments for blood pressure and ECG . . . . . . . . 66

viii

List of Abbreviations and Symbols

Abbreviations

ACF Autocorrelation functionANOVA Analysis of varianceBPV Blood pressure variabilityCO Cardiac outputCVP Central venous pressureDAP Diastolic arterial pressureECG ElectrocardiogramHF High frequency componentHFnu Normalized high frequency componentHR Heart rateHRV Heart rate variabilityIVC Infenior vena cavaLA Left Arm ECG electrodeLF Low frequency componentLFnu Normalized low frequency componentLL Left Leg ECG electrodePdias Diastolic blood pressurePmean Mean arterial blood pressurePP pulse pressurePSD Power spectral densityPsys Systolic blood pressureRA Right Arm ECG electroderes Arteriolar resistance

ix

List of Abbreviations and Symbols

RL Right Leg ECG electrodeRMSSD Root mean square of the squared differencesRR Interval between two R-peaksSAP Systolic arterial pressureSD Standard deviationSNR Signal-to-noise ratioSV Stroke volumeSVC Superior vena cavaTBV Total blood volumeZPFV Zero pressure filling volume

Symbols

α Angle between subject and the groundρ Density of bloodτ Time constant∆t Transition timeC CapicitanceCn Hemodynamical compliancefc Cutoff frequencyg Earth’s gravitational acceleration = 9.81 m/s2

ghyper Gravity constant of hypergravitygmicro Gravity constant of reduced rgravityh Effective hydrostatic lengthPe External pressure sourcePh Hydrostatic pressure sourcePn Pressure at node nPth Intra-thoracic pressureqn Flow through compartment nR Electrical resistanceRn Hemodynamical resistanceThyper Duration of hypergravity phaseTmicro Duration of reduced gravity phaseVmax Maximal interstitial blood volume lossVn Volume in compartment n

x

Chapter

1Introduction

Since the beginning of mankind, the human has the desire to explore the world andto understand all the physical laws, determining how the world is what it is. Duringthe Renaissance, roughly from the 14th to the 17th century, this desire for knowledgepeaked, resulting in many discoveries. Many explorers risked their lives to discovernew continents (Christopher Columbus), to detect new animal species (CharlesDarwin), to find new resources, to comprehend the earth’s physical laws (IsaacNewton), and many more examples can be found. Since the beginning of the 20thcentury, a revolution in science can be remarked. Most of world’s secrets are revealedand the science comes into contact with the limits of the physics. Scientists came upwith new theories to be able to explain processes taking place at these physical limits,like quantum physics (Heisenberg and Schrödinger), theory of relativity (AlbertEinstein), string theory and many more theories can be found. With the end of theexploration of the world, the exploration of the space was started as a race betweenthe United States of America (USA) and the Sovjet Union (USSR) during the ColdWar. Nowadays several planets of our solar system are explored with unmannedvehicles, many research studies are performed in the International Space Station(ISS), the first commercial space shuttles are designed and constructed by companiesand the first preparations are made for sending humans to Mars. The explorationand commercialisation of space is peaking nowadays.

Once going beyond the protective atmosphere of the earth, the astronauts arein the hostile environment of space. They are exposed to harmful cosmic radiationand in particular important, they do not experience any gravity. The body with allof its biological processes, however, is formed on earth with the presence of gravity.In absence of this gravity, the body will adjust itself accordingly. Some of theseprocesses slowly adapt, for example the adaptation of the musculoskeletal system,while other processes react faster to the changing environment, like for example thecardiovascular system [1]. Many research topics are dedicated to study the responseof human biological processes to weightlessness. Some of these studies used datafrom experiments in outer space [2–4], however ground-based studies, simulatingweightlessness, are more cost-effective. These simulation possibilities are head-down-bedrest, head-out-of-water immersion and parabolic flights [1]. All of these reduced

1

1. Introduction

gravity simulations have their own differences as to real reduced gravity, but many ofthe changes in human physiology induced by simulation are similar to those in realspace flight [1]. Only parabolic flights leads to a total lack of hydrostatic pressuregradients, during about 20 seconds of reduced gravity [5].

The cardiovascular system is an important biological process because it regulatesthe blood pressure and heart rate to changes in environment. By insufficient adaptionof this system to gravitational stress, for example when going from supine to standingposition, the person would pass out [6, 7]. This is often seen in astronauts ontheir return to normal gravitational environment after long duration missions inspace. This disorder of the cardiovascular system is called postflight orthostaticintolerance and can be prevented using countermeasures, which tend to counteractthe consequences of the microgravity on the body. Examples can be found in thetraining devices for the astronauts to prevent weakening of the muscular system orin the lower body negative pressure trousers to prevent a decrease of blood volumein the legs [1]. Development of effective countermeasures requires a lots of researchin understanding on how the biological system works and in determining the mostefficient way to fool the system.

Due to gravity changes during parabolic flights, body fluid hydrostatic pressuregradients arise, leading to a blood and body fluid redistribution. Within a parabolicflight, the microgravity is preceded and followed by a hypergravtiy phase. Duringthis hypergravity, blood is shifted to the lower extremities, resulting in a reduction ofvenous return and hence stroke volume [8] and during reduced gravity the opposite istrue [1, 9, 10]. This hemodynamic alternation results in an autonomic reflex responseleading to hemodynamic and cardiac adaptations [11]. The most important reflexmechanisms are the baroreflex and the cardiopulmonary reflex, leading to changesin the autonomic nervous system activity via parasympathetic and sympatheticmodulations. Parasympathetic modulation is found to have a global inhibitory effect,decreasing heart rate, arterial resistance and venous tone, while the sympatheticmodulation is found to have a more excitatory effect [8]. Heart rate variability (HRV)and blood pressure variability (BPV) are two unique tools for obtaining insight intothis modulation of the cardiovascular system. HRV, derived from electrocardiogram(ECG) recordings, and BPV, derived from finger pressure measurements, can both bemeasured non-invasively and in a continuous way. The sympathovagal balance of theautonomic nervous system can be estimated using classical signal analysis methodsin time and frequency domain, calculated in a standardized way as described in[12]. The time domain measures are the mean value, the standard deviation and theroot mean square of the squared differences. The used frequency domain measuresare the high frequency component, low frequency component and their normalizedversions. However, the interpretation of HRV and BPV measurements using theseclassical indices, can be difficult because of the complex mechanisms involved inthe autonomic regulation. Therefore a model-based approach can be useful toease the interpretation of these classical indices, as these mathematical modelsdirectly represent the interactions between the autonomic nervous system and the

2

cardiovascular system [8].

The existing models of the cardiovascular system can be classified into two maincategories: behavioural models and representation models [8]. Behavioural modelsare based on signal processing and identification theories, such as nonlinear additiveautoregressive process with exogenous influence (NAARX) or autoregressive, moving-average models with exogenous input (ARMAX). An iterative least-squares algorithmwill be used to optimize the parameters of these predefined model. The parametersof this model make it possible to distinguish between a patient and control group, asshowed in [13] using a NAARX-model. However the physiological interpretation ofthe parameters is still difficult as there is no direct structural relationship betweenthe model and the physiology.

Representation models are derived in a more pragmatic way, by modelling thephysiology of the different subsystems and connecting them together to make up thecardiovascular system. This way of derivation demands a lot of experiments, butit results in a high insight of the internal functioning of the system. In literature,several models can be found, derived this way, each with their own design and theirown specific aim. Some models predict the short-term cardiovascular response tohead-up tilt test [8, 14, 15], while others for example predict the long-term changesin the body water homeostasis under hormonal regulation [16].

The purpose of this study is to analyse the HRV and BPV during Moon, Marsand zero gravity conditions. Biomedical data of six subjects is collected duringthe Joint European Partial-G parabolic flight campaign, taking place in June 2011.During this flight campaign, parabolas were flown under Moon, Mars and zero gravityconditions. Hypothesized is that there is a monotonic relation between the HRVand BPV, and the gravity conditions. In studies from the Laboratory ExperimentalCardiology at KU Leuven [9, 11, 17] the HRV is examined during parabolic flightsas function of the hypergravity (1.8g), normogravity (1g) and microgravity phase(0g). The BPV is also studied for the same gravity levels in previous studies [5, 10].However with this study it is also possible to analyse the cardiovascular behaviourat intermediate gravity conditions, those from the Moon gravity (0.16g) and Mars(0.38g).

A second aim of this study is to gain insight in the gravity dependency ofthe cardiovascular system. Therefore a cardiovascular model is constructed whichsimulates the transient, short-term, beat-to-beat cardiovascular response to changinggravity during one parabola. A similar purpose of model is found in the cardiovascularmodels made for simulating the cardiovascular response to head-up tilt tests [8, 14, 15].The implementation started from a pre-existing model, developed by Thomas Heldtet al. [15], of which the C++ source code was made available on Physionet [18, 19].Adaptations were made to the source code of this cardiovascular model to simulatethe gravity dependency occurring during the parabolic flights. The simulated bloodpressure and heart rate were compared with the measured data to validate thismodel. After model validation, the model can be used to discuss results and gaininsight in the cardiovascular response.

3

1. Introduction

The second chapter describes the data acquisition during the parabolic flights.In that chapter, more information about the experiment and the acquisition of ECG,blood pressure and respiration is provided. The third chapter gives a total overviewabout the methodology used to describe the HRV and BPV. First the pre-processingof the data is explained. Secondly, a more detailed description of the calculationof time and frequency domain measures is given. Thirdly, the statistical methodto analyze these classical variability indices is explained in detail. Fourthly theimplementation of the cardiovascular model is discussed. The fourth chapter coversthe discussion of the HRV and BPV results, as well as the simulation results of thecardiovascular model. In the fifth chapter a conclusion is formulated and possibilitiesfor further work are mentioned.

4

Chapter

2Data aquisition

Biomedical data is needed to quantify the effect of gravity on our cardiovascularsystem. During the Joint European Partial-G parabolic flight campaign, gravity con-ditions of Moon, Mars and weightlessness were simulated. During these flights severalbiomedical signals, such as ECG, blood pressure and respiration, were measured onsix subjects. This data was collected using a ccNexfin monitor (BMEYE, Amsterdam,The Netherlands), shown in figure 2.1. First the experiment will be discussed more indetail, followed by a discussion of the acquisition of each biomedical signal. Duringthe measurements the collected data was stored on the internal memory of theccNexfin monitor. The data is written in special format files (.csd, .idx, .ses) andwith a program, called FrameInspector (version 1.32.0.0, BMEYE), the data fileswere converted such that they are compatible with the Matlab platform (.bin, .csv)[20].

Figure 2.1: Acquisition equipment and recorded signals - The equipment,consisting of two ccNexfin monitor, is shown in the middle with the recorded signals

around it.

5

2. Data aquisition

2.1 Experiment

During one parabolic flight 31 parabolas were flown, 1 test parabola (0.16g - Moon),12 Moon parabolas (0.16g), 12 Mars parabolas (0.38g), 6 zero gravity parabolas (0g).These flights were performed with an Airbus A300 with the interior specially fittedout to accomodate experiments. The company Novespace was responsible to performthese parabolic flights. To allow the aircraft to free-fly a parabolic trajectory, anaccelerating entry flight is followed by a pull-up and engine throttle back [1, 21].During the apex of the parabolic trajectory the passengers experience reduced gravity.After the apex an accelerating exit flight is followed by a pull-down phase. Theflight profile of a zero gravity parabola is visualized in figure 2.2. During pull-up andpull-down phase an increased gravity is experienced. By changing the attack angleand the velocity of the plane, the combination of the lift, drag on the body of theplane, engine push and gravity weight changes, resulting in other simulated gravityconditions, like those of Moon or Mars.

Figure 2.2: Example of a flight profile during a weigthlessness parabola -The flight profile consist of a pull-up phase, reduced gravity phase and a pull-downphase. During pull-up and pull-down phase an increased gravity will be experienced.By changing the attack angle and the velocity of the plane, other gravity conditionscan be simulated. Figure taken from Labratory for Space and Microgravity Research

[22]

Each parabola can be divided into 5 phases, a normogravity phase before thestart of the parabola (phase 1), one during the hypergravity phase before (phase2) and after (phase 4) the reduced gravity phase (phase 3) and a normogravityphase after the parabola (phase 5). The acceleration experienced during these flightsis measured by Novespace, with a sampling frequency of 16 Hz; and also by KULeuven using a home-made accelerometer circuit, with a sampling frequency of 200Hz. These signals are necessary to be able to segment the biomedical signals.

Six subjects were selected for this study. These 6 volunteers were healthy non-smoking men between 22 and 32 years of age (mean ± SD: 28 ± 5 year; stature: 181

6

2.2. Biomedical signals

± 2 cm; mass: 76 ± 7 kg; BMI: 23 ± 2). They are free from any cardiovascularpathology and a special flight medico-physical examination was performed at theMedical center of the Belgian Air Force, Brussels, 2 months before the flight campaignin order to pass FAA III tests.

To eliminate the effects of pharmalogical agents that might alter cardiovascularautonomic nervous system control, neither general medications nor medications forthe control of motion sickness were taken before or during flights. One subjectsuffered from severe nausea during the second parabola and received scopolamine, amediation for control of motion sickness. Therefore his data is left out of the analysis.Due to nausea and vomiting symptoms, another subject did not finished parabola18 and 19. To prevent free-floating during reduced gravity phase, the subjects werefixed in their seats with a seatbelt and with a line around their feets. One personaccompanied the test subjects to operate the ccNexfin monitor.

Besides measurements during the parabolas, also baseline measurements wereperformed before and after the parabolic flight. These preflight and postflight baselinemeasurements were performed using a fixed protocol with changing position: 10 minin supine position, 10 min standing and 10 min sitting position.

2.2 Biomedical signals

2.2.1 ECG signal

The ECG signal is a transthoracic interpretation of the electrical activity of the heartover a period of time. The polarization of the heart starts at the atria and travels asa wave to the apex of the heart. The resulting polarization vector of the heart ismeasured by projecting it onto an axis determined by the two measuring electrodes.Two different ccNexfin monitors were used, one with a 1-lead configuration and onewith a 6-lead configuration. Sticky foam electrodes with a gel cavity were used. Toachieve a good adhesion and contact of these electrodes, the skin was cleaned withan alcohol wipe to ensure the skin to be dry and clear of oil. The ECG signal wasrecorded with a sampling frequency of 1000 Hz.

For the 1-lead configuration 2 electrodes (RA and LA) were placed on the upperpart of the chest, on the right and left side, respectively. The third electrode (LL)was placed on the left side of the under part of the abdomen, just above the seatbelt.With one electrode (LA) grounded (0V), ECG is measured between the two otherelectrodes (RA - LL). However, afterwards it was found that the postions of electrodeLA and LL were interchanged, leading to a measurement of the ECG betweenelectrode RA and LA. This gives rise too a more noisy signal and a change of theshape of the ECG signal. For the 6-lead configuration 2 electrodes (RA and LA)were placed on the upper part of the chest on the right and left side respectively.The other 2 electrodes (RL and LL) were placed on the right and left side of theunder part of the abdomen. With one electrode (RL) grounded, ECG is measured in6 different ways as explained in the triangle of Einthoven, see figure 2.3. To analysisthe ECG signal, only the lead II (RA - LL) ECG signal is used.

7

2. Data aquisition

Figure 2.3: Einthoven triangle for a 6-lead configuration - The ECG ismeasured in 6 different ways. The heart polarisation vector will be projected on lead

I, II or III or on aVR, aVL, aVF. Figure taken from [23]

The ECG signals measured using the 6-lead configuration are less influenced bynoise and movement compared to those from the 1-lead configuration. This canbe seen in figure 2.4, which shows a movement artifact of the skin, resulting in abaseline drift, for the two configurations. For the 6-lead configuration this artifactdoes not lead to a loss of the R peaks, while for the 1-lead configuration the R peakscannot be distinguished from the noise. The wrong connection of the electrodes forthe 1-lead configuration lowers the amplitude of the R peaks with almost an order ofmagnitude compared to the 6-lead configuration. This leads to a lower signal-to-noiseratio (SNR) for the 1-lead configuration. Figure 2.5 shows the ECG signal in a 2.5seconds interval for every subject. ECG of subject 1, 3 and 5 is measured using 6-leadconfiguration while the other subjects were attached to an 1-lead configuration. Ascan be seen the amplitudes of the R peaks of the first group are higher. For 1-leadconfiguration the S wave is largely negative and the T wave is as high as the R peak,as a result of the relative orientation between the cardiac axis and the placement ofthe electrodes. These differences in form and noise need to be taken into accountwhen pre-processing the signals.

2.2.2 Blood pressure

The blood pressure signal is measured non-invasively with a sampling frequency of200 Hz. This measurement of continuous blood pressure is based on the Finaprestechnology, using the principle of the volume clamp method [24]. With this method,finger arterial pressure is measured using an inflatable bladder in a finger cuff incombination with an infrared plethysmograph [25]. First the unloaded diameterof the finger artery is determined, which occurs when the finger cuff pressure andintra-arterial pressure are equal . At this unloaded diameter the transmural pressureacross the finger arterial wall is zero. The diameter of the artery is measured using

8


Figure 2.4: Comparison between 1 and 6-lead configuration - Left shows amovement artifact for a ECG signal measured in 6-lead configuration. As can seenthe R peaks remain visible and can be detected. Right shows a movement for a 1-leadconfiguration. The R peaks are not visible anymore and can be detected. Remarkalso the difference in amplitude of the R peaks between the 2 different configurations

Figure 2.5: A 2.5 seconds ECG signal for each subject - ECG of subject1, 3 and 5 is measured using 6-lead configuration while the other subjects wereattached to a 1-lead configuration. Remark the higher R peak amplitudes for 6-leadconfiguration. For 1-lead configuration the S wave is largely negative and the T wave

is as high as the R peak.

9

2. Data aquisition

the infrared plethysmography. Figure 2.6 shows a schematic view of the finger withthe finger cuff and with the light source and light detector of the plethysmograph.After knowing the unloaded arterial diameter the artery is clamped at this unloadeddiameter by varying the pressure of the finger cuff inflatable bladder using the fastcuff pressure control system. The cuff pressure thus provides an indirect measure ofintra-arterial pressure.

Figure 2.6: Schematic view of blood pressure measurement - The blue andblack circles represent the infrared light source and detector, respectively. They areused to measure the diameter of the finger arteries, indicated by the small red circles.The pressure cuff around the finger is used to clamp the artery at its unloadeddiameter. The pressure in the cuff will be the same as the intra-arterial pressure.

Figure taken from [25]

The unloaded diameter of the finger artery will vary due to changes in hematocrit,stress and the tone of smooth muscle in the arterial wall. Because the value of thisunloaded diameter is crucial for the accuracy of the measurement, the unloadeddiameter is estimated during several calibration periods. Therefore periods ofconstant cuff pressure are required to adjust the diameter based on the signalfrom the plethysmograph. The PhysioCal (abbreviation for Physiologic Calibration)algorithm used in the ccNexfin monitor does not only use the amplitude, but alsointerprets the shape of the plethysmograph signal during 2 periods of constant cuffpressure. Figure 2.7 shows an example of the continuous blood pressure signal with acalibration period between 10-12 seconds. More information of the working principlecan be found on the website of Finapres Medical Systems B.V. [25].

The hand with the finger cuff is held at heart level with a bandage. In this waythe measured blood pressure is the same as the one at the heart, without an extrahydrostatic pressure due to a hydrostatic column between heart and finger.

2.2.3 Respiration

The respiration is measured with the MLT1132 Respiratory Belt Transducer fromADInstruments [26]. The belt was worn around the abdomen at the level of thediaphragm and during respiration the abdominal circumference changes, leading toa stretch in the belt. This stretching places a strain on the piezo-electrical sensor,which generates a voltage. This voltage is amplified, after the signal is filtered with

10


Figure 2.7: Example of the continuous blood pressure signal. - Exampleof the continuous blood pressure with a calibration period between 10-12 seconds,

which consist out of two constant pressure periods.

a high pass filter to eliminate baseline drift. The output voltage of the piezo sensorchanges linearly with the change in circumference and therefore it is a good measureof the respiration. The signal is collected with a sampling frequency of 200 Hz.Figure 2.8 shows the electrical circuits of the high pass filter and the amplifier. Theinput voltage of the filter is the voltage generated by the piezo sensor and the outputvoltage of the filter is the input voltage to the amplifier. The high pass filter is aSallen-Key high pass filter of second order [27], meaning that there is an attenuationof 40dB per decade and the cutoff frequency (fc) is determined as:

fc = 12π√R1R2C1C2

(2.1)

where R1, R2, C1 and C2 are resistances and capacitances as shown in figure2.8. It was chosen to take R1 = R2 = 2.28MΩ and C1 = C2 = 820pF, such thatthe cutoff frequency is 85.13 Hz. The ratio between R3 and R4 determines theamplification of the output voltage, but it has also an effect on the Q-factor, whichdetermined the bandwidth of the filter. Because the output of the filter was amplifiedin a second stage, no additional amplification is needed and therefore it was chosento take R3 = R4 = 1000Ω. The range of the normal breathing frequencies lies inbetween 8 and 20 breaths per minute and as result of the high cutoff frequency,these frequencies are filtered out with an attenuation of about -50 dB, as shownin figure 2.9. In this figure, the thin blue line shows the transfer function of thishigh pass filter, while the vertical green line indicates the cutoff frequency and thevertical black lines indicate the range of the normal breathing frequencies. Due tothe attenuation of the lower frequencies, there is also a distortion of the respiration

11

2. Data aquisition

signal. For example, a normal breath cycle is characterized by a period of constantchest circumference during expiration, but this is filtered out. This makes it hard touse these respiration signals. A comparison afterwards with the signal from a nasalthermistor shows this disparity with the respiration signal measured using the belt,making the measured respiration signals unreliable. Therefore the respiration signalsare not used in the rest of this study. An other source of noise is the influence ofthe signal to other movements of the abdomen, for example when the subjects weretalking or when they moved for repositioning. This leads to very noisy signals, wereit is almost impossible to determine the respiration signal. Figure 2.10 shows anexample of a respiration signal with a movement artifact.

Figure 2.8: Electrical circuit of high pass filter and amplifier - FigureA shows the circuit for the high pass filter, figure B shows the circuit for theamplifier. The values for the resistances and capacitances are: R1 = R2 = 2.28MΩ,C1 = C2 = 820pF, R3 = R4 = 1000Ω and R5 = 1000Ω. The resistance R6 is tuned

such that the amplication of the signal is sufficient.

12


Figure 2.9: Magnitude response of the high pass filter - The transfer functionof the high pass filter is shwon in blue. The black vertical lines indicate the range ofnormal breathing frequencies, while the red vertical line indicates the cutoff frequency.

Note that there is an attenuation of about -50dB at the breathing frequency.

Figure 2.10: Example of raw respiration signal - Raw respiration signal withsome movement artifacts

13

Chapter

3Methodology

In this chapter the methodology used in this study is explained more in detail. Thefirst section covers the data pre-processing of the different signals, needed to dofurther analysis on them. The second section discuss the techniques used to analyzethe data, using time and frequency domain measures and the statistical analysis.The third section gives information about the implementation of the cardiovascularmodel to simulate the cardiovascular response to gravity changes during parabolicflights.

3.1 Data pre-processing

In this section the data pre-processing will be discussed. Using the equipment asdescribed in previous chapter raw signals were obtained such as an ECG signal,a blood pressure signal and an acceleration signal. These signals need first to bepre-processed to be able to do further analysis on them. In the followong subsectionsthe pre-processing of the acceleration, ECG and blood pressure will be discussed.

3.1.1 Acceleration

The acceleration data is recorded in two ways, one with Novespace equipment andone using a home-made accelerometer circuit from KU Leuven. The accelerationfrom Novespace has a better SNR and it is calibrated to obtain values expressedin g, although it is recorded during the whole flight without a relation with thebiomedical signals. In contrast, the acceleration from KU Leuven is measured atthe same time as the biomedical signals. The acceleration data from KU Leuvencan therefore be used to segment the biomedical data into the different parabolasand phases. Because the acceleration from KU Leuven is not calibrated, it givesvalues in millivolts instead of in g, the segmentation is done by first segmenting theacceleration data from Novespace and matching these data with those obtained fromKU Leuven.

First the acceleration data from Novespace is segmented into the different parabo-las, subdivided into the different phases, as mentioned in chapter 2. These different

15

3. Methodology

phases are detected using threshold values. The end of phase 1 is determined asthe last 1g value before detecting the acceleration which exceeds a threshold of 1.5g.The start and ending of phase 3 is detected as the first and last point that exceedthe gravity dependent threshold. The start of phase 5 is determined in a similar wayas the end of phase 1. Table 3.1 gives an overview of the threshold values used forthe different phases.

Table 3.1: Threshold values - Overview of the threshold values used for thesegmentation.

Threshold valuesparabola phase 1 phase 2 phase 3 phase 4 phase 5Moon 0,1667 gMars 1 g 1,5 g 0,38 g 1,5 g 1 gzero 0,05 g

After this segmentation the acceleration data from KU Leuven is matched withthose from Novespace by using the cross correlation function. The data measuredis split into several data segments. The number of the first parabola of such adata segment is known and makes it possible to extract the corresponding parabolafrom the Novespace acceleration. This parabola part is correlated with the wholeacceleration data from KU Leuven and the first maximum near 1 from this cross-correlation gives an estimation of the time delay between the two acceleration signals.Using this delay the acceleration signals are matched and all the biomedical signalsare segmented. Figure 3.1 shows an example of this matching process. Figure 3.1.ashows one parabola extracted from the Novespace data and this parabola will beused as a template to correlate with the acceleration signal from KU Leuven, shownin figure 3.1.b. The cross correlation function is shown in figure 3.1.c. The delaybetween the two acceleration signals is determined as the first maximum of the crosscorrelation function and is indicated with a red dot. Before calculating the crosscorrelation function, the acceleration signal from KU Leuven is passed through aButterworth low pass filter of order 8 and with a normalized cutoff frequency of 0.05to eliminate the high frequency noise. The acceleration signal from Novespace isresampled to 200 Hz, to obtain two signals with the same sampling frequency.

3.1.2 ECG

For further analysis, a tachogram was derived from the ECG signal. A tachogramcontains the interval duration between two consecutive R-peaks as a function of time.To derive the tachogram, the locations of the R-peaks need to be detected. This isdone by pre-processing the ECG signal using the Pan-Tompkins algorithm, followedby an adaptive thresholding and a searchback procedure.

The pre-processing of the ECG signal, using the Pan-Tompkins algorithm asdescribed in [28], is needed to enhance R-peaks. The output of this Pan-Tompkins

16

3.1. Data pre-processing

Figure 3.1: Example of matching acceleration from Novespace to theacceleration obtained by KU Leuven - Plot a. shows 1 parabola extracted fromthe Novespace data and this parabola will be used as a template to correlate with theacceleration signal from KU Leuven, shown in plot b. The cross correlation functionis shown in plot c. The delay between the two acceleration signals is determined asthe first maximum of the cross correlation function and is indicated with a red dot.

algorithm is based on analysis of the slope, amplitude and width of the QRS complexes.The algorithm includes the following series of filters on the orignal ECG signal:

• lowpass filtering with cutoff frequency of 11 Hz,

• highpas filtering with cutoff frequency of 5 Hz,

• derivative-based filtering using a second order scheme,

• squaring,

• integrating by using a moving-window integration filter with window length of0.15 seconds,

For each filter the corresponding delay is determined and summed to obtain atotal delay. This delay is necessary to compensate the locations of the peaks in thePan-Tompkins output for the location of the R peaks in the ECG signal. Figure 3.2shows the intermediate steps and the output of the Pan-Tompkins algorithm.

The peaks of the Pan-Tompkins output correspond to the R-peaks with takinginto account the total delay. A simple peak detection algorithm is used to detect allpeaks and an adaptive thresholding with searchback procedure is used to select allpeaks corresponding to an R-peak, such that the noise is not detected. A peak isdetected as R-peak when the amplitude of the peak is larger than a certain thresholdvalue. This threshold value will be adapted throughout the procedure by:

threshold = NPKI + 0.125(SPKI −NPKI) (3.1)

17

3. Methodology

Figure 3.2: The intermediate steps and the output of the Pan-Tompkinsalgorithm - Shown are the intermediate steps and the output of the Pan-Tompkinsalgorithm on a noisy 10 seconds ECG signal from subject 4. From left to right : theresampled ECG signal; the band pass filtered signal; the signal after passing throughthe derivative filter; the squared signal; and the integrated version of the squaredsignal. This last signal is the output of the Pan-Tompkins algorithm. The peaks of

the Pan-Tompkins output correspond to the R-peaks in the ECG signal.18


where SPKI represents the peak level that the algorithm has learned to be thatcorresponding to R-peaks, and NPKI represents the peak level related to non-R-peakevents, for example due to noise. These values are also adapted using followingequation:

SPKI = 0.125Peak(i) + 0.875SPKI if Peak(i) is an R-peak (3.2)NPKI = 0.125Peak(i) + 0.875NPKI if Peak(i) is a noise peak (3.3)

with Peak(i) the current peak which is selected as noise or R-peak. The value0.125 in equation 3.1 is slightly different as the value of 0.25 found in [28]. Our datacontains a lot of noise and using the normal formula would result in a too largethreshold value.

If one R-peak is missing in a certain interval, the searchback procedure detectsthis. It starts the selection of the R-peaks over again, starting from the location ofthe previous detected R-peak and it halves the threshold, such that smaller peaksare selected. An R-peak is missing if the duration between a peak and the previousdetected R-peak exceeds the limit of 1.66 times the average RR interval. This averageRR interval is determined by averaging the distances between the last 4 R-peaks. Toensure that there will be no endless loop, the number of searchbacks after each otheris limited to 5. After these 5 times the average RR interval is set to 1.66 times theaverage RR interval and the selection procedure will just proceed.

When a peak exceeds the threshold, it is checked if the distance between thisR-peak and the previous one is not too small, defined by a blanking period of 75%of the average RR interval. However if one peak is detected in the blanking periodof the previous selected R-peak, it is checked which one of the two peaks is the morecorrect R-peak. This is performed using the following decision rules:

Peak(i)|RRnew −RRav|

>Rpeak

|RR−RRav|⇒ Peak(i) is the correct R-peak (3.4)

Peak(i)|RRnew −RRav|

<Rpeak

|RR−RRav|⇒ Rpeak is the correct R-peak (3.5)

Via these formula the amplitude of the new selected peak (Peak(i)) and thedistance to the previous one (|RRnew − RRav|)is compared with those from theprevious one. The one with the highest amplitude and with the distance closer to theaverage RR interval, will be selected as the most correct one. This blanking periodimplementation is necessary due to the noisy ECG signal from the 1-lead configuration,leading to some spurious peaks appearing in the output of Pan-Tompkins algorithm.The thresholds and parameter value of the searchback procedure and blanking periodare taken from a study of Widjaja et al. [29]. The implementation of this blankingperiod leads to the consequence that the RR interval can not change with 75% withrespect to the average one, but physiologically this is not the case.

19

3. Methodology

The initial threshold, SPKI, NPKI and average RR interval are choosen by theoperator by clicking on the two first peaks of the output. The R-peaks in theECG signal are detected by finding the maximum in a small interval around thecorresponding locations determined from the Pan-Tompkins output, compensatedwith the total delay of the pre-processing filters. Figure 3.3 shows an example of thepeak detection on the Pan-Tompkins output and on the ECG signal.

Figure 3.3: Example of the peak detection on the Pan-Tompkins outputand ECG signal - The figure on the left shows the peak detection on the Pan-Tompkins output. As can seen all peaks are detected well, notwithstanding thelarge variation in amplitude of the peaks. The figure on the right shown the R-peak detection in the ECG signal. Despite of the low SNR and large T-waves, the

algorithm is capable to detect all R-peaks well.

The tachogram is pre-processed further to eliminate the detection of ectopicbeats. Ectopic beats is a disturbance of the cardiac rhythm, leading to an extraR-peak right after an other R-peak without in between a T-wave. This is eliminatedwith a so called 20% filter: RR intervals differing more than 20% of the previousinterval are replaced by the average value of the 5 preceding and 5 following intervals[29]. Figure 3.4 shows an example of elimination of the ectopic beats by using a 20%filter. The two ectopic beats occurring in the reduced gravity phase around 90 and110 seconds are eliminated after these pre-processing step.

3.1.3 Blood pressure

The blood pressure is measured continuously as mentioned in chapter 2. For furtheranalysis, a systogram and diastogram are obtained, containing the systolic anddiastolic blood pressure values as a function of time. The systolic and diastolic valuesare the maximum and minimum blood pressure values, respectively. The systolicvalues are obtained by simple thresholding of a pre-processed blood pressure signalwith the root mean square value as threshold. The blood pressure is pre-processedby resampling from 200 Hz to 100 Hz and filtering using a Butterworth high pass

20


Figure 3.4: Example of the pre-processing of the tachogram with a 20%filter - The upper plot shows the tachogram before, and the lower plot shows thetachogram after passing through the 20% filter. As can seen the ectopic beats

occurring at about 90 and 110 seconds were eliminated.

filter with a normalized cutoff frequency of 0.03 and an order of 4 to remove thebaseline drift. Secondly, adaptive thresholding is used to detect the peaks in order todetermine the locations of the systolic values. The absolute systolic blood pressurevalue is obtained by searching the maximum in the original blood pressure signal(without pre-processing) in an interval around the location found in the previousstep. This is necessary because the filtering changes the magnitude of the signal. Asearchback procedure to find the missing peaks could not be implemented due tothe calibration periods, however after manual checking, it was found that this wasnot necessary because no maxima were missed. Figure 3.5 shows an example of thesystolic values detection. Once the systolic values are detected the diastolic valuesare easily found as the minima between two consecutive systolic values. For analysisit is also interesting to describe the blood pressure using two other values, which arethe mean arterial pressure (Pmean) and the pulse pressure (PP). Pmean and PPare calculated as shown in the following equations:

Pmean =Psys + 2Pdias

3 (3.6)

PP = Psys − Pdias (3.7)

The calibration periods in the blood pressure signal, needed to determine thecorrect unloaded diameter of the finger artery, leads to parts with no blood pressure

21

3. Methodology

Figure 3.5: Example of the detection of systolic values - Figure on the leftshows the filtered blood pressure signal with a Butterworth high pass filter to removethe baseline drift. The horizontal line indicates the threshold used to find the peaks.This threshold is given by the root mean square of the blood pressure signal. Usingthe locations of these peaks the magnitude of the systolic value is found in the

original signal as shown in the figure on the right.

information. Some pressure signals contain many calibration periods and leavingthese segments out of the analysis would lead to a huge loss of data. To overcomethis problem the blood pressure is interpolated at the moments of calibration. Thisis done by using a cubic spline interpolation at the estimated interpolation locations.These interpolation locations are determined as the mean of a forward and a backwardestimation.

First a forward estimation is computed, using the average interval betweenconsecutive pressure values from the 6 preceding systolic or diastolic values. Addingthis reference interval to the last location, leads to a first interpolation location. Anew reference interval can be estimated and added to the first interpolation location,leading to the second interpolation location. This will be repeated till the end ofthe calibration period is reached. The same is done for the backward estimation butstarting from the end of the calibration period and using the 6 following values. Theactual interpolation locations are calculated as the mean of the forward and backwardestimations. This leads to a better result than when only using forward or backwardestimations because extrapolation leads to large errors, especially at the borders ofthe calibration period. Figure 3.6 shows an example of this estimation process. Thegreen vertical lines indicate the borders of the calibration period and the red circlesindicate the 6 points used for the forward and 6 points for the backward estimation.The magenta colored circles mark the backward estimated locations, while the blackcolored circles mark the forward estimated locations. The red asterixes are thesystolic values interpolated using a cubic spline approximation. An advantage of thiscalculation method is that when the ECG signal is too noisy, the locations of the

22

3.2. Data analysis

systolic or diastolic values could be used as an estimation of the locations of the Rpeak, leading to a tachogram without using the ECG signal.

Figure 3.6: Example of interpolation of a calibration period - The greenvertical lines indicate the borders of the calibration period and the red circles indicatethe 6 points used for the forward and 6 points for the backward estimation. Themagenta colored circles mark the backward estimated locations, while the blackcolored circles mark the forward estimated locations. The red asterixes are the

systolic values interpolated using a cubic spline approximation.

3.2 Data analysis

After acquisition and pre-processing of the data, the data can be analysed, usingseveral measures. In this section the relevant measures will be discussed. The analysisis performed either in time or in frequency domain and the measures are calculatedin a certain window. First more information will be given about the data selection.Before starting the analysis the signals are checked on noise. Appendix A gives anoverview about how many parabolas can be used per subject, per phase and pergravity level.

23

3. Methodology

Table 3.2: Mean duration and range of the different phases.

Mean (s) range (s)

phase 2 13.6 (8.0 - 17.5)phase 3 (Zero) 21.6 (20.5 - 22.2)

phase 3 (Moon) 24.5 (21.0 - 26.0)phase 3 (Mars) 30.2 (26.5 - 32.5)

phase 4 11.9 (2.8 - 18)

3.2.1 Data selection

The time and frequency domain measures are calculated in a fixed segment durationin order to be able to compare the different results. This segment duration is chosenusing the information about the duration of the different phases, as shown in table3.2. The duration of phase 1 and phase 5 are not shown because these phases arelarge enough to pose no problems with the duration selection. The duration forphase 3 depends on the different gravity levels. To compare all the phases together,a minimal duration of about 8 or 10 seconds is needed, resulting from the durationfor phase 2 and 4. But this will result in a loss of data for the other segments. Thatis why a minimal duration of 20 seconds is chosen, resulting from the duration rangeof phase 3 during zero gravity parabola. This duration makes it also possible tocompare the results with previous studies. By using a window length of 20 seconds,phase 2 and 4 can not be investigated for the rest of the analysis. Other studiesin the past, like the studies from Frank Beckers and Bart Verheyden [9, 11], wereable to use the hypergravity phase in their analysis due to the different definitionof the tresholds for the segmentation. Because the relation between gravity andthe cardiovascular response during the reduced gravity phase is of major interest,excluding phase 2 and 4 is supposed not to be problematic.

Every segment for phase 1, 3 and 5 is truncated to the minimal duration of20 seconds. There is choosen to take the middle part of the segments for phase3, because there the influence of the hypergravity is reduced, while there is stillexpected to be a large modulation of the autonomic nervous system. For phase 1 thelast part of the segment is chosen, while for phase 5 the first part is taken, discardingthe first 5 seconds to eliminate the influence of hypergravity.

3.2.2 Time domain measures

The heart rate variability and blood pressure variability is characterized using timedomain measures. For each segment, the mean value, standard deviation (SD)and root mean square of the squared differences (RMSSD) are calculated, in astandardized way as described in [12]. The SD reflects the power of the wholeautonomic modulation, while the the RMSSD reflects alterations in autonomic tonethat are predominantly vagally mediated [30]. The benefits of these time domainmeasures were proven in several studies to identify cardiovascular problems. Time

24

3.2. Data analysis

domain measures changes with the duration of calculation and thus a fixed durationlength is necessary.

3.2.3 Frequency domain measures

Another way to characterize the heart rate variability and its autonomic modulation,is by using frequency domain measures [12]. A high frequency (HF) and low frequency(LF) component of the HRV is calculated. The HF component relates with the fastparasympathetic nervous modulation of the heart rate, while the LF component isfound to be an indication of the sympathetic modulation with some vagal influences.In a study of Malliani [31] they have showed an antagonistic interaction between thosetwo modulations, and therefore the interpretation of the LF and HF componentsalone is not sufficient to discuss the sympathovagal balance. A better approachwould be to compare the two frequency components, using the normalized HFnu(HFnu= HF

LF+HF) and LFnu (LFnu= LFLF+HF) component, or using the ratio of

the LF component to the HF component (LF/HF). A higher LFnu and a higherLF/HF mean a higher predominance of sympathetic activity, while a higher HFnucomponent and lower LF/HF indicate a higher predominance of parasympatheticactivity. Between time and frequency domain indices, established correlations havebeen found: RMSSD correlates with the HF power component (r = 0.96), while SDis found to correlate significantly with the total power [30].

The frequency measures are determined as the integral of the power spectraldensity (PSD). Due to the small duration of the segments additional signal pre-processing needs to be performed before calculating the PSD. The steps followed aredescribed in a paper of Verheyden et al. [11]:

• resampling the tachogram at 2 Hz using a cubic spline interpolation,

• linear trend removal to prevent DC and artificial low frequency contribution,

• tapering both ends performed with a Hamming window [A = 0.08 + 0.46(1−cos 2πt

T )],

• assesing stationarity by comparing the difference of a running mean 〈xi〉(with sliding window of 6 points) with the SD value of the whole segment(|〈xi〉 − 〈xi+1〉| < SDtot) ,

To improve the frequency resolution a last important step is performed. Thevery short duration of the segments (20s) give rise to a time-frequency conflict dueto the Heisenberg principle [11]. Via a mathematical technique of zero paddingthe frequency resolution can be enhanced artificially. This is done by adding zerosto both segment sides to obtain 256 equally spaced data points. This leads to afrequency resolution of 0.0078 Hz. The maximal frequency is 1 Hz (= fs

2 , Nyquistcriterion), which is lowered to 0.5 Hz due to the transfer function of the Hamming

25

3. Methodology

Figure 3.7: Pre-processing steps for the PSD calculation - From top tobottom: the resampled RR intervals with in red its linear trend; the detrended RRintervals by subtracting the linear trend from the resampled RR intervals; the RRintervals after passing trough a Hamming filter; the RR intervals after zero padding.

This latter is used to calculate the PSD.

window [32]. The lowest measurable frequency is determined by the minimal lengthand is 0.05 Hz (= 1

20s). Figure 3.7 shows the different preprocessing steps.

The PSD is simply calculated as the squared Fourier transform of the pre-processed tachogram. The LF component and the HF component are computed byintegrating the PSD in specific bands. For the LF component this starts from thelowest measurable frequency (0.05 Hz) up to 0.15 Hz. The HF component rangesfrom 0.15 Hz up to 0.4 Hz [11, 12]. Figure 3.8 shows an example of a PSD withthe LF and HF limits marked with vertical lines. Due to spectral leakage after theconvolution of the signal with the Hamming window, the LF component should beinterpreted carefully. The spectral leakage results in a higher value for LF componentthan actually occurring. This method of calculation is validated in a work of Aubertet al. [32].

26

3.3. Statistical analysis

Figure 3.8: Example of a PSD - PSD from the previous preprocessed RRintervals. The vertical lines marked the limits for the LF (black) and HF (red)

components.

3.3 Statistical analysis

Instead of making conclusions about the cardiovascular response of one person, aconclusion about the response of the whole population is preferred. To be able tomake a conclusion for the whole population by just using a small sample of subjects,statistics need to be used. Table 3.3 gives an overview of the most commonly usedstatistics, all with their own scope. The statistics can be divided into two main groups,according to which assumptions are made. The parametric statistics assume normaldistributed values and lead to a conclusion about the mean and standard deviation ofthe population. The non-parametric statistics assume identical distribution for all thesubgroups in the analysis, which could be different from the normal distribution, butthey lead only to a conclusion about the mean rank of the population, no absolutevalues are obtained. A second subdivision depends on the experiment, which could bepaired or unpaired. In a paired experiment the different conditions or treatements areperformed on the same subject, while in an unpaired design different subjects undergodifferent treatments. This has an important impact on the statistics used, becausethe inter subject variability needs to be taken into account. A third subdivision canbe found in the number of groups to be compared. Comparing just 2 groups can bedone by using a simple statistical test, while performing the same statistical test on3 or more groups, leads to an increase of the type I error. This type I error is whenthe null hypothesis is rejected when it is true. Using adjusted statistical statisticsthis type I error is decreased to the significance level.

The experiments were performed in a paired design, because each gravity condition

27

3. Methodology

Table 3.3: Overview of the different statistics - Overview of the differentstatistics. Each statistic has a different application, depending on the design of theexperiment. The statistic can be designed for a paired or unpaired experiment, or for2 or more than 2 groups. The statistic could also be parametric of non-parametric.

Parametric Non-parametric2 groups 3+ groups 2 groups 3+ groups

paired Paired T-test Repeated measures Wilcoxon Friedmanone-way ANOVA Signed

rank testunpaired Unpaired T-test One-way ANOVA Kruskal-Wallis

is measured several times on the same person. A conclusion of the population wantsto be made in a parametric way and this for 4 gravity levels (0g - 0.16g - 0.38g - 1g).Due to these considerations a repeated measurements one-way ANOVA is chosen. Asignificance level of 5 % is used. To properly apply a repeated measurements one-wayANOVA, the assumption of normality and the assumption of constant variance needto be fulfilled. These assumptions are tested on the residuals, which are the differencebetween the values of one group and the mean following from the statistical analysis.The normality assumption is tested using the Lilliefors test, while the assumptionof constant variance is tested using the Levené’s test. The original data is for mostcases not normally distributed and a Box-Cox transformation is used to ensure thenormality. The Box-Cox transformation is a member of the family of power transformfunctions which create a rank-preserving transformation of the data. The formula ofthis Box-Cox transformation is determined by:

y(λ)i =

yλi −1λ , if λ 6= 0

log(yi), if λ = 0(3.8)

with λ a parameter maximizing the likelihood function, such that the transformeddata is maximal normal distributed as possible. For the rest of this text, all theassumptions for the repeated measurements one-way ANOVA are fulfilled, unlessmentioned otherwise.

3.4 Cardiovascular model

In many engineering domains, modelling leads to a better understanding of differenttechnical and biological processes. In this section a cardiovascular model to simulatethe cardiovascular response to parabolic flights is discussed. First the implementationof a model which is used as starting point is treated, secondly the changes made tothe gravity dependency of this cardiovascular model is discussed more in detail.

28

3.4. Cardiovascular model

3.4.1 Implementation

The cardiovascular system is characterized by its spatial distributed physical character-istics and its large temporal range of dynamic behavior. Modelling the cardiovascularsystem in the entire range of operation is technically infeasible. Therefore choicesmust be made regarding the spatial and temporal representation of the system.In literature, several models can be found, each with their own design and theirown specific aim. Some models predict the short-term cardiovascular response tohead-up tilt test [8, 14, 15], while others for example predict the long-term changesin the body water homeostasis under hormonal regulation [16]. The purpose of ourmodel is to simulate the transient, short-term, beat-to-beat cardiovascular responseto changing gravity during one parabola, which last less than 5 minutes. Internalprocesses with time constants larger than 5 minutes must not be incoorporated.The spatial resolution can also be limited because it is not necessary to known theblood pressure at each point in our body. As explained further on, there is need tohave certain spatial distinguish between several body parts to be able to implementsome gravity dependent mechanisms. For this reason a lumped-parameter modelis preferred to quasi-distributed models of the arterial and venous circulation [33].A lumped-parameter model describes a body part more in general with a smallnumber of parameters and unknowns, while a quasi-distributed model describes abody part on a much smaller scale, leading to a larger number of unknowns. Using alumped-parameter model has thus a large impact on the computational complexity.

One of the models fulfilling these temporal and spatial requirements is the one de-velopped by Thomas Heldt et al. [15]. This model, containing 21 compartments, wasused to simulate the cardiovascular response to gravitational stress during a tilt fromsupine to head-up position. Figure 3.9 gives an overview of the circuit representationof this lumped-parameter model. The arteries, veins and capillaries of the body aredivided in different compartments, which can be represented hemodynamically aselectrical equivalent circuits. The body is roughly divided in an arterial system (rightside of the figure) and a venous system (left side of the figure) with a right heartmodel, a pulmonary circulation model and a left heart model in between (middle ofthe figure). The arterial system, consisting of the thoracic aorta, abdominal aorta,ascending aorta and brachiocephalic arteries, transports the blood from the heart tothe systemic circulation. This systemic circulation is divided in four parts. These arethe upper body circulation, the renal circulation, the splanchnic circulation and thelower legs circulation. This division is necessary to take the gravity into account forthe different parts. The main part of the human blood flow passes through these foursystemic circulations. Therefore a further subdivision is not necessary and would becomputational too expensive. Passing through these systemic compartments, theblood is transported back to the heart via the venous system, consisting of abdominalveins and the superior and inferior vena cava. Figure 3.10 shows an enlargement ofone of such compartments. This electrical equivalent circuit is used to determinethe equations of the whole system. At each node the pressure is defined and usingthe capacitive and resistive laws, the flow through a resistor and capacitor can be

29

3. Methodology

Figure 3.9: Circuit representation of the lumped parameter model - Thebody is roughly divided in a arterial system (right side of the figure) and a venoussystem (left side of the figure) with in between the heart model and the pulmonarycirculation (middle of the figure). The systemic circulation is divided in 4 parts: theUpper Body circulation, the Renal circulation, the Splanchnic circulation, and theLeg circulation. Those systemic circulation compartements are connected with theRight and left heart model via the Thoracic Aorta, Abdominal Aorta/veins, superiorand inferior vena cava, Ascending aorta and Brachiocephalic Arteries. Figure taken

from [15]

30


Figure 3.10: Enlargement of circuit representation of one compartment -At each node the pressure can be determined and the flow through a resistor of thecompartement can be calculated using the pressure difference at two nodes. Thisgive rise to a coupled set of first order differential equations. Figure taken from [15]

calculated using the pressure difference between the nodes, as can be seen in thefollowing equations:

qn = Pn−1 − Pn + PhRn

qn+1 = Pn − Pn+1Rn+1

(3.9)

qc = d

dtVn = dVn

d(Pn − Pe)· ddt

(Pn − Pe)

Using the principle of conservation of mass, the combination of these three flowequations leads to one first order equation:

d

dtPn = Pn−1 − Pn + Ph

CnRn− Pn − Pn+1

CnRn+1+ d

dtPe (3.10)

One compartment is thus described by one first order equation, while the entirehemodynamical model is described by a set of coupled first-order differential equations.This coupled set of first order differential equations are solved numerically using afourth order Runge-Kutta scheme with variable step size.

Two pressure sources were included in the model as can be seen on figure 3.10.One is a pressure source acting on the inside of the vessel; this is called the hydrostaticpressure component Ph which dependens on the gravity. The other pressure sourceis one acting on the outside of the vessel and is called the external pressure Pe. Thelast one plays an important role in the capacity of blood volume storage in an arteryor vein. This external pressure is different for the different positions in the bodyand could be the intra-thoracic pressure, intra-abdominal pressure, intra-upper bodypressure and intra-legs pressure. In this model, only the intra-thoracic pressure willbe different from zero, but for example when the cardiovacular response of lower

31

3. Methodology

body negative pressure tests wants to be simulated, the intra-legs pressure needs tobe adapted.

The blood pressure and heart rate are under control of the parasympathic andsympathetic nervous systems. Two important mechanisms can be found in the bodyas guardians of the short-term blood pressure homeostasis [15]. One mechanismis situated at the high pressure side of the circulation and is called the arterialbaroreflex. The other, at the low pressure side of the circulation, is called thecardiopulmonary reflex but its role in the cardiovascular homeostasis regulation ishardly questioned. Both reflex mechanisms are implemented in a similar way asillustrated schematically in figure 3.11. Stretch receptors in the arterial wall of theaortic arch (arterial baroreflex) and in the vena cava (cardiopulmonary reflex) giverise to electrical signals transported via the afferent fibers to the medulla oblongata,which processes this pressure information. The response leads to a reciprocal effecton the two efferent limbs of the autonomic nervous system: the parasympatheticand sympathetic outflow. An increased signal of the parasympathetic modulationleads to a release of acetylcholine which acts on the heart by slowing it down.The symphathetic system leads to the release of norepinephrine which leads to anincrease of heart rate and cardiac contractility via β1-sympathetic receptors in cardiactissue, and it affects the arteriolar and venous tone via the α1-sympathetic receptorsfound in the muscular walls of arterioles and veins [15]. The control mechanismsare implemented as a set point controller where an error signal is produced as thedifference between the calculated pressure and the pre-defined set point pressure. Thiserror signal is translated to parasympathetic and sympathetic control signals via theconvolution with a transfer function, one for each signal (α1, β1 and parasympathetic)with their own specific timing parameters. The parasympathetic signal leads toa decrease of the heart rate, while the sympathetic signal has an increasing effecton the heart rate, cardiac contractility and arteriolar resistance and a decreasingeffect on the zero pressure filling volume of the veins. These effector mechanismsare also shown in figure 3.11. More information about the implementation of thiscardiovascular model can be found in the work of Thomas Heldt et al. [15].

3.4.2 Gravity dependency

The gravity affects the cardiovascular system in three different ways: a body fluidshift, change in intra-thoracic pressure and an interstitial blood volume loss. Beforestarting the discussion about the implementation of these processes, the profileof the gravitational constant during a parabola needs to be simplified. Using apiecewise linear approximation, the amount of parameters can be reduced to 11parameters as shown in figure 3.12. The parameters are the onset time of theparabola (t0), the transition time needed to increase or decrease the acceleration(∆t), the duration of the hypergravity (Thyper) and of the reduced gravity phase(Tmicro) and the magnitude of the gravitational constant of hypergravity (ghyper)and reduced gravity (gmicro). After averaging of all parabolas of the same gravitylevel, using the acceleration data from Novespace, one gravity profile is obtained

32


Figure 3.11: Arterial baroreflex and cardiopulmonary reflex mechanism- Stretch receptors in the arterial wall of the aortic arch and in the vena cava giverise to electrical signals transported via the afferent fibers to the medulla oblongatawhich will process this pressure information. A sympathetic outflow response leadsto a change in heart rate, cardiac contractility, arteriolar resistance and venoustone, while a parasympathetic outflow only influences the heart rate. The arterialreceptors measured the pressure at the aortic arch (∆PAA(t)) and at the carotidsinus artery (∆PCS(t)), while the cardiopulmonary receptors measured the right

arterial transmural pressure (∆PRA(t)). Figure taken from [15]

for Moon, Mars and zero gravity parabola. The averaging is done using 39 (3 x13) Moon parabolas, 36 (3 x 12) Mars parabolas and 18 (3 x 6) zero parabolas.Figure 3.13 shows these averaged gravity profiles together with their piecewise linearapproximation. The values for those 11 parameters are determined for each gravitylevel and are reported in table 3.4.

The gravitational stress during a parabolic flight leads to a shift of the body fluidin the hemodynamical system. This rapid volume redistribution is modelled at eachvascular segment by the inclusion of a hydrostatic pressure component, Ph, for whichthe gravity dependency is given by:

Ph = ρg(t)hsin(α) (3.11)

where g(t) is the gravitational constant changing during parabolic flights, h is theeffective length of the vascular compartment, ρ the density of blood and the angleα represents the orientation of the person with respect to the horizontal plane. Inthe work of Thomas Heldt et al. [15], the angle α changes during a head-up tilt test,while in our experiments this angle remains fixed to 90, which is the angle of the

33

3. Methodology

Figure 3.12: Characterisation of the simplified, piecewise linear functionof the gravitational constant - A finite amount of parameters can be used tocharacterize this simplified, piecewise linear function of the gravitational constant.The parameters were the onset time of the parabola (t0), the time needed to increaseor decrease the acceleration (∆t), the duration of the hypergravity (Thyper) and ofthe reduced gravity phase (Tmicro) and the magnitude of the gravitational constant

of hypergravity (ghyper) and reduced gravity (gmicro)

Figure 3.13: Simplified, piecewise linear function of the gravitationalconstant during the Moon, Mars and zero gravity parabolas - These aredetermined from the averaged acceleration for Moon (3x13 parabolas), Mars (3x12

parabolas) and zero gravity (3x6 parabolas)

34


Table 3.4: Parameters of piecewise linear gravity function - Overview ofthe values of the parameters.

Parameters of piecewise linear gravity functionZero g Moon g Mars g

Thyper1 (s) 13 12 10Tmicro (s) 21 23 28.5

Thyper2 (s) 14 13.5 8.5

∆ t1 (s) 5 5.5 8.5∆ t2 (s) 6.5 9 9∆ t3 (s) 5.5 6.5 8.5∆ t4 (s) 11.5 8 11.5

ghyper1 (g) 1.78 1.7 1.7gmicro (g) 0.01 0.16 0.381

ghyper2 (g) 1.58 1.6 1.65

subject with respect to the ground of the aeroplane when standing or sitting.

The term h is the effective length of a vascular compartment which representsthe effective hydrostatic height of that compartment. This length is chosen to behalf the anatomical length of the vascular segments. One consideration has to bemade according to the position of the persons. In our experiments the test subjectswhere sitting down and not standing. This has an impact on the effective length ofthe arterial and venous compartement of the legs. A part of the legs stays parallelto the ground and so this part does not contribute to the hydrostatic pressure. Thispart from from buttock to knee needs to be substract from the total anatomicallength of the leg arteries and veins, as used in the work of Thomas Heldt et al. [15].Using the anthropometrics coming from a study performed by the US army [34], thebuttock-knee length is estimated to be 61.68cm on average for men. Figure 3.14gives a graphical representation of this length.

Next to the rapid volume redistribution due to the occurence of changes inhydrostatic pressure Ph, there is a second mechanism influencing the blood volumedistribution. The gravitational disturbances influence the balance between hydrostaticand osmotic pressure gradients across capillary walls. During increasing gravity thisunbalance would lead to an increase of transcapillary fluid flow which results ina decrease of intravascular volume and vice versa during decreasing gravity. Thisbehaviour follows an exponential function and is researched in Hagan et al. [35]during head-up tilt tests. The exponential course can be described by a singleRC-circuit for each compartment and give rise to two extra parameters for eachcompartment, which leads to a computational more expensive model. Using theapproximated gravity profile, this problem could be simplified by calculating an

35

3. Methodology

Figure 3.14: Buttock-knee length as defined in [34] - The buttock-knee lengthis on average 61.68cm for men. Figure taken from [34]

analytical solution for the blood volume loss and flow, dividing these over the differentsystemic compartments. Figure 3.15 shows the RC-circuit model for calculating theintra-vascular volume loss and the transcapillary leakage flow. For each region ofchanging gravity the analytical solution is given in appendix B and is determined bytwo parameters, the time constant of the exponential function τ and the maximalinterstitial volume loss Vmax. Vmax can be interpreted as the maximal blood volumeloss when time goes to infinity. In the work of Thomas Heldt [15] the time constant τhas a constant value of 4.6 minutes, while Vmax is found to be linearly dependent onthe orientation angle α. In our case it is assumed that the Vmax is a linear functionof the magnitude of the gravitational stress. So the values where given by:

Vmax1 = 700ml . (ghyper1 − 1)

Vmax2 = 700ml . (gmicro − 1) (3.12)Vmax3 = 700ml . (ghyper2 − 1)

Figure 3.15: RC-circuit to model the interstitial blood volume loss - Asimple RC-circuit can be used to calculate the interstitial blood volume loss and

transcapillary blood flow analytically. Figure taken from [15]

Figure 3.16 shows the transcapillary blood flow and interstitial blood volumeloss as reaction to the changing gravity for zero, Moon and Mars parabolas. During

36


hypergravity phase a net influx of blood into the capillary wall can be observed(positive transcapillary flow), which results in a decrease of total blood volume ofabout 30ml at its maximum. During the reduced gravity phase, a net efflux of bloodinto the vessel (negative transcapillary flow) can be remarked, which results into anincrease of the total blood volume of about 30 ml at its minimum. This amount ofblood volume loss is small compared to the total blood volume (±0.6 % of TBV) dueto the large time constant of this process with respect of the timing of the changinggravity. So this process would not have a large impact on the cardiovascular modelbut it is still implemented because it is one of the mechanism at which the gravityhas an influence, in particulary when the duration of reduced gravity of hypergravityis enlarged, as is the case for example for astronauts during launch into space.

Figure 3.16: Transcapillary blood flow and interstitial blood volume loss- Transcapillary blood flow and interstitial blood volume loss as reaction to thechanging gravity. During hypergravity there is an increase of transcapillary flowinto the the muscular wall, leading to an increase of blood volume loss. Duringreduced gravity there is a negative transcapillary flow, leading to an decrease of the

interstitial blood volume loss.

A third component, depending on the gravity, is the intra-thoracic pressure (Pth),as showed during head-up tilt tests in studies of Mead and Gaensler [36] and Ferris etal. [37]. A drop of 3.5mmHg in intra-thoracic pressure is demonstrated in responseto a head-up tilt to 90. When extrapolating this data to changes in gravitationalconstant, this would lead to a drop of 3.5mmHg for a change of 1g. The intra-thoracicpressure is defined as in the following equation:

Pth = Pth0 − 3.5mmHg · g(t) (3.13)

where Pth0 is the intra-thoracic pressure during supine position or during reducedgravity and is estimated at -4mmHg. Figure 3.17 shows the intra-thoracic pressurechanges as response to the gravity changes. During hypergravity the intra-thoracicpressure changes to about -10mmHg, while during reduced gravity this pressure

37

3. Methodology

increases to a value between -5.3 and -4 mmHg, depending of the gravity level. Theintra-thoracic pressure has an influence on the return blood flow to the vena cavaand determined the pressure of the blood flow in the arteries and veins at the thorax.

Figure 3.17: Intra-thoracic pressure changes as response to the changinggravity - During hypergravity the intra-thoracic pressure changes to about -10mmHgwhile during reduced gravity this pressure increases to a value between -5.3 and -4

mmHg, depending on the gravity level

38

Chapter

4Results and discussion

In this chapter the obtained results from the statistical analysis are discussed andconclusions about the cardiovascular response in function of the different gravitylevels are presented. First the results for the blood pressure variability and secondlythe results for the heart rate variability are discussed, resulting from the time andfrequency domain measures. In a third section the validation of the cardiovascularmodel, together with additional results from the model, is explained more in detail.All boxplots are shown as the mean ± 2 x SD. An asterix above the boxplot indicatesa significant difference (significance level of 5%) with the normogravity (1g).

4.1 Blood pressure variabilityInstead of discussing the results for systogram and diastogram seperately, it wouldbe more interesting to discuss the results for the mean arterial pressure (Pmean)and the pulse pressure (PP), which were derived from the systolic and diastolicvalues as described in section 3.1.3. Figure 4.1.a and 4.1.b show the statistical resultsfor the mean Pmean and PP value as a function of the gravity. As can be seenthe mean arterial pressure increases with increasing gravity, except for the zerogravity parabolas. The Pmean is for all gravity levels significantly different from thenormogravity. The mean PP shows a decreasing trend but this trend is less distinctcompared to those of the mean Pmean. Figure 4.1.c and 4.1.d shows the boxplots forSD and RMSSD as a function of gravity. As mentioned before in section 3.2.2, theSD correlates with the total power of the autonomic modulation, while the RMSSDcorrelates with the measure for the high frequency component of this modulation,corresponding to the parasympathetic control. For Pmean it can be seen that thereis a clear non-linear decreasing trend for the SD, while also the RMSSD shows adecreasing trend, except for the Mars gravity. Thus concluded that there is an increaseof the autonomic modulation at reduced gravity compared to normogravity, whichcan be partially explained due to the increase of the parasympathetic modulation.

Figure 4.2.a and 4.2.b give the statistical results for the SD and RMSSD of thepulse pressure. For the SD again a decreasing trend can be remarked, but this isnot true for the RMSSD. The RMSSD for Moon and Mars gravity seems to be

39

4. Results and discussion

Figure 4.1: Mean Pmean and PP and SD and RMSSD Pmean - The leftplots shows the mean Pmean (plot a.) and mean PP (plot b.) as a function ofgravity. An increasing trend is found for the Pmean, while a decreasing trend can beremarked for the PP. The plots on the right shows the SD (plot c.) and RMSSD

(plot d.) of the Pmean, which show both a decreasing trend.

significantly different from those for zero and normogravity. This means that theparasympathetic modulation on the pulse pressure is low during Mars and Moongravity, notwithstanding the increase in total power of the autonomic modulation.This results in the conclusion that the sympathetic control has a large impact on theregulation of the pulse pressure.

The ratio between RMSSD and SD provides a quantification of sympathovagalbalance of the cardiovascular system. Figure 4.2.c and 4.2.d give the statisticalresults of this ratio for Pmean and PP. For Pmean this ratio is more or less constantfor the reduced gravity phases, however it is significantly lower compared to those ofthe normogravity level. There is a smaller proportion of the parasympathetic controlduring reduced gravity compared to the normogravity level. This means that thesympathetic control has a larger share in the modulation of the blood pressure. Theratio RMSSD/SD for PP shows an increasing trend, which means a larger relativeimportance of the parasympathetic control with increasing gravity.

In literature the blood pressure variability is moderately studied [5, 10] for zerogravity parabolas and those studies show a slightly increasing trend for mean arterialpressure and a decreasing trend for pulse pressure as a function of gravity. Ourresults, shown in figure 4.1, are similar. However, the mean blood pressure for thezero gravity does not correspond with the hypothesis of a monotonic trend, it istoo high compared with the blood pressure from Moon and Mars parabolas. Otherblood pressure variability indices, like SD and RMSSD of the mean arterial pressure,are less frequently studied during parabolic flights, compared with the heart ratevariability indices. Our results (figure 4.1 and 4.2) show a decrease of the total powerof the modulation (SD) and a decrease of the parasympathetic modulation (RMSSD)as a function of gravity, for the pulse pressure and the mean arterial pressure. The

40

4.2. Heart rate variability

Figure 4.2: SD and RMSSD of the PP and RMSSD/SD of Pmean andPP - Plot a. and plot b. give the statistical results for the SD and RMSSD of thePP, while plot c. and plot d. give the results for the ratio RMSSD/SD for Pmeanand PP, respectively. SD PP shows a decreasing trend, while for the RMSSD PP anonmonotonic trend is visible. However the ratio RMSSD/SD are statistical lower

during the reduced gravity, compared with the normogravity.

ratio of these two numbers (RMSSD/SD) gives an indication of the sympathovagalbalance. This ratio is statistically lower compared with the normogravity value, givean evidence that during reduced gravity there is a higher sympathetic predominance.For the zero gravity parabola a slightly higher RMSSD/SD is found, which declaresthe higher mean arterial pressure and pulse pressure.

The sympathovagal balance regulates the blood pressure and is important toprevent postflight orthostatic intolerance. One of the conditions associated withorthostatic intolerance is the orthostatic hypotension [38]. Due to an insufficientmodulation, the blood pressure cannot recover fast enough when going for examplefrom supine to standing position. Several studies [6, 7] found a correlation betweenthe sympathovagal imbalance and the occurrence of orthostatic intolerance afterperforming head-up-tilt tests with subjects with vasovagal syncope. These studiesfound an increase of the LF and HF component of the mean arterial blood pressurein healthy subjects after a 60 tilt test, resulting in an increased normalized HFcomponent. A tilt test from supine to standing position, has a similar cardiovasculareffect as going from reduced gravity to normogravity. The normalized HF componentcorrelates with the RMSSD/SD and therefore the results of those studies correspondwith the increased sympathetic predominance found in our study.

4.2 Heart rate variabilityFigure 4.3 shows the statistical results for the mean, SD, RMSSD and RMSSD/SDfor the RR intervals. The heart rate is the reciprocal of the RR interval and therefore

41


the results for the RR intervals make it possible to draw conclusions about theheart rate. As can be seen on figure 4.3.a, the mean RR interval increases, or theheart rate decreases, with increasing gravity. However this change in heart rate isnot significant on the 5% level. It can also be noticed that the mean RR intervalduring the Moon parabolas shows a lower tendency, resulting in a higher heart rate.One possible explanation of this higher heart rate, is the higer mental stress of thesubjects, because the Moon parabolas were the first parabolas flown. Excitementduring parabolic flights has found to have a large impact on the heart rate, as beenshown in a study of Beckers et al.[9]. In that study every subject participated in atleast two flights. A large inter-flight variation has been demonstrated by comparingthe mean RR interval of the same parabola (first, middle and last 5 parabolas)between the two flights. Therefore in that study only the data from the second flightwere used and that is why only the last 6 parabolas of each gravity level were usedin our study.

The results of the SD (4.3.b) and the RMSSD (4.3.c) show a decreasing trend.Thus there is an increase of the total power of the autonomic modulation when inreduced gravity, which can be partially explained by an increase in parasympathicmodulation (as can be seen in the increase of RMSSD when in reduced gravity).The ratio RMSSD/SD gives again a comparison of the relative importance betweensympathetic and parasympathetic modulation, as shown in figure 4.3.d. During Moongravity and Mars gravity the relative importance of the parasympathetic modulationis smaller compared to those from normogravity and zero gravity, resulting in theslightly lower RR interval at these gravities.

Figure 4.3: Statistical results of the time domain measures (mean - SD -RMSSD - RMSSD/SD) of the RR intervals as a function of gravity - Plota. and plot b. give the statistical results for the mean and SD of the RR interval,while plot c. and plot d. show the results for the RMSSD and the ratio RMSSD/SDfor the RR intervals. No trend in RR interval can be found, while in RMSSD andSD a clear decreasing trend can be observed. Note also the lower RR interval, and

thus higher heart rate, during the Moon parabolas

42

4.2. Heart rate variability

In previous studies from the Laboratory Experimental Cardiology at KU Leuven[9, 11, 17] the HRV shows a clear trend as a function of the gravity (0g - 1g - 1.8g)for the standig position of the subjects during parabolic flights. In the study fromBeckers et al. [9] a decreasing trend is found in the mean RR interval and in theRMSSD of the RR interval as a function of gravity. In our study the RMSSD followsa similar decreasing trend, however our results for the mean RR interval shows anonmonotonic trend. This contradictory result can be partially explained by theexcitement state of the subjects. Some subjects were more excited compared toothers, resulting in a different progress of the mean RR interval. Figure 4.4 showsthe mean RR interval for the different subjects as a function of gravity. Two subjectsclearly show a decreasing trend of the mean RR interval (subject 3 and 4), whileothers shows no trend (subject 1) or even a increasing trend (person 2 and 5). Thestatistical results combines all these different trends, resulting in no clear trend inthe mean value of RR interval.

Figure 4.4: Mean RR for each subject as a function gravity level - Twosubjects clearly shows a decreasing trend of the mean RR interval (subject 3 and 4),while others shows no trend (subject 1) or even a increasing trend (person 2 and 5).

The analysis is also performed in the frequency domain, using frequency domain

43


measures as described in section 3.2.3. Figure 4.5 shows the statistical results ofthe frequency measures. Figure 4.5.a and 4.5.b shows the high frequency (HF) andlow frequency (LF) components of the RR intervals. As can be seen there is adecreasing trend of these components towards increasing gravity. As mentionedbefore the HF component correlates with the fast parasympathetic modulation, whilethe LF component is an indication of the slow symphathetic modulation with vagalinfluences. As shown, both modulations increase when going into reduced gravity.Using the normalized frequency components, a conclusion can be drawn about therelative importance of the parasympathetic control (using HFnu) or sympatheticcontrol (using LFnu). Figure 4.5.c shows the normalized HF component (HFnu).The normalized LF component can be interpreted using the same plot by taking intoaccount that the sum of the two normalized components is 1 (LFnu = 1 - HFnu). Anonmonotonic trend is visible on the HFnu component, but there is a small decreaseof this component during the Moon parabolas. This means that during Moon gravitylevel there is a sympathetic predominance, leading to an increase of the heart rate.This is also a conclusion which is made when interpreting the results of the meanRR intervals and can also be stated using the results for the ratio between LF andHF components. The latter is shown in figure 4.5.d. As can be seen there is a slightincrease of the ratio during Moon gravity, which means again a higher sympatheticcontrol compared to the parasympathetic control.

Figure 4.5: Statistical results of the frequency domain measures (HF -LF - HFnu - HF/LF) of the RR intervals as a function of gravity - Plot a.and plot b. give the statistical results for the HF and LF components of the RRinterval, while plot c. and plot d. give the results for the HFnu and the ratio LF/HFfor the RR intervals. Although the monotonic trend in LF and HF component, there

is no trend found in the normalized HFnu and the ratio LF/HF.

In the studies from Seps and Verheyden [11, 17] the frequency measures of HRVwere examined as a function of gravity (0g - 1g - 1.8g). In these studies, meanRR interval and the normalized HFnu component show a decreasing trend, whilethe ratio LF/HF and LFnu follows an increasing trend. Reassessing our obtained

44


results, as shown in figure 4.5, no statistical trend for LF/HF, HFnu and LFnu isfound. Due to this lack of difference in sympathovagal balance, the mean RR intervalshows a nonmonotonic trend. Others factors, for example the mental stress, play animportant role on the modulation.

Although there is no clear trend in the mean RR interval and sympathovagalbalance, there is a certain trend visible in the power of the sympathetic and parasym-pathetic modulation, characterized via the LF and HF components. This givesevidence that the parasympathetic and sympathetic modulation always interacts, oras citated from a study from Malliani [31]: "they interact just like flexor and extensortones or excitatory and inhibitory cardiovascular reflexes". Only the ratio betweenthose two frequency measures, gives valuable information about the predominance ofvagal or sympathetic activity.

One should be careful while interpreting the results of the frequency domainmeasures. The LF component is measured between the lowest measurable frequency(0.05 Hz = 1/20s) and the upper limit of 0.15 Hz. As mentioned before in section3.2.3, spectral leakage due to side lobes of the Hamming window leads a value forLF which can be larger than the actual value. It is therefore important to comparethe results of the frequency domain measures with the results of the time domainmeasures (RMSSD and SD).

4.3 Cardiovascular modelIn this section, first the validation of the cardiovascular model is discussed, followedby a presentation and discussion of additional results from the model.

4.3.1 Model validation

For each subject and each gravity level a beat-to-beat systolic and diastolic arterialpressure (SAP and DAP), heart rate (HR), stroke volume (SV) and central venouspressure (CVP) are simulated with a sampling frequency of 2 Hz. The model is notonly adapted for the height of the subject, via the length of the vascular segments, butalso a change in arterial set point and nominal heart rate is implemented. These arecalculated from the preflight baseline measurements in sitting position and is neededto obtain a corret absolute value of pressure and heart rate during the normogravity.A small optimization algorithm was written to optimize the end-systolic complianceof the left ventricle, the arterial set point and nominal heart rate to come up withthe exact pulse pressure, heart rate and systolic and diastolic pressure for phase 1 ofa certain gravity parabola for one subject. The end-systolic compliance of the leftventricle has a large influence on the pulse pressure and therefore it is needed toobtain the correct systolic and diastolic values. However the optimization resulted invalues for the end-systolic compliance out of the reasonable physiological range, asdefined in [15]. An end-systolic compliance which is too large or small, will result ina system which reacts slower than compared with the measured data. There can beconcluded that optimization of only several parameters is not sufficient enough to

45


obtain a specific model for one subject. This means that at this moment it is notpossible to simulate perfectly the cardiovascular response of one person. In order toproperly compare the simulation results with the measured data, the mean value ofthe simulated data is set to those from the measured data.

Figure 4.6 shows the simulated SAP, DAP and HR in bold for subject 1 duringa zero gravity parabola. The thin curve is the average measured systolic pressure,calculated from the last six noise-free parabolas. The vertical lines indicate thedifferent phases of the parabola. A large similarity between the measured andsimulated heart rate and blood pressure can be noticed. The course of blood pressureand heart rate during the parabola can be explained physiologically. At the onset ofhypergravity a hydrostatic pressure gradient will force the blood to the legs, with adrop of the stroke volume and a drop of the blood pressure as a consequence. Witha certain the delay the cardiovascular system will response to it by increasing theblood pressure to another set point value. Before it reaches its set point value, thetransition from hypergravity to reduced gravity forces the blood to flow towards theupper body, resulting in an increase of the pressure at heart level. Again, with acertain delay the body reacts to this change, leading to a lowering of pressure andwith a small overshoot, explained by the bent in the curve in the middle, it almostreaches its steady state value. The transition from reduced gravity to hypergravityleads intially to a pressure drop, followed by an increase of the pressure. Fromhypergravity to normogravity the blood pressure restored its normal value, after atransient period. The changes in heart rate can be explained in a similar way, withfirst an increase in heart rate during hypergravity, followed by a decrease duringreduced gravity and a recovery to a steady state value.

Figure 4.6: Example of the simulated blood pressure and heart ratetogether with the measured data - The left figure shows the systolic (upperline) and diastolic blood pressure (lower line), while the right figure shows the heartrate for the simulation (bold line) and the measured data (thin line). They are theresults for subject 1 during a zero gravity parabola. Remark the good resemblance

between the measured and simulated data.

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To validate the simulation results, a comparison is made with the measured datafrom the parabolic flights. Figure 4.7 shows the error between the simulated and themeasured data for mean arterial pressure (Pmean), pulse pressure (PP) and heartrate (HR) for each gravity level. The mean error with its 95% confidence band isshown, calculated by averaging it for the different parabolas and subjects. The dataof subject 4 is rejected due to the limited complete parabolas as consequence of thenoisy ECG signal, as be summarized in appendix A. For each gravity level only thelast six noise-free parabolas were used, to eliminate the effect of mental excitement.As can be seen the error for Pmean is approximately in the range of [-20,15] mmHg,while the PP shows an error of about [-15,10] mmHg. The heart rate has an error inthe range of [-10,20] beats per minute. The plot gives an indication that the errordepends on the timing within the parabola, resulting in a not randomly distributederror. Therefore it can be concluded that the simulation results do not correspondwell with the measured data. As mentioned before this is due to the lack of optimizedmodel parameters for each subject. That is why an optimization algorithm shouldbe written to optimize all model parameters to one subject.

Figure 4.7: Mean error of mean arterial pressure (Pmean), pulse pressure(PP) and heart rate (HR) for each gravity level - The mean error is shownwith its 95 % confidence interval. The magnitude of the errors largely depends on

the timing within the parabola, resulting in a not randomly distributed error

The purpose of the model is not to simulate perfectly the cardiovascular responseof one person, but to simulate the dynamics of our cardiovascular system, as responseto gravity changes. Signals with almost the same dynamics, have a similar autocor-relation function (ACF). Figure 4.8 shows the ACF’s of the Pmean, PP and HRfor the different gravity levels, calculated from averaging the ACF for the different

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subjects. The thick line displays the mean ACF, while the 95% confidence intervalis indiacted with a thin line. The red curve is the ACF for the simulated data,while the blue curve is those for the measured data. As can be seen, the locationsof the large negative and positive peaks correspond well with the locations of thecorresponding peaks of the measured ACF. Only the amplitude of the peaks are toolarge, meaning that the amplitude of the simulated response is larger compared tothe measured data. However the mean arterial blood pressure and the heart rateshow extra dynamics during Moon and Mars parabolas, which result in an extra peakbetween a time lag of 20 and 40 seconds. This can be explained more in detail whencomparing heart rate and mean arterial blood pressure for one subject during Marsparabola, like shown in figure 4.9 for subject 1. As explained before the simulatedblood pressure shows a pressure drop due to the fluid shift during the transition tohypergravity. But this pressure drop is not found in the measured data. Also therecovery of the pressure to the steady state value occurs much faster in the simulateddata than in the measured data. This pressure drop and faster recovery will giverise to the extra peak in the ACF. For the heart rate the overshoot when going tothe steady state value is larger in the measured data compared with the simulateddata and there is a large drop of heart rate at the end of the reduced gravity phase.These two things together result in the extra peak of the ACF. The duration of thereduced gravity phase of the Mars parabolas are the largest, compared to those fromMoon and zero gravity parabolas. This is one reason why the recovery to the steadystate value take place, giving rise to those extra dynamics. However, due to the goodcorrespondence of the ACF, the model is validated good enough to simulate themain components of the heart rate and blood pressure response. When discussingadditional results, the shortcomings of this model must be kept in mind.

4.3.2 Model results

The heart rate and blood pressure variability of the simulated data can be assessedin a similar way as been used for the measured data. Statistical analysis cannot beperformed using only four simulated responses per gravity level, but to be able todraw proper conclusions, only the change of the biomedical signal corresponding thepreceding normogravity phase 1 is analysed. Therefore a decrease of the mean valuewith respect to the normogravity phase, results in a negative value. Figure 4.10shows the results for mean arterial pressure (Pmean) and pulse pressure (PP), usingthe time domain measures to characterize the biomedical signals, as described insection 3.2. It can be seen that mean Pmean shows a slightly decreasing trend as afunction of gravity till Moon gravity, but increasing towards Mars and normogravity,which has a change of zero. The mean PP shows a clear decreasing trend. The ratioRMSSD/SD shows an increasing trend for the Pmean, while it shows a decreasingtrend for PP. Comparing these figures with the results from the statistical analysis,showed in figures 4.1 and 4.2 of this section, shows similar results for the meanPmean, mean PP and RMSSD/SD Pmean, but the results for RMSSD/SD PP donot correspond. The higher mean blood pressure at zero gravity was thought tobe influenced by other internal processes, like stress, but these simulated results

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Figure 4.8: Autocorrelation function (ACF) of Pmean, PP and HR foreach gravity level - The mean ACF is shown in thick line, while its 95 % confidenceinterval is indicated in thin line. The red curve is the ACF for the simulated data,while those for the measured data is displayed in blue. There is a good resemblanceof the ACF’s, besides the larger amplitude of the peaks and the extra dynamics

arising during the Moon and Mars parabolas.

Figure 4.9: Example of the simulated blood pressure and heart rateduring Mars gravity for subject 1 - The left figure shows the systolic (upperline) and diastolic blood pressure (lower line), while the right figure shows the heartrate for the simulation (bold line) and the measured data (thin line). These are theresults for subject 1 during a Mars parabola. Remark the extra dynamics due tothe faster reaching of the steady state value and due to the extra pressure drop and

heart rate reduction.

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invalidated this. The blood pressure is not able to reach the steady state value in theshort period of the reduced gravity, meaning that the mean is of a transient signal.This leads to a calculation of mean value which is largely influenced by the durationand position of the segment used to calculate this mean value. A 20 seconds intervalin the middle of the reduced gravity phase is used for the calculation of the indices,as mentioned in section 3.2.1. Because the duration of the reduced gravity phase ofa zero gravity parabola is smaller compared to the other gravities, the calculationof the mean value is influenced by the hypergravity phase. Figure 4.11 illustratesthis principle for subject 1. The figure shows the mean arterial pressure duringzero gravity parabola (left plot) and Moon gravity parabola (right plot). The blackvertical lines indicates the start and stop times of the segment used to calculated themean value, while the horizontal black line represents the mean value. As can beseen due to the high blood pressure included in the first seconds of the segment forthe zero gravity parabola, the mean value for this parabola is higher compared tothose from Moon parabola. This confirmed the principle found in a study of Liu etal. [5]. They found that the mean arterial pressure reached a maximum of about 2-4seconds after the beginning of the microgravity, and is thus included in the segmentof calculation for the zero gravity.

Figure 4.10: Mean and RMSSD/SD of the mean arterial pressure(Pmean) and the pulse pressure (PP) for the simulated data - Plot a.shows the mean Pmean and plot b. shows RMSSD/SD of the Pmean. Plot c. andplot d. shows the same time domain measures for PP. Remark the decreasing trendfor mean Pmean and PP and for RMSSD/SD of PP, while the RMSSD/SD Pmean

shows an increasing trend.

Figure 4.12 shows the results for the time and frequency domain measures of theheart rate. The mean heart rate is lower compared to normogravity and decreasesfurther towards decreasing gravity. The change in heart rate is limited; it decreasesmaximal 6 beats/min with respect to those from normogravity. The ratio RMSSD/SDshows a nonmonotonic trend, however a small dip can be found at Moon gravity,

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Figure 4.11: Comparison of the mean arterial pressure (Pmean) for zeroand moon gravity parabola - Left plot shows the Pmean during a zero gravityparabola, while the right plot shows the Pmean during a Moon parabola. The blackvertical lines indicates the start and stop times of the segment used to calculated themean value, while the horizontal black line is the calculated mean value. Due to theinclusion of the high blood pressure in the first seconds of the segment for the zerogravity parabola, the mean value for this parabola is higher compared to those from

Moon parabola.

which was also found in the statistical results in figure 4.3. There seems to be quite adifference in sympathovagal balance between the different gravity levels, which is notmonotonically increasing or decreasing. The high frequency (HF) component of thesimulated heart rate response shows a decreasing trend which is highly non-linear,which was also found in the results from the measured data, as shown in figure 4.5.The ratio LF/HF shows an increasing trend. Except for the Moon gravity, wherethe stress plays a crucial role, these results correspond well to the results from themeasured data.

Besides the calculation of blood pressure and heart rate, the model makes itpossible to have an accurate estimation of the stroke volume. Multiplying the strokevolume (SV, in ml) with the heart rate (HR, in beats/min) gives an estimation ofthe cardiac output (CO, in ml/min). Figure 4.13 gives an example of the progress ofSV, HR and CO during a zero gravity parabola for subject 1. When entering thehypergravity phase the SV decreases due to reduced venous return as consequenceof the hydrostatic pressure gradient, while during reduced gravity the SV increases.Notwithstanding the reciprocal behaviour of HR, the CO follows the same trend as

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Figure 4.12: Mean, RMSSD/SD, HF and LF/HF of the heart rate (HR)for the simulated data - The plot a. shows the mean HR and the plot b. showsthe RMSSD/SD, while plot c. gives the HF component and plot d. the ratio LF/HF.A monotonic increasing trend in mean HR and LF/HF can be remarked, while theRMSSD/SD shows a nonmonotonic trend. HF shows a highly non-linear decreasing

trend. Remark the small dip in RMSSD/SD during the moon gravity.

the SV. Again using time domain measures, the variability of SV and CO can becharacterized, as shown in figure 4.14. It is found that both, SV and CO, decreaseas a function of gravity. Similar results were also found in a study from Liu et al.[5] for standing subjects during zero gravity parabolas. The RMSSD/SD shows forboth a decreasing trend, meaning that the sympathovagal regulation of SV and COis largely parasympathetic dominated when in reduced gravity.

An advantage of using a cardiovascular model is getting access to parameters of theinternal functioning. The regulation of blood pressure and heart rate is performed viaparasympathetic and sympathetic modulation of the cardiac contractility, vascularresistances and the venous tone. Figure 4.15 shows the changes in end-systoliccompliance of the left and right ventricle, while figure 4.16 shows the changes inperipheral arteriolar resistances and the venous zero pressure filling volume (ZPFV) ofthe four systemic circulation compartments for subject 1 during zero gravity parabola.As shown before, the SV drops due to the hydrostatic pressure gradient when enteringthe hypergravity phase. To counteract this volume change, the compliance of theleft and right ventricle decreases to maintain the stroke volume. This also has animpact on the pulse pressure. During the reduced gravity phase the opposite istrue. To counteract for the drop in blood pressure when entering hypergravity, thevascular resistance increases as can be seen on figure 4.16. The hydrostatic pressuregradient forces the blood to pool in the legs during the hypergravity. To counteractthis pooling of blood the body reacts by decreasing the zero pressure filling volumeof the veins, which is the volume in a vein when there is no pressure acting on thevessel wall.

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Figure 4.13: Example of stroke volume (SV), heart rate (HR) and cardiacoutput (CO) during one parabola - From top to bottom: the SV, the HR andthe CO for subject 1 during a zero gravity parabola. During hypergravity the SVdecreases, while in reduced gravity the SV increases. Notwithstanding the reciprocal

behaviour of HR, the CO follows the same trend as the SV.

Figure 4.14: Mean and RMSSD/SD of the stroke volume (SV) and thecardiac output (CO) for the simulated data - Plot a. shows the mean SV andplot b. the RMSSD/SD of the SV. Plot c. and d. shows the same time domainmeasures for CO. Remark that the mean SV and CO decreases with increasing

gravity. Also the RMSSD/SD of both signals shows a decreasing trend.

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Figure 4.15: End-systolic compliance of left and right ventricle during oneparabola - The compliance is a measure of the cardiac contractility. To counteractfor a drop of the SV, the compliance decreases during hypergravity and increasesduring reduced gravity. The red asterix indicates the starting point of changes in

this parameter.

Figure 4.16: Arteriolar resistances and zero pressure filling volumes(ZPFV) of the four systemic circulation compartements during oneparabola - Left figure shows the arteriolar resistances, right figure shows the zeropressure filling volumes. During hypergravity the resistances increase and duringreduced gravity the opposite is true. The zero pressure filling volumes decreaseduring hypergravity and increase during reduced gravity. The red asterix indicates

the starting point of changes in this parameter.

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In the cardiovascular model it is implemented that those three effector mechanismsare under sympathetic control, and thus not under the parasympathetic control. Thesympathetic signal is different from the parasympathetic signal due to a convolutionwith a different transfer function, which has a large delay to program the slowerresponse of the sympathetic modulation. This explains the delay between the onsetof the parabola and the start of the reaction of our body. On figures 4.15 and 4.16this delay is indicated with a red asterix. For the change in end-systolic complianceand arteriolar resistance this delay is 4 seconds, while the delay for the zero pressurefilling volume is 7.5 seconds. The delay of the parasympathetic modulation of theheart rate is about 1 second. The lag of the reaction of our cardiovascular system tothe gravity, explains the drop in blood pressure at the transistion to hypergravity.

This delay of our cardiovascular model is implemented as a constant for eachgravity level. But in reality it can be that the delay depends on the gravity levelor on the alert status of the subjects. The subjects were warned by the pilots ofthe airplane when the hypergravity phases started and ended, and this gives thepossibility to prepare for the parabola. This means that there are other importantfactors influencing the cardiovascular system besides the gravity dependent processes.These extra factors are not implemented in the simulation model and this can thusexplain the difference between measured and simulated data. To get an idea of thedelay of our cardiovascular system in the measured data, the delay between themeasured and simulated data is calculated as the maximum of the cross correlationfunction (CCF). Knowing the delay of the simulated model, it is possible to get anidea of the delay of the measured data. Before calculation of the CCF, the measureddata is filtered with a Wiener filter to eliminate the noise. The transfer function ofthis filter is given by:

W (ω) = Sd(ω)Sd(ω) + Sη(ω) (4.1)

where Sd(ω) represent the power spectral density (PSD) of the model, which isdetermined by the simulated data, and Sη(ω) is the PSD of the noise, estimated fromthe first 45 seconds of the measured data. The maximum of the CCF in an intervalof [-12.5, 12.5] seconds is taken as the delay between the measured and simulateddata. Figure 4.17 shows the statistical results of this delay for mean arterial pressureand heart rate after a repeated measurements ANOVA to compensate for the intersubject variability. As can be seen the delay for heart rate and blood pressure isnegative, meaning that the measured cardiovascular response leads the simulatedresponse. The introduced delay in the model is thus too large, compared to whatis seen in reality. Figure 4.17 shows also that the delay increases as a function ofgravity for the mean arterial pressure, while the delay for the heart rate is almostconstant, except for Moon gravity. This last phenomenon can again be explained dueto the mental stress of the subjects, leading to a higher heart rate. During the firstparabolas flown, they were preparing themselves faster for the upcoming parabolacompared to the last parabolas.

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The absolute values of the delay for the blood pressure are between -2 and -4seconds. Together with the delays implemented in the cardiovascular model thisgives a delay of 0 and 2 seconds from the beginning of the parabola for the resistanceand the compliance. The delay values for the heart rate are situated around the-4 and -5 seconds, and together with the delay of the model, this leads to a totaldelay of 0 or -1 seconds starting from the onset of the parabola. About 1 secondbefore the parabola starts, our cardiovascuar system is preparing for the upcominghypergravity phase, by starting to increase the heart rate accordingly. This givesthus an evidence that the cardiovascular system is influenced by other factors, whichare not implemented in the cardiovascular model.

Figure 4.17: Statistical results for the delay between simulated and mea-sured mean arterial blood pressure and heart rate. - The delay is calculatedas the maximum of the cross correlation function between filtered measured dataand simulated data. The delays are all negative, meaning that the measured data

leads the simulated data.

This is one of the few studies where a cardiovascular model is used to simulatethe cardiovascular behaviour during parabolic flights. Many models were made tosimulate the transient behaviour of head-up tilt test [8, 14, 15], lower body negativepressure or head-down bedrest. The model of Thomas Heldt et al. [15] is usedas starting point and changes were made to the gravity dependent processes, asdescribed in section 3.4. The model response is validated with the measured datafrom the parabolic flights and showed a good correspondence. But one dissimilaritycan be found at the transition to hypergravity. The fluid shift towards the lowerextremities lowers the venous return and results in a pressure drop at the heartlevel, but which is not found in the measured data. A possible explanation is the

56


opportunity to prepare our cardiovascular system to the parabolic flight, as suggestedin previous paragraph.

A second explanation is a possible disparity between the measured finger pressureand the simulated inter-arterial pressure at the heart level. During experiments thearm was bent such that the hand is at heart level. Due to this bent in de arm theblood flow in the arm will be different, compared to those in the thorax. Figure 4.18gives an schematical overview of the blood flow during the transition to hypergravity.A hydrostatic pressure gradient will induce a fluid shift towards the legs. The bloodvolume of the upper arm shifts to the elbow, and the same is true for the bloodvolume of the forearm. Due to a saturation of the blood volume at the elbow thepressure drop and the finger tip is limited.

A third explanation is found in a study of van Heusden et al. [14]. A similar modelis used but with the incorporation of the viscoelastic properties of the veins, called thestress-relaxation. Stress-relaxation refers to the intrinsic ability of the vascular wallsto contract slowly when the pressure falls and to stretch slowly when the pressurerises [14]. They have demonstrated for head-up tilt test that with incorporation ofthis model, the intial dip of the pressure is not shown in the simulated blood pressure.However it is a quite slow process, while the changes in gravity were faster duringthe parabolic flights.

Figure 4.18: Schemetic representation of the blood flow in the bent armduring transition to hypergravity - Due to a hydrostatic pressure gradient theblood volume of the upper arm shifts to the elbow, and the same is true for theblood volume of the forearm. Due to a saturation of the blood volume at the elbow

the pressure drop at the finger tip is limited.

The adaptation of the model to a subject is limited by changing the arterialand nominal heart rate setpoint, and adapting the vascular lengths of the differentcompartments for the height of the person. In order to perfectly predict the bloodpressure and heart rate, more parameters should be optimized. This should be doneby using an optimization algorithm.

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The model was intentionally made for simulation of cardiovascular response ofhead-up tilt tests in standing position. Our experiments were performed on subjectsin sitting position and to implement the change form standing to sitting position,the length of buttock to knee is substracted from the length of leg veins and arteries.The change to sitting position can also have an influence on other parameters whichwere not modelled. For example some vessels could be blocked or narrowed whileseated in the chair, thus increasing the vascular resistance and lowering the zeropressure filling volume of the veins. In the future this can be verified by optimizingthis parameter for a certain response of a subject.

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Chapter

5Conclusions and future work

Heart rate variability and blood pressure variability during different gravity conditionswere examined in this study. This was done via time and frequency domain analysis,as well as via modelling of the cardiovascular response to parabolic flights. Amonotonic relation between the indices and the gravity was hypothesized. For bloodpressure variability a monotonic relation as a function of gravity for the mean arterialblood pressure was found, however the mean blood pressure for zero gravity deviatedfrom this monotonic relation. Via the simulation results of the model, it was proventhat this deviation is a consequence of the transient behaviour of the blood pressureand the influence of the preceding hypergravity. The mean heart rate showed anonmonotonic trend, due to the different progress of the mean heart rate for eachsubject. This showed that there were others factors influencing the heart rate, forexample the excitement state of the subjects during the first parabola flown.

It was also found that both the sympathetic and parasympathetic modulationof heart rate and blood pressure decreases with increasing gravity, confirming thefact that the parasympathetic and sympathetic modulation interacts. Only theratio between these two modulations reveals information about the sympathovagalbalance. During reduced gravity the modulation of blood pressure was found tobe significantly sympathetic predominant compared to the normogravity. Also thehigher heart rate during Moon parabolas was explained via an increased sympatheticpredominance of the modulation. This study showed a good correspondence betweenthe time and frequency domain measures, leading to a justification of the calculationmethods.

A cardiovascular model was implemented to simulate the cardiovascular responseto gravity changes during parabolic flights, started from a pre-existing model for head-up tilt tests. This led to more insight in the internal functioning of the cardiovascularsystem. The results showed an increase of the arteriolar resistance, a decrease ofend-systolic compliance of the left and right ventricle of the heart and a decrease ofthe venous zero pressure filling volume during hypergravity. The opposite was foundduring reduced gravity. These effector mechanisms were implemented with a delayedsympathetic modulation, however a smaller delay or even a negative delay was foundin the measured data. This led to the conclusion that the cardiovascular system was

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5. Conclusions and future work

able to prepared itself to the oncoming parabolas. This gave an indication that thecardiovascular system is influenced by other factors, which are not implemented inthe cardiovascular model.

Parabolic flights are one of the ground-based possibilities to simulate differentgravity conditions, leading to reduced hydrostatic pressure gradients, however only ina window of about 20 seconds of reduced gravity. The small duration of this reducedgravity phase is a large limitation. In this time window the cardiovascular systemis modulated by the parasympathetic modulation, but it can not reach a steadystate value due to the slower sympathetic response and therefore only a transientcardiovascular response was measured. This has implications for the calculation andinterpretation of the time and frequency domain measures. It was proven that thesemeasures were largely determined by the duration and position of the window withinthe reduced gravity phase. The frequency domain measures should be interpretedcarefully due to spectral leakage of the windowing operation. Further researchshould reveal new measures, able to characterize the transient behaviour within smallduration windows, by using for example nonlinear measures.

Although the cardiovascular model described well the dynamics of the HRVand BPV, there was found an initial pressure dip in the simulated response whichwas not seen in the measured data. Further research coud reveal the cause of thisextra dip. Comparing the inter-arterial pressure, measured via a catheder, with thesimultaneous measured finger pressure, could reveal a possible disparity betweenthose two measurements, caused by the bent arm. These experiments could also beused to came up with a transfer function transforming the inter-arterial pressureto the finger pressure. This could be implemented in the model such that it ispossible to simulate the finger pressure, making direct comparison with measureddata possible. Another explanation for the missing of the initial pressure dip, is theablity to prepare the cardiovascular system on the oncoming parabola. To eliminatethis preparation, the subjects should close the eyes and ears, such that they cannothear or see the announcement of the parabola by the pilots.

The model could be used to simulate the cardiovascular response, specific for onesubject. An optimizitation program is required to determine the parameters of themodel, such that the error between simulated and measured response is minimizedfor one subject. By means of the values of these parameters, it would be possible todetermine the risk of fainting for the subjects. Therefore more experiments have tobe performed with a control group and subjects with vasovagal syncope to identify arelation between the parameters and the occurence of the vasovagal syncope.

A large limitation of the cardiovascular model is that not all mechanisms wereimplemented, such as the respiratoy sinus arrhythmia or the influence of stresshormones. It is known that the influence of respiration on the heart rate has animportant impact on the cardiovascular system. Including the different processeswould lead to a more refined model. More experiments are needed, as for examplethe experiment investigating the change in hormonal distrubution as reaction to the

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stress, which is performed during the 56th ESA Parabolic Flight Campaign [39] inMay 2012.

There should also be investigated if there are no other processes depending on thegravity. This can be done by performing more experiments on intermediate gravitylevels and determined the optimal parameters for different subjects. The value of theparameters can be investigated as function of gravity, taken into account the intersubject variability via statistics. When a parameter show a trend as a function ofgravity, it can be concluded that this parameter probably has to change with gravityand thus a new gravity dependent process needs to be implemented.

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Appendices

63

Appendix

ANumber of reliable segments

Due to noise, some segments could not be used. Table A.1 gives an overview ofthe reliable segements for the blood pressure and ECG signal. It is arranged persubject (1 -6), per gravity level (Moon - Mars - Zero) and per phase (1 - 5). Thecolumn ’All’ represents the number of noise free parabolas. As can seen there is nodata used from subject 5 because he became sick during the second parabola. Alsosubject 3 became sick during parabola number 18 and 19, resulting in 2 missing Marsparabolas. Subject 4 had an really noisy ECG, resulting in a huge loss of informationfor the ECG-signal.

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A. Number of reliable segments

Table A.1: Number of reliable segments for blood pressure and ECG -Overview of the number of usable segments per phase, per gravity level and per

subject. The column ’All’ sums up the number of noise free parabolas.

Blood pressure ECGMoon

1 2 3 4 5 All 1 2 3 4 5 AllSubject 1 13 13 13 12 11 10 13 13 13 13 13 13Subject 2 13 13 13 13 13 13 13 13 13 13 12 12Subject 3 13 13 13 13 13 13 13 13 13 13 13 13Subject 4 13 13 13 13 13 13 11 13 12 11 11 7Subject 5 0 0 0 0 0 0 0 0 0 0 0 0Subject 6 13 13 13 13 13 13 12 13 10 12 13 10

Mars1 2 3 4 5 All 1 2 3 4 5 All

Subject 1 12 12 12 12 12 12 12 12 12 12 12 12Subject 2 12 12 12 12 12 12 12 12 12 12 12 12Subject 3 10 10 10 10 10 10 10 10 10 10 10 10Subject 4 12 12 12 12 12 12 7 9 10 12 10 3Subject 5 0 0 0 0 0 0 0 0 0 0 0 0Subject 6 12 12 12 12 11 11 12 12 12 12 12 12

Zero1 2 3 4 5 All 1 2 3 4 5 All

Subject 1 6 6 6 6 6 6 6 6 6 6 6 6Subject 2 6 6 6 6 6 6 6 6 6 6 6 6Subject 3 6 6 6 6 6 6 6 6 6 6 6 6Subject 4 6 6 6 6 6 6 4 5 4 3 4 2Subject 5 0 0 0 0 0 0 0 0 0 0 0 0Subject 6 5 6 6 6 6 5 6 6 4 5 6 4

66

Appendix

BInterstitial blood volume loss

The interstitial blood volume loss can be calculated as an analytical solution of asimple RC-circuit as shown in figure B.1. The interstitial volume loss Vi is calculatedfrom the first order differential equation of this circuit:

RCd

dtVi(t) + Vi(t) = Vh(t) (B.1)

with Vh(t) the hydrostatic volume component, calculated from the hydrostaticpressure component. An expression for the transcapillary flow is given by:

qloss = Vh(t)− Vi(t)RC

(B.2)

Figure B.1: RC-circuit to model the interstitial blood volume loss - A sim-ple RC-circuit is used to calculate the interstitial blood volume loss and transcapillary

blood flow analytically. Figure taken from [15]

Using the simplified gravity profile, an analytical solution can be calculatedfor the different regions as indicated in figure B.2. First a general solution of thehomogeneous differential equation is computed, while in a second step the particularsolution is calculated using the method of variation of coefficients. The analyticalsolution is the summation of the homogeneous solution and the particular solution:

Vi(t) = Vih(t) + Vip(t) (B.3)

The homogenous equation and the general solution Vih(t) of the homogeneousequation are defined as:

67

B. Interstitial blood volume loss

Figure B.2: Simplified gravity profile - The simplified gravity profile makes itpossible to calculate an analytical solution for the different regions, indicated in thefigure as encircled numbers. The initial condition of each region is chosen as such

that there is a continuous progress of blood volume loss.

τd

dtVi(t) + Vi(t) = 0 (B.4)

Vih(t) = Ae−tτ (B.5)

where τ is the time constant of the exponential determined as the product of Rand C, and A is an integrating constant determined by the intial conditons.

The particular solution Vip(t) is determined using the mathematical method ofvariation of coefficients, and it is performed by substituting

Vip(t) = A(t)e−tτ (B.6)

into the differential equation B.1 and determing A(t) for this particular case.This can be done for each region of changing gravity, with specification of the correctright hand side (RHS) of the differential equation B.1. For example for region 1, theRHS is give by:

V(1)h = Vmax1t

∆t1(B.7)

with Vmax1 the maximal blood volume loss according to the hypegravity leveland ∆t1 the duration of the transition from normogravity to hypergravity. Theparameter Vmax can be interpreted as the maximal volume loss when time goes toinfinity. The superscript (1) refers to the region for which this solution is applicable.

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After substitution into the differential equation an expression for the derivative ofA(t) is obtained:

V(1)h = Vmax1t

∆t1= τA′(t)e−

tτ − τA(t)e−

tτ

1τ

+A(t)e−tτ

= τA′(t)e−tτ (B.8)

⇒ A′(t) = Vmax1t

∆t1etτ (B.9)

After integration by parts an expression of A(t) is found:

A(t) = Vmax1∆t1

(1− τ)etτ (B.10)

Combining the general and particular solution and taking the initial conditioninto account (V (1)

i (0) = 0) an expression for V (1)i (t) is obtained:

V(1)i (t) = Vih(t) + V

(1)ip (t)

= Vmax1∆t1

τe−tτ + Vmax1

∆t1(t− τ) (B.11)

Using the relation between the blood flow and volume loss, as stated in equationB.2, and expression of the blood flow loss can be found:

q(1)loss = Vmax1

∆t1(1− e−

tτ ) (B.12)

For region 3, 5 and 7 the solution can be calculated in a similar way, using adifferent expression for Vh(t). The initial conditions are chosen as such that theblood volume progress will be continuous. This means that the blood volume loss atthe end of previous region determines the blood volume loss at the beginning of thefollowing region.

The solution for a constant gravity regions (region 2, 4, 6 and 8) could easily becalculated using the formulas from the preceding regions. During constant gravitythere is an exponential decay of the blood flow. For example for region 2 this leadsto the following equation:

q(2)loss(t) = q

(1)loss(∆t1)e−

t−∆t1τ = Vmax1

∆t1(1− e−

∆t1τ )e−

t−∆t1τ (B.13)

Using the rearranged form of equation B.2, an expression of the blood volumeloss can be calculated:

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B. Interstitial blood volume loss

V(2)i (t) = Vh − τq

(2)loss(t) = Vmax1 − τq(2)

loss(t)

= Vmax1(1− τ

∆t1(1− e−

∆t1τ )e−

t−∆t1τ ) (B.14)

The time constant τ is found to be 4.6 minutes, while the parameter Vmax isfound to be linearly dependent on the tilt angle in the work of Thomas Heldt and histeam [15]. Extrapolating this data to our experiments leads to a linearly dependencyof Vmax as function of the gravitational constant:

Vmax1 = 700ml . (ghyper1 − 1)

Vmax2 = 700ml . (gmicro − 1) (B.15)Vmax3 = 700ml . (ghyper2 − 1)

The implementation of these equations is validated by simulating a simplestanding-supine-standing test with a period of 15 minutes for each experiment.Figure B.3 shows the simulated blood volume loss and transcapillary flow afterthis experiment, simulated using the fact that 1g corresponds to standing, while 0gcorresponds to supine position. The exponential decay is clearly visible in the bloodvolume and flow due to the large duration of the standing and supine periods. Ascan be seen the maximal blood volume loss will not exceed the 700 ml, due to thedefinition of Vmax . The simulated results resembles well the data as described in astudy of Hagan et al.[35], as can be seen in figure B.4. It shows the percent changein plasma volume after similar standing-supine experiments. The plasma volume isdetermined by taking blood samples on several time instants during the experiment.Due to the good resemblance, the implementation of these equations is validated.

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Figure B.3: Simulated interstitial blood volume and transcapillary flowduring stand-supine test - Interstitial blood volume and transcapillary flowsimulated after 15 minutes of standing, 15 minutes of supine and 15 minutes of

standing.

Figure B.4: Change in percentage of the plasma volume after standing-supine experiments - The same exponential decay can be seen as in the simulated

blood volume loss. Figure taken from [35]

71

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