Design, Test and Measurements

Master Thesis

A System for Multi-Modal Assessment ofCardiaovascular Parameters-

Design, Test and Measurements

Name: Ankit MalhotraExaminer: Prof. Dr. rer. nat. Martin Ryschka

Second Examiner: Steffen Kaufmann, M. Eng.

Date of Submission: 30th August 2013

Abstract

AuthorAnkit Malhotra

Title of ThesisA System for Multi-Modal Assessment of Body Parameters-Design, Test and Meas-urements

KeywordsElectrocardiography (ECG), Photoplethysmography (PPG), Impedance Cardiogram(ICG), Wavelet, MATLAB, Pulse Arrival Time (PAT), In-Ear , Pressure measurements,Field Programmable Gate Array (FPGA), Analog Front End (AFE)

AbstractPatient monitoring is very vital for the well being of a patient as it provides valu-able information for the prognosis and help the health givers to provide a medicaltherapy. Multi-modal assessment of the different parameters related to the cardi-ovascular system is highly crucial as it provides with the complete condition of theheart, the arterial system and gives a lot more information than the normal ECG andPPG.This work introduces a novel multi-modal vital-parameter measurement system.The system is able to accurately and simultaneously measure a single channelElectrocardiography (ECG), four Photoplethysmography (PPG) signals, the pres-sure in both ears, as well as the broad-band, time resolved thorax bioimpedance.The designed measurement system is based on a System on Chip (SoC) Field-Programmable Gate Array (FPGA) (LFXP2-17) and a Texas instrument analog frontend (ADS1298). Furthermore the system is in compliance with the IEC60601-1 safetyrequirements and allows excitation currents from 125 µA to 5 mA. The employed im-pedance demodulation is Fast Fourier Transform (FFT) based. Moreover the ECG,PPG, ICG and pressure measurements have a temporal resolution of about 1 ms.The measured time resolved conductivity curve in connection with ECG, PPG andpressure signals are digitally processed and analysed in Mathworks MATLAB. Es-timates of the Stroke Volume (SV), the Cardiac Output (CO), PAT, Pulse Wave Velo-city (PWV), Heart Rate Variability (HRV) and respiration rate are estimated from thedifferent measurements.

Kurzzusammenfassung

AutorAnkit Malhotra

Titel der MasterarbeitEin System für die Erfassung Multi-Modaler Vitalparameter - Systemdesign, Veri-fikation und erste Messungen

SchlüsselwörterElektrokardiogramm (EKG), Photoplethysmographie (PPG), Impedanzkar-diographie (IKG), Wavelet, MATLAB, Pulse-Arrival-Time, Aussenohr-Druckmessungen, Field Programmable Gate Array (FPGA), Analoges Frontend

KurzzusammenfassungPatienten Monitoring ist ein wichtiges Werkzeug in Anamnese und Prognose umeine optimale Therapie auszuwählen und zu überwachen. Multimodale Erfassungvon verschiedenen Vitalparametern, welche im Zusammenhang mit dem Herzkre-islaufsystem stehen, ist dabei besonders wichtig, da sie eine umfassende Analysedes Zustandes des Herzkreislaufssystems ermöglicht und über die simple Erfassungeines EKG oder PPGs hinausgeht.Diese Masterarbeit beschreibt ein neues multimodales Vitalparameter-Messsystem.Das entwickelte Messsystem ist in der Lage genaue und synchrone Messungen vonverschiedenen Parametern durchzuführen. Die Parameter sind Einkanal-EKG, vierPPG, zwei Aussenohr-Drücke, sowie die zeitlich aufgelöste thorakale Breitband-bioimpedanz.Das entwickelte Messsystem basiert auf einem System on Chip (SoC) FPGA (LFXP2-17) in Kombination mit einem Texas Instruments Analog Front End (AFE, ADS1298).Darüber hinaus erfüllt das System die Anforderungen an die IEC60601-1 Sicherheit-sregularien. Das System erlaubt Impedanzmessströme von 125 µA bis zu 5 mA undbesitzt eine FFT-basierte Impedanzmodulation. Die zeitliche Auflösung ist bei allenMessmodalitäten grösser als 1 ms.Die gemessenen, zeitaufgelösten Messungen werden digital verarbeitet und mith-ilfe von Mathworks MATLAB analysiert. Basierend auf den Messungen wird dasSchlagvolumen, das Herzminutenvolumen, die Puls-Arrival-Time, die Pulswel-lengeschwindigkeit, die Herzratenvariabilität sowie die Atemfrequenz ermittelt.

Acknowledgment

Feeling gratitude and not expressing it is like wrapping a present and not giving it.

William Arthur Ward

Electronics is a science which integrates many small components to produce an usefuldevice. Without these different components and their proper functioning it is impossibleto invent anything. So each and everyone mentioned here are analogies to the real worldcomponents, without them a successful completion would have been, just a mere dream.

I am really indebted to Prof. Dr. Martin Ryschka for being the ’power supply’ for thethesis. Without your immense support, advice and wisdom nothing would have worked.I wanna thank Steffen the ’FPGA’ for being a friend rather than being a mentor. Moreoverfor providing an extraordinary healthy environment and sharing the know how with me.Then I wanna thank Gunther and Felix the ’ADC and DAC’ whose support provided strengthto take my ideas to the next domain. And for their keen interest in my work. Specially Iwant to thank Gunther for making the PPG sensors when the CLK speed was too high.

Moreover I want to thank my parents ’PCB board’ who let me nurture and provided aplatform full of opportunities. And their unending love and faith in me. Bedrock of mylife. My closet advisor. Not just the wind beneath my wings, but the wings themselves.

I really want to thank Jenny and Matthias my ’Heat sinks’, you guys always took the heataway and made things look so easy. I really cherish our endless coffee sessions, lunchbreaks and many things more......

Christina(Tinee) the ’Capcitor’ my best critic. Who always charged me up with energy whenI needed it most. Thanks for being there whenever I wanted, specially our long Skype talksin the night, when we worked in parallel.

Rest I wanna thank Emely the ’LED’, your wise ideas and your small concerns produce awavelength which beacons through my mind. Then I wanna thank Seb, Ivan and Zeljko forbeing the ’Resistors’ and ’Inductors’ without you guys, it was also impossible. And finallyall my best buddies back home Mohit, Akshay, Anuraj and Kshtij.......

To wrap it up I would like to thank all my beloved friends. Our friendship was bornin a very odd way, but I would not have expected otherwise, as all of us are odd in ourown beautifully weird world. You portray the symptoms of my shortcomings, and thecelebration of my virtues. I have become a better man because of the mirror you hold upfor me. Thank you and I love you.

Table of Contents

Table of Contents

Acronyms vii

1. Introduction 11.1. Future of Patient Monitoring Devices . . . . . . . . . . . . . . . . . . . . . . . 31.2. Motivation and Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2. Materials and Methods 82.1. Cardiac and Vascular System . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2. Electrocardiography (ECG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3. Photoplethysmography (PPG) . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3.1. Pulse Wave Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 142.3.2. Morphological Analysis of PPG Signals . . . . . . . . . . . . . . . . . 172.3.3. Relation to the Pressure Curve . . . . . . . . . . . . . . . . . . . . . . 19

2.4. In-Ear Measurement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.5. Bioimpedance and Impedance Cardiogram (ICG) . . . . . . . . . . . . . . . 26

2.5.1. Impedance Plethysmography . . . . . . . . . . . . . . . . . . . . . . . 282.6. Stroke Volume (SV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.7. Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.7.1. The Discrete Wavelet Transform . . . . . . . . . . . . . . . . . . . . . 33

3. System Design 363.1. Requirement Analysis of the System . . . . . . . . . . . . . . . . . . . . . . . 363.2. Block Diagram of the Developed System . . . . . . . . . . . . . . . . . . . . . 373.3. Ensuring Medical Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.4. Analog Part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.4.1. ECG Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.4.2. PPG Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.4.3. In-Ear Module/Headphone Interface . . . . . . . . . . . . . . . . . . 483.4.4. Impedance Cardiometry Module . . . . . . . . . . . . . . . . . . . . . 51

3.5. Digital Part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.5.1. Analog Front End (AFE) . . . . . . . . . . . . . . . . . . . . . . . . . . 553.5.2. Field Programmable Gate Array (FPGA) . . . . . . . . . . . . . . . . 56

3.6. Power Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.7. Layout of the Printed Circuit Board (PCB) . . . . . . . . . . . . . . . . . . . . 59

3.7.1. Part Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.7.2. Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4. Software and Testing 644.1. Principle Firmware Buildup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.2. Wavelets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664.3. MATLAB Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

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Table of Contents

4.4. Requirement Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5. Results 715.1. Development of In-Ear Measurement System . . . . . . . . . . . . . . . . . . 715.2. Standard Measurement Setup and Study Outline . . . . . . . . . . . . . . . . 725.3. Signal Acquisition and Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 735.4. Stroke Volume and Cardiac Output . . . . . . . . . . . . . . . . . . . . . . . . 785.5. Pulse Arrival Time (PAT) and Pulse Wave Velocity (PWV) . . . . . . . . . . . 805.6. Heart Rate Variability (HRV) . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

6. Summary and Outlook 87

References xiv

A. CD Content xv

Declaration for the Master’s Thesis xvi

vi

Acronyms

Acronyms

AC Alternating Current

ADC Analog to Digital Converter

AFE Analog Front End

AV Atrioventricular

CAGR Compound Annual Growth Rate

CCU Cardiac Care Unit

CO Cardiac Output

DAC Digital to Analog Converter

DC Direct Current

DDS Direct Digital Synthesis

DRL Driven Right Leg

DSP Digital Signal Processing

DVP Digital Volume Pulse

DWT Discrete Wavelet Transform

ECG Electrocardiography

EIT Electrical Impedance Tomography

EMI Electromagnetic Interference

FFT Fast Fourier Transform

FIFO First In First Out

FIR Finite Impulse Response

FPGA Field-Programmable Gate Array

HPF High Pass Filter

HRV Heart Rate Variability

I/O Input/Output

IC Integrated Circuit

ICG Impedance Cardiogram

ICU Intensive Care Unit

IEC International Electrotechnical Commission

IIR Infinite Impulse Response

IMB Impedance Meter Board

INA Instrumentation Amplifier

IR Infrared

JTAG Joint Test Action Group

LT Linear Technology

LUT Look-up Table

LVET Left Ventricular Ejection Time

MSPS Mega Samples Per Second

PAT Pulse Arrival Time

PC Personal Computer

PCB Printed Circuit Board

PEP Pre-Ejection Period

PFU Programmable Function Units

PGA Programmable Gain Amplifier

PPG Photoplethysmography

PTT Pulse Transient Time

PWV Pulse Wave Velocity

RAM Random Access Memory

SA Sinoatrial

SNR Signal to Noise Ratio

SoC System on Chip

SPI Serial Peripheral Interface

SpO2 Saturation of Peripheral Oxygen

STFT Short Time Fourier Transform

SV Stroke Volume

vii

Acronyms

TI Texas Instruments

USB Universal Serial Bus

VHDL Very High speed Hardware DescriptionLanguage

viii

1. Introduction

Health is a large word. It embraces not the body only, but the mind and spirit as well;... and not

today’s pain or pleasure alone, but the whole being and outlook of a man.

James H. West

Patient monitoring is a vital part in operating rooms, emergency rooms, intensive care and

critical care units. Additionally, it proves to be invaluable for respiratory therapy, recovery

rooms, out-patient care, transport, radiology, catheterization laboratory, gastroenterology

departments, ambulatory, home, sleep screening applications as well as simple check-ups

or for diagnosis.

But nowadays health professionals need more than a II Lead ECG and Saturation of Peri-

pheral Oxygen (SpO2) monitoring in the patient monitoring devices. Medical practice

demands an array of vital parameters both real-time and trending to better understand

the physiological condition of the patient. A typical multiparameter device looks for ECG,

SpO2, CO, hemoglobin, temperature, bioimpedance, EEG, non-invasive blood pressure,

invasive blood pressure, respiration, and implanted pacemaker activity.

Around the world engineers and clinicians are working to make devices which could

fit different parameters for specific functions such as in ambulatory, Intensive Care Unit

(ICU), Cardiac Care Unit (CCU), post-anesthesia recovery, various labs and treatment suites,

and general ward recovery, as well as offsite consulting specialists and homes. Each de-

partment has different demands depending upon the patient condition but the medical

data generated should be compatible and connectable to known medical format.

Modern medicine is interested in the patient’s cardiovascular and pulmonary functions,

as well as cerebro-neurological response, and the body’s ever-changing homeostasis or

faltering condition. The medical device industry demands ever-improving technologies in

capturing immediate condition, change, and rates of change. They call for improvements

in accuracy and quality, size reduction, as well as technological advances in data capture,

transmission, and storage. Image 1.1 below shows a modern patient monitoring set-up.

1

1. Introduction

Figure 1.1: Modern Patient Monitoring Setup1

Interventional caregivers of every level are asking for that next technology in vital sign

monitoring-confirming effective overall resuscitation efforts, especially adequate compres-

sions and oxygenated blood flow to the brain.

Additionally, they want improved human factors that reduce labour costs and errors. The

ultimate package must respond to the current landscape of oversight agencies and regu-

latory bodies, plus significant engineering challenges. The aim of this thesis is to integrate

some of the widely available parameters such as ECG and PPG with some new parameters

such as In-Ear signal, cardiac output and blood perfusion non-invasively through imped-

ance measurement.

The need for a new physiological site is to provide continuous monitoring to patients out-

side the hospital setup. As due to scarcity of resources it is very difficult to monitor all

patients in clinics, hence there is a large demand for offsite monitoring. But this offsite

monitoring is difficult to achieve, as sensors are attached on body parts which obstruct

1http://www.te.com

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1. Introduction

motion. Therefore there is a demand for getting the same relevant physiological data from

different sites. And In-Ear measurement setup proves to be a very promising site.

The SV depends upon heart size, contraction of the heart muscles and end-diastolic volume,

thus provides a window to analyze the overall activity of the heart. Hence there is an im-

mense need to monitor it specially after the heart surgery. Till now the gold standards (Fick

principle, thermodilution and Ultrasound methods) are not at all continuous. Hence there

is a need for measuring these parameters continuously.

1.1. Future of Patient Monitoring Devices

New technologies in various fields of engineering has fueled the development of new med-

ical devices world wide. Scientists and researches of completely different backgrounds

work together to improve the health science. Any new development in any field is being

effortlessly migrated to the health sector, may it be sophisticated chips, FPGAs or cloud

computing. All the new developments known or unknown will play a major role and

change the outlook of how we perceive physiological monitoring.

Latest development is not limited to modification of existing technology such as improve-

ment of resolution, portability and safety of the patient, but various researchers are challen-

ging the historical trend by introducing completely new concepts. These new researchers

not only provide a new kind of data for the clinicians and health providers, but also make

the decision making for a particular physiological condition more predictable.

One of the recent development in the field of respiratory monitoring is credited to Dräger

which introduced PulmoVista 500 based on Electrical Impedance Tomography (EIT) is cap-

able to show regional distribution of lung ventilation directly at bedside [10]. EIT techno-

logy was first introduced in year 1978 by John G. Webster, but the first medical realization

came in year 1984 due to work of David C. Barber and Brian H. Brown. The electric im-

pedance potentially has many applications, one of which is to calculate the CO of the heart

which is further evaluated by this current master thesis.

Furthermore the miniaturization in each and every sphere of technology has empowered

engineers with sophisticated tools, which are not only on small scale but have added ad-

vantage of accuracy as well as cost effectiveness. For example by the various Integrated

3

1. Introduction

Circuit (IC) manufacturing companies have introduced chips which are smarter and highly

efficient. Moreover the technical documentation and online tools provided by these com-

panies have made engineering designs much simpler and have boosted the research on

much larger level. These innovations have made the patient monitoring to be a ubiquitous

phenomenon, and a further boom due to better wireless and data management technology.

Many new sites which are pretty much different from conventional physiological sites are

a recent development. This not only has changed tactics for sensor technology but also

has increased the patient comfort level. For example Assurance Biosense 1 introduced a

biomedical device in which the sensor is placed on nasal ala (fleshy portion of nostril) and

provides cardiorespiratory parameters. Even the site behind the ear has been introduced

as an integrated wearable vital sign monitor [14]. This location is ideal for both physiolo-

gical and mechanical reasons. And the results from various such studies have shown a

promising trend in vital parameter monitoring which could be performed without a fancy

setup.

One more area of research which is growing in popularity is to measure everything non-

invasively. Each invasive intervention in the body produces an orifice in our inherent

defence mechanisms which can cause an onset of infection. This is a disaster in terms of

health of the patient as well as the cost structure of the provided health care as it leads to

prolonged stays at hospitals. These shortcomings can be easily avoided by non-invasive

applications. Non-invasiveness shortens the time required for health procedures and you

don’t need a professional health giver to do a daunting and sometime dangerous task. For

example non-invasive bilirubin measuring for neonatal and non-invasive arterial blood

pressure measuring are considerably quicker to use.

There is enormous physical and mental stress for the patients. This can be avoided and can

lead to speedy and pain free recovery. Hence non-invasive methods can help to prevent

traumatization of the patient. For example in monitoring of invasive arterial blood pres-

sure complications such as haemorrhages or haematomas can be prevented. Many new

devices are being introduced in the market which have non-invasive capabilities and still

produce promising results, for example CO and SV analysis by ICG [27]. On the same base

1http://www.xhale.com

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1. Introduction

another system was developed by Osypka Medical GmbH 1, Berlin, Germany and much

research is done to make it compatible for an end product [40].

Research analysts around the world have always predicted a growth in the patient mon-

itoring irrespective of the developed or the developing countries. For example the U.S.

market for advanced patient monitoring systems has grown from $ 3.9 billion in 2007 to

$ 8.9 billion in 2011 and the total U.S. patient monitoring market is expected to grow at a

compound annual growth rate of 4.1% by 2018.

This growth is not only valid to developed countries, but there had been an increase in

medical spending on both micro and macro level for emerging economics of the world. As

well as this applies to the BRIC2 countries, which have developed at an astonishing rate

of 20% Compound Annual Growth Rate (CAGR) in the period of 2006 to 2011. The major

contributors for such a growth is the rising household income, urbanization as well as an

aspiration for better health care facilities.

Furthermore there has been rise in remote patient monitoring trends. A new analysis

from Frost and Sullivan3 on data management systems for Patient Monitoring Markets

in Europe finds that the markets earned revenues of $126.8 millions in 2009 and estimates

this to reach $161.4 millions in 2013. The markets covered in this research are ECG, sleep

and respiratory, neurological and anesthesia monitoring.

1.2. Motivation and Aim

At this point it is very important to give an overview about the thesis. The task is to

build a system which has four modules namely PPG, ECG, In-Ear and ICG [21] in a single

Printed Circuit Board (PCB). Moreover the system also makes use of a very efficient and

powerful AFE (ADS1298,Texas Instruments) which is capable of converting analog signals

to digital signals at the same instant of time. The control unit of the system is based on

the FPGA(LFXP2-17-QN204C) from Lattice Semiconductors and the interface between the

PCB and the computer is done via a Universal Serial Bus (USB) 2.0 interface chip. So the

system has the capability to do simultaneous recording of data and transfers it to a Personal

1http://www.osypkamed.com2Brazil, Russia, India, China3http://www.frost.com/

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1. Introduction

Computer (PC) for further analysis. At this point it is also crucial to understand that the

system is not real time as the data is not computed with any time constraints but with

high resolution and accuracy, and then transmitted to the PC for further analysis. So to

conclude this short overview- the system is in-time but not real time. All the modules and

their principles are explained in the coming chapter.

As mentioned earlier patient monitoring is a very early invention. But due to its coal-and-

ice nature towards patient’s health it is an ever evolving research field. This thesis aims at

developing and investigating a novel non-invasive system for cardiac impedance measure-

ments. Moreover to develop a new technique for measurements of pressure pulses inside

the ear. From now on this would be termed as in In-Ear measurement system and would

be explained in the upcoming chapters. But for providing an overview the reader can refer

to [20]. The simultaneous capturing of data is a very integral part of the system because it

provides us with a window to estimate more parameters such as PAT, PWV, SV, CO, HRV

etc. The analysis of a single waveform just provides information related to the particular

physiological measurement. But simultaneous measurements lead to more information.

One of the aims is to relate the two basic parameters of the body called ECG and PPG with

the cardiac impedance and In-Ear measurements. ECG is significant because it is easy to

detect and acts as a reference for each and every other signal. This is because it is the

grass-root signal for every other volume and pressure related waveform in cardiovascu-

lar system. Furthermore to investigate PWV along the arterial tree through the developed

hardware. Specifically the aims of the thesis are:

Thesis aim 1: To develop a hardware and software capable of multi modal assessment of

different body parameters simultaneously, which would in turn help in evaluating differ-

ent parameters such as ECG, PPG, In-Ear measurements and cardiac impedance measure-

ments. This is explained in Chapter 3.

Thesis aim 2: To setup the principle and verify the In-Ear measurement system. Correl-

ating the signal with PPG and taking measurements in both the ears simultaneously and

comparing them.

Thesis aim 3: To modify and extend the existing hardware of the impedance meter V1.01

so to conduct cardiac impedance measurements.

6

1. Introduction

Thesis aim 4: To correlate all the measurements such as ECG, PPG, In-Ear signal and

cardiac impedance measurement and to discover significant sites on the signals relating

to normal physiological condition. The basics for doing such an analysis is explained in

Chapter 2.

Thesis aim 5: To calculate PAT values at different sites especially inside ear via head-

phones and pressure measurements. This is explained in Chapter 5.

Outline of the thesis The current thesis is organized as it follows:

Chapter 1 provides an introduction to the thesis by describing its motivation and major

research aims.

Chapter 2 focuses on the compilation of the background information required for the un-

derstanding of the research works performed within the thesis. In particular this chapter

reviews physiology concepts related to cardiovascular system, Ear, ECG, PPG and bioim-

pedance. Concept of PAT and the morphology of PPG is also introduced.

Chapter 3 introduces hardware development of all the different modules with schematics

and simulations in Cadence OrCAD1 and LT spice2 respectively.

Chapter 4 introduces the concepts for the development of software related to FPGA and

analysis of the various biomedical signals through Mathworks MATLAB3.

Chapter 5 introduces the different waveforms from different modules and comparison

of some moduleties at different physiological sites. And representing results obtained

through PAT, PWV, number of respirations, HRV, SV and CO.

Chapter 6 concludes this thesis by summarizing the performed research, underlining the

main limitations and setting guidelines for the future work.

1http://www.cadence.com2http://www.linear.com/designtools/software/3http://www.mathworks.de/products/matlab/

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2. Materials and Methods

In this chapter the prime motto is to discuss all the underlining principles and ideas behind

each and every modality and how their inter-relationship plays a key role for unfolding

new concepts and ideas which could take the patient monitoring to a new level.

2.1. Cardiac and Vascular System

Heart is a hollow organ made up of muscles and is responsible for pumping blood through-

out the body. The average human resting heart rate is 75 times per minute [52]. The heart

consists of four chambers, two superior atria and two inferior ventricles. The atria are

responsible for receiving the blood whereas ventricles are responsible for discharging the

blood throughout the body.

c.Left Atrium

d.Left Ventricle

a.Right Atrium

b.Right Ventricle

a.

b.

c.

d.

4.Pulmonary capaillaries of

right lung

9.Systematic capillaries of head and upper limbs

Pulmonary capillaries of left

lung

Oxygen-rich bloodOxygen-poor blood

KEY:

3.Pulmonary trunk and pulmonary arteries

2.Right ventricle

1. Right atrium(deoxygenated blood)

Superior vena cava

Inferior vena cava

Coronory sinus

10

5.Pulmonary veins(oxygenated blood)

6.Left atrium

7.Left ventricles

Aorta and systematic

arteries

Figure 2.1: Path of blood flow through the heart and the body based on [52]

8


Deoxygenated blood flows through the superior vena cava into the right atrium and then is

pumped into the right ventricle via tricuspid valve. Then this blood is pumped out through

the pulmonary artery to the lungs, where the deoxygenated blood changes to oxygenated

blood. From this point the oxygenated blood enters again into the heart through left atrium

where it is pumped through the mitral valve into left ventricles before leaving through the

aortic valve to aorta. Aorta is responsible to supply oxygenated blood to the whole body.

The complete process is illustrated in Figure 2.1.

A cardiac cycle is a term referring to all or any of the events related to the flow or blood

pressure that occurs from the beginning of one heartbeat to the beginning of the next [52].

The complete cardiac cycle is divided into five major stages. The first stage is called At-

rial Systole in which atrium of heart contracts and pumps the blood into ventricles. The

second stage is isovolumic ventricular contraction as the name states the volume is constant,

the ventricle of the heart starts to contract and AV and semilunar valves close. The third

stage is ventricular ejection when the ventricles are contracting and semilunar valves are

open. The fourth stage is Isovolumic ventricular relaxation again the volume is constant the

ventricles relax and there is no flow of blood into the ventricles. Moreover the semilunar

valves close due to pressure of blood in aorta. The final stage is Diastole in which semilunar

valve present in heart closes and artrioventricular (AV) valve is open and the heart is in re-

laxation stage. All the phases of the cardiac cycle are shown in figure 2.2 below.

Figure 2.2: Phases of Cardiac Cycle [52].

9


2.2. Electrocardiography (ECG)

As the heart is composed of specialized cardiac muscle fibers called auto-rhythmic fibers

hence, are self excitable. They generate action potentials which in turn trigger heart poten-

tial. They act as pacemaker, setting the rhythm of electrical excitation that causes contrac-

tion of the heart. Moreover they form the cardiac conduction system. This system makes

sure that cardiac chambers are simulated to contract in a coordinated manner. Cardiac ex-

citation usually begins at Sinoatrial (SA) node and responsible for contraction of the right

and left atrium.

By conducting along arterial muscle fibres, the action potential reaches Atrioventricular

(AV) node. Here the action potential slows down because the difference in cell structures.

This delay provides time for the atria to empty their blood into the ventricles. From AV

node the action potential enters bundle of His or AV bundle. After propagating along the

AV bundle, the action potential enters both the right and left bundle branches. The bundle

branches extend through the interventricular septum towards the apex of the heart. Finally

Purkinje fibers rapidly conduct the action potential from the apex of the heart through the

whole ventricles.

As action potentials propagate through the heart, they generate more electric potentials

that can be detected at the surface of the body. This measurement is termed as an ECG and

the instrument used for recording of these electrical signals is called electrocardiogram.

Electrodes are generally placed on limbs and on the chest and various recordings are made.

Usually there is a twelve electrode configuration in clinical practice. By comparing these

records a lot of information about the heart could be determined. The typical heart ECG

(II lead) is shown in Figure 2.3.

10


P

R

Q

S

T

U

P-Q Segement

P-Q interval

S-T Segement

S-T interval

QRS interval

Ventricular contractionAtrial contraction

Figure 2.3: ECG Signal

Normally, the frequency range of an ECG signal is about 0.05-150 Hz [42]. The ECG signal

is characterized by five peaks and valleys labeled by the letters P, Q, R, S, T. Sometimes

there is another peak called U peak(which signifies the isoelectric line). The P-, QRS- and T-

waves reflect the rhythmic electrical depolarization and re-polarization of the myocardium

associated with the contractions of the atria and ventricles. This ECG is used clinically

in diagnosing various abnormalities and conditions associated with the heart. The time

period of the various peaks in the ECG are mentioned in Table 2.1.

Interval Time period

P-R interval 120 ms to 200 ms

Q-T interval 350 ms to 440 ms

S-T interval 50 ms to 150 ms

P-wave interval 110 ms

QRS interval 90 ms

Table 2.1: Time period of different intervals in ECG based on [52]

The normal value of heart beat lies in the range of 60 to 100 beats/minute. A slower rate

than this is called bradycardia and a higher rate is called tachycardia. If the cycles are not

11


evenly spaced, an arrhythmia may be indicated. Many other abnormalities like myocar-

dial infarction, cardiac murmurs, cardiac dysrhythmias etc. from the heart could be easily

detected from ECG analysis.

2.3. Photoplethysmography (PPG)

In PPG optical properties of the tissue are used for diagnoses. When the light is irradiated

on a tissue a number of processes can take place, for example photo-thermal reactions,

photo-chemical reactions, fluorescence, transmittance and reflectance. But the most signi-

ficant property which is used in the PPG analysis is the time dependent absorption of light

due to pulsation of the blood in the blood vessels [5]. This property is used to detect the

blood volume changes in the vascular bed of the tissue. The human body is made up of

several different kinds of cells which are responsible for the optical properties of tissue.

These cells are responsible for scattering, reflection and transmittance of the light waves.

The major events that take place in the tissue are shown in Figure 2.4.

Absorption

Incident light

Snake component

Ballistic component

Diffuse transmittance

Diffuse reflectance

Medium

Figure 2.4: Different events when light passes through a medium (based on [13])

As it can be seen that some photons of incident light passes directly through the tissue and

are termed as ballistic components. Other components are reflected or absorbed by the

tissue. Snake components are those components which scatter less and then nearly follow

the same direction as the ballistic components. They can be differentiated by the delay in

12


arrival time at the detector. Photons which scatter many times will diffuse through the

tissue and either end up as diffuse transmittance or reflectance.

The basic optical properties of a tissue model can be described by four parameters: Refract-

ive index, Absorption coefficient, Scattering coefficient and Phase function. The human

body is made of different types of structures such as blood, skin, bone and different kind

of tissues. Hence these properties vary according to the location and the particular tissue

under consideration. As the sensory system in this project is based on reflectance PPG,

hence the main area of study has been always blood vessels located beneath the skin. The

skin tissue consists of layers as shown in figure 2.5. The outermost layer is called the epi-

dermis followed by the dermis, which is divided into papillary and reticular region. The

innermost layer is called hypodermis and contains adipose and areolar tissue. All these

layers have different compositions [52]. The optical properties are therefore different in all

these layers as the distribution of blood vessels is different in the skin. Hence it is very

important to consider the location of PPG sensor.

Stratum corneum

Papillary region

Reticular region

Adipose tissueAreolar tissue

Epidermis

Dermis

Hypodermis

Figure 2.5: Layers of the Skin1

1http://www.lionden.com/ap1out-skin.html

13

http://www.lionden.com/ap1out-skin.html


2.3.1. Pulse Wave Generation

The major absorption of the infrared occurs due to three major component water, fat and

blood present in the tissue. The concentration of blood and water changes due to metabolic

activities. For example due to the heart beat the concentration of oxygenated (HbO2) and

deoxygenated haemoglobin (Hb) varies and hence the absorption of photons also varies.

Absorption at the wavelengths 650 nm and 950 nm differs significantly between HbO2 and

Hb as shown in Figure 2.6 [57]. As it can be visualized from the figure the excitation coef-

ficient for Hb and HbO2 changes with wavelength. In general two different wavelengths

are used for acquiring PPG signals, i.e. 650 nm (Red LED) and 940 nm (Infrared). Both

the wavelengths have different excitation coefficient. The excitation coefficient of red in-

frared is very large for Hb in comparison to HbO2. For this particular thesis infrared emit-

ters are used as the excitation coefficient is quite linear for both Hb and HbO2. Isobestic

wavelength is the point where the excitation coefficient for both Hb and HbO2 is same and

the corresponding wavelength is 810 nm.

0

200

400

600

800

1000

600 650 700 800 900 1000

Hb02 Hb

Red LED

Isobestic wavelength

Infrared LED

Wavelength λ\ (nm)

Exci

tati

on

Co

ffe

cie

nt\

(1

\Mo

l.cm

)

Typical Wavelengths in pulse oximetry

Figure 2.6: Absorption spectrum of Hb and HbO2 based on [57]

This is not the only reason for absorption but also due to changes in the volume of the ves-

sels which happens when the heart pumps blood in the vessels, then the vessels expand in

14


size to accommodate a sudden change in pressure, this phenomenon also causes a change

in the absorption properties [57].

As mentioned earlier some photons are absorbed and some are reflected. Due to the pump-

ing action of the heart there is a change in the concentration of Hb and HbO2 in blood ves-

sels. This change in concentration is sensed via photodiode due to change in absorption of

a particular wavelength (940 nm). The change detected is PPG waveform which contains

both the DC and AC part shown in Figure 2.7.

Figure 2.7 below illustrates the natural morphology of the signal for cardiac rhythm which

is obtained through optical measurement and is known as a PPG, where the word photo

comes from the optical measurement and plethysmogram is the change in volume in blood

vessels due to blood pressure [3]. The pulse produced travels like a wave inside the vessel

due to continuous beating of the heart.

Inci

den

t lig

ht Tr

ansm

itte

d

ligh

t

Ab

sorb

ed

ligh

t

AC

DC

Tdia

tOne Cardiac Cycle

Dicrotic notch

Pulsating arterial blood

Nonpulsating arterial blood

Nonpulsating Venous blood

Other Tissue (like skin, bone, muscles, etc.)

Figure 2.7: PPG signal with AC and DC components (based on [13])

As illustrated in the figure 2.7 there are various contributions to the absorbed light due to

tissues such as bone, skin, etc. and due to venous blood plus non-pulsating arterial blood.

These are all termed as Direct Current (DC) level of the signal. Whereas the Alternating

Current (AC) level only consists of pulsating arterial blood due to the contraction and

relaxation of the heart. The minimum point is marked as Tdia, where the absorption is

15


lowest but transmittance is highest marked by diastole in the cardiac cycle. Afterwards

there is a rapid increase in the absorption and decrease in transmittance, through this the

maxima is achieved and denoted by Tsys. This corresponds to systole in the cardiac cycle.

A dicrotic notch is produced by the sudden closure of semilunar valve; there is a sudden

increase of the aortic pressure due to elastic recoil of the blood in the aorta. This happens

because of a sudden change in the pressure difference between the left ventricle and aorta.

This phenomenon keeps on happening throughout the arterial tree, whenever a branching

of arteries occur.

Let the initial wave travelling in the blood vessel be Forward-wave and then at some point

due to branching of the arteries i.e. when blood moves from arteries to capillaries there

is a sudden decrease in diameter of the vessel. Due to this the wave is reflected back

and is termed as Reflected wave. As the heart beats continuously there are always Forward-

waves at a time interval 4t which depends on various physiological parameters. When

both the reflected wave and forward wave combine they form the actual PPG wave. This

phenomenon is illustrated in Figure 2.8.∆t

Actual Wave

Forward-Wave

Reflected wave

Figure 2.8: Formation of Dicrotic Notch (based on [31])

But still the origin of AC and DC components of the PPG signal is not fully understood and

still studies are undergoing to understand the genesis of this particular morphology and

16


how this morphology changes with age, cardiac and vascular disorders and location of the

sensor on the body. The AC component of the PPG wave provides a direct relationship

to the arterial pressure wave. The wave propagates from the central to the peripheral

vascular system. In the periphery there is a decrease in diameter of the arteries, as well as

the elasticity, these more resistant vessels induce reflection in the wave. So the PPG signal

in whole is a summation of propagation, as well as reflectance of the blood pressure inside

the vessel.

2.3.2. Morphological Analysis of PPG Signals

The amplitude of the pulsatile component of the PPG signal is influenced by respiration,

nervous system and other factors such as sensor positioning which influence the normal

perfusion. [18]. Other factors also include the sensor positioning and hence its highly vari-

able and is not considered as a physiological marker. Change in the temperature has an

effect on the amplitude of the signal but there is no significant effect on the contour of

the signal. Local cold diminishes the pulse without significant effect whereas heat has the

opposite effect [33]. Moreover it is shown that administration of vasodilator drugs have

an effect on the blood pressure but no significant affect on the contour of PPG signal [46].

These studies indicate that the contour of the signal only changes when there is a charac-

teristic change in the systemic circulation.

Various research groups have tried to analyze the different contours of the PPG pulse such

as a prominent study done by Dawber et al. in 1973 [9]. They classified the PPG into four

basic contours as shown in the Figure 2.9.

The class one consists of a distinct notch followed by class two with a notch but not very

distinct as in the case of class one. In class three there is no notch present but there is

a change in the angle of decent. In class four no notch is formed as well as there is no

change in angle of decent. It was also concluded that class one is more prevalent in young

individuals and class 4 is prevalent in older patients suffering from cardiac issues.

17


Class I Class II

Class III Class IV

Figure 2.9: Classification of the DVP or PPG waveform based on Dawber et al. (based on [9])

Another significant study was done by Takazawa et al. in 1998. They made use of second

derivative of the PPG waveform to mark various significant points in the contour. The

second derivative of the PPG wave form with these points is shown in figure below 2.10.

PPG Waveform

II Derivative

a

b cde

cb

a

de

Class I Class 3

Figure 2.10: The PPG waveform (upper panel) and its second derivative (d2PPG/dt2, lower panel)

showing the definition of the a, b, c, d and e waves for waveforms of class 1 and class 3

based on Dawber et al. (based on [9])

The relative height of the different points on the PPG particularly the point d to point a in

class three has been related to aging and arterial stiffness and effects of vasoactive drugs.

The b/a ratio has been related to carotid distensibility and ageing [33]. In a study to assess

arterial distensibility in adolescents, the d/a ratio identified individuals at increased risk

of developing atherosclerosis [33]. This study by using second derivative has also been

applied to study of peripheral pressure pulse with similar results [41].

18


2.3.3. Relation to the Pressure Curve

To assess the velocity of propagation of pulse waves through the arterial tree has always

gathered the attention of physicians and physiologists. In 1808, Young stated the interrela-

tionship between PWV and artery stiffness. Since then PWV is associated as most relevant

cardiovascular risk factor [39] [37].

The common approach to calculate PWV relies on tracking of pressure pulses created at

systolic ejection, i.e. the pressure pulse created at the onset of left ventricular ejection.

While travelling through the arterial tree, the arrival time of a pressure pulse at two dif-

ferent sites is detected. This delay provides the propagation time or Pulse Transient Time

(PTT). Finally PWV can be calculated as the ratio between the distance between the two

measurement sites and the PTT [29].

In general for a given arterial segment of a particular length , PWV is defined as:

PWV =Length4t

(2.1)

where 4t is the time pressure pulse will require to travel through the whole segment,

it is also known as PTT. PTT is typically measured indirectly through a related quantity

known as PAT. PAT is calculated as the delay between the R peak of ECG and a particular

point in the PPG signal, such as the foot (PATf oot), peak (PATpeak) or maximum slope point

(PATf irstderivative) (see Figure 2.11). PAT is related to PTT as follows:

PAT = PEP + PTT (2.2)

where PEP stands for Pre-Ejection Period (PEP). It represents the isovolumetric contraction

time of the heart, which is the time it takes for the myocardium to raise enough pressure

to open the aortic valve and start pushing blood out of the ventricle [7].

19


R-wave

PEAK

ECGPPG

Figure 2.11: Graphical representation for the calculation of PAT (based on [25])

Major determinants of Pulse Wave Velocity: The major determinants of PWV are age,

blood pressure, gender, heart rate and pathological changes.

• As the age increases the pulsatile strain breaks the elastic fibres in the arterial wall,

which are replaced by collagen [53]. This in turn increases the arterial stiffness and

consequently leads to increased PWV. But there is very small change in the muscular

or distal arteries.

14

6

0 90

PW

V \

(m

\s)

Age\years

Distal arteries

Central elastic arteries

Figure 2.12: The dependency of PWV with age (based on [15])

20


• Increase in arterial blood pressure leads to increase in the arterial stiffness and vice

versa. Due to increase in arterial stiffness both the reflected and forward waves

propagate much more faster in the vessels. Hence instead of reaching aorta at dia-

stole, the reflected wave reaches it during systole.

• When the heart rate increases arterial stiffness also increases. Because heart rate in-

fluences the time required by the blood vessels to distend, hence an increase in the

rigidity of the arterial wall occurs [15].

• Young, middle age and healthy adult men display higher PWV values as compared

to women of same age [11].

To conclude, an increase in the blood pressure would raise arterial stiffness and ,thus leads

to increase in the PWV. But this is only valid to central arteries such as the aorta. Unfortu-

nately for non-elastic or distal arteries this relationship is not unique because of effects of

vasomotion1 [15] (shown in Figure 2.13 ).

6

3

20 110BP\(mmHg)

PW

V\(

m\s

)

6

PW

V\(

m\s

)

3BP\(mmHg)

20 110

ELASTIC ARTERIES

NON-ELASTIC ARTERIES

Due to Vasomotion

Figure 2.13: The relationship between BP and PWV for elastic (aorta) and non-elastic (periphery)

arteries (based on [15])

1Vasomotion is mechanical oscillation in blood vessels leading to change in diameter, independent of heartbeat, innervation or respiration

21


2.4. In-Ear Measurement System

The auditory canal is approximately 2.5 cm in length and 0.7 cm in diameter and lies inside

the temporal bone [52]. Auditory canal is composed of elastic tissue and cartilage bone.

The shape of the auditory canal is like the Greek letter sigmoid ( ς ). The periphery of the

auditory canal is made of soft elastic tissue mainly composed of cartilage tissue continuing

from the pinna and is covered by the skin. Ear provides a natural anchoring position for

all In-Ear headphones [14]. The latter part of the auditory canal is composed of hard bone

and hence is very rigid. The major artery supplying blood in this region is carotid artery

which is a branch of aorta. The carotid artery is further divided into internal and external

carotid artery, which supplies oxygenated blood to the head and neck region of the body.

Figure 2.14: Major arteries inside ear (for numbers refer to table 2.2) 1

As shown in the figure 2.14 above the internal and external carotid artery which are further

divided into various small arteries, and are responsible for supplying blood to internal and

external ear. The arteries which supply blood to various parts of the ear and their location

are shown below in table 2.2. As it is clear from the table 2.2 below that the ear region,

where the measurement device is present is rich in supply of oxygenated blood.

1www.inkling.com/read/atlas-of-anatomy-gilroy-thieme-2nd/chapter-37/arteries-of-the-middle-ear

22

www.inkling.com/read/atlas-of-anatomy-gilroy-thieme-2nd/chapter-37/arteries-of-the-middle-ear



Orgin Artery Distribution

Internal Carotoid

Caroticotympanic ① Tympanic cavity(anterior wall),Pharyogotympanic(auditory) tube

External Carotoid

Ascending pharyngeal (medial branch)

Inferior tympanic ②

Tympanic cavity(floor),promontory

Maxillary (terminal branch)

Deep auricular ③ Tympanic cavity(floor), tympanic membrane

Anterior tympanic ④ Tympanic membrane,mastoid antrum, malleus, incus

Middle meningeal

Superior tympanic ⑤

Tympanic cavity(roof), tensor tympani, stapes

Posterior auricular (posterior branch)

Stylomastoid Stylomastoid ⑥ Tympanic cavity(posterior wall), mastoid air cells, stapedius muscle, stapes

Posterior tympanic ⑦

Chorda tympani, tympanic membrane, malleus

Table 2.2: Different arteries in the human ear at various locations 1

Tympanic membrane or ear drum is a thin transparent membrane between external and

middle ear. The tympanic membrane is covered by epidermis and is lined by simple

cuboidal epithelium. Between the epithelial layers is connective tissue composed of col-

lagen, elastic fibers, and fibroblasts. The tympanic membrane has also very rich supply of

the blood, as shown in the Figure 2.15.

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23




Figure 2.15: Medial view: various small arteies present on tympanic membrane 1

Middle ear is a small air filled cavity lined by epithelium and present in the petrous portion

of the temporal bone. It is separated from external ear through tympanic membrane and

contains the three smallest bones of the human body known as Malleus, Incus and Stapes.

And from the internal ear by a thin bony partition that contains two small membrane-

covered openings: the oval window and the round window. The anterior wall of the

middle ear also contains an opening, which connects to the pharyngotympanic tube also

commonly known as eustachian tube. The eustachian tube is made up of bones and cartil-

age and is connected to nasopharynx (superior portion of the throat). It is normally closed

at its medial (pharyngeal) end. During swallowing and yawning, it opens, allowing air to

enter or leave the middle ear until the pressure in the middle ear equals the atmospheric

pressure. When the pressure is balanced the tympanic membrane vibrates freely according

to sound but when there is a difference in pressure pain, ringing, vertigo and deafness may

occur [52].

To further analyze the signal and to find the genesis of the signal a model is assumed. In

this modeling the auditory canal is considered to be cylindrical shaped with the tympanic1www.inkling.com/read/atlas-of-anatomy-gilroy-thieme-2nd/chapter-37/arteries-of-the-middle-ear

24




membrane at one end and some kind of sealing1 is done as shown in Figure 2.16. As

its already known that the average pressure inside the middle layer is equivalent to the

atmospheric pressure due to eustachian tube. Due to high perfusion inside the ear, the

pulsating blood changes the volume in-between the tympanic membrane and the sealing,

and hence pressure keeps on changing between the middle ear and outer ear. We assume

that air inside the outer ear when properly sealed is ideal gas :

P ·V = n · R · T = constant (2.3)

Therefore

4P =constant4V

(2.4)

Where,

P = Pressure inside ear (mm/Hg)

V = Volume (cm3)

n = number of moles

R = Universal gas constant

T = Temperature (K)

4 V = change in volume (cm3)

4 P = change in pressure (mm/Hg)

This pulsating blood leads to formation of a signal which is equivalent to blood pressure

signal. As mentioned earlier the signal discovered inside the ear is termed as In-Ear meas-

urement or signal.

1which could be achieved either through good fitting headphones or through some other means

25


SealingTymphanic Membrane

Atmospheric Pressure

Middle Ear

Change in volume (∆V)

due to pulsating blood

Eustachian tube

Tissue rich with arteries

Figure 2.16: Model for In-Ear measurement

Therefore change in volume due to blood flow leads to change in pressure inside the ear

which is detected in pulsations via In-Ear measurement system.

2.5. Bioimpedance and Impedance Cardiogram (ICG)

The electrical impedance is the Alternating Current (AC) resistance and gives the complex

ratio of voltage to current. In contrast to the electrical resistance, the impedance is complex

and depends on the frequency. Figure 2.17 shows the electrical impedance in the complex

Gaussian plane.

|Z|

Im (Z)

Re(Z)

Figure 2.17: Representation of an inductive impedance in the complex plane after Gauss

26


The complex impedance Z for harmonic excitations, e.g. in the polar form, as shown in the

equation below, where V and I are the amplitudes of the voltage and current at the angular

frequency ω.

Z = |Z|ejϕ =|V(ω))||I(ω)| ej(ϕV(ω)−ϕI(ω)) (2.5)

With the phase angle φ and the angular frequency ω= 2πf.

Bioimpedance: In general, the human body consists of numerous cells. Moreover between

the individual cells there is so-called extracellular space, which is generally filled with

extracellular fluid. The cells have semi-permeable plasma membranes and contain intra-

cellular fluid and thus the plasma membrane separates the intracellular fluid from the

extracellular fluid. The intracellular and extracellular fluid contains water and dissolved

salts, which are conducting in nature. The membrane itself consists of a lipid bilayer and

proteins which are non conducting [32].

The electrical isolation of conducting extra and intracellular fluid through the bilayer lipid

membrane is an example of a capacitor and explains the observed frequency dependence of

the electric bioimpedance. To describe quantitatively the behaviour of the tissue, different

simplifying assumptions are made. A simplified model of electrodes skin impedance with

usage of one capacitor C and resistor RE in parallel. And a resistor RS in series with them

is shown in Figure 2.18 below. Whereby RE represents the shunt impedance of the cell

membrane and RS represents the extracellular fluid.

Rs

C

RE

Figure 2.18: Simplified electrical equivalent circuit diagram of a cell or a bunch of cells

27


2.5.1. Impedance Plethysmography

It is a method of determining changing tissue volumes in the body, based on the meas-

urement of electric impedance of tissue. The measurement of tissue impedance is closely

related to lead field theory. According to it’s change in the conductivity and permittivity of

a region produces a change in the impedance signal which is proportional to the amount

of current flowing in that region [32].

4Z =

ˆ14σ

JLE(t0) · JLI(t1)dv (2.6)

Where,

4Z = impedance change (Ω/m3)

t0, t1 = time instants

4σ = conductivity change between the two time instants (S/m = 1/Ω ·m)

JLE = lead field of the voltage measurement electrodes for unit reciprocal current

(1/m2)

JLI = lead field of the current feeding electrodes for unit current (1/m2)

v = volume (m3)

The equation 2.6 describes how the change in the conductivity of a fixed volume material

can be converted to change in impedance, which could be evaluated by the measured

voltage divided by the applied current. The region v consists of an inhomogeneous volume

conductor whose conductivity (as a function of position) at time t0 is σ(t0). At t1, this has

changed to σ(t0), and it is this change (t1)-(t0) =4σ which are responsible for the measured

impedance change4Z.

Above equation 2.6 lays the theoretical basis for impedance plethysmography, but still

there is an uncertainty regarding how conductivity varies throughout the thorax as a func-

tion of time in a cardiac cycle. More research is required to develop models, which could

provide a clear understanding.

28


Measurement of the Thorax Impedance: Electric current in the frequency range of 20-

100 KHz is usually administered into the volume conductor (Thorax) and corresponding

voltage is measured. The Ratio of the values gives the impedance dZ, which is variation

over time. The DC value is eliminated as its being contributed by other tissues such as

skin, bones, muscles, etc. and only impedance variation values are further analysed. To

eliminate the effect of electrodes, four electrode pair method is used for analysis. Usually

outer electrodes are used for introducing current in the subject and the inner pair are used

for measuring the corresponding voltages (this could be vice versa also)1 [32].

The impedance changes in the thorax are usually measured by placing four band elec-

trodes2. In the physical arrangement of the outer pair, one electrode is placed around the

abdomen and the other around the upper part of the neck. For the inner electrode pair, one

electrode is placed around the thorax at the level of the joint between the xiphoid and the

sternum, called the xiphisternal joint, and the other around the lower part of the neck [27].

The common setup is shown in figure 2.19 below.

Sternum

Xiphoid Xiphi-Sternal joint

I U

Figure 2.19: Placement of the band electrodes in the measurement of the thorax impedance [32]

But various other configurations have been researched such as a 9 or 8 electrode config-

uration. By changing the electrode configuration, the current fields are altered inside the

thorax [24]. Reasons for using four electrodes:

1well known as four electrode setup2which can be easily replaced by ECG electrodes

29


- The current density is higher near the electrodes then elsewhere in the tissue. This

causes the measured impedance to weight more near the electrodes than the overall

tissue. This could be really advantageous when an array of electrodes is used for

measurements.

- Pulsating blood through the tissues causes changes in skin electrode impedance and

overall change in tissue impedance. Skin electrode impedance is in series with tissue

impedance hence two electrode system leads to high current density under voltage

sensing electrodes. Whereas in four electrode configuration it is more uniform dens-

ity.

- Moreover the overall electrode impedance is in series with the tissue impedance and

hence this change may also occur over perfusion therefore leading to an error.

Figure 2.20 presents a typical thorax impedance curve (|Z|), its first time derivative (dZ/dt),

and the simultaneous ECG. Lababidi et al. [27] studied the timing of each significant notch

in the first derivative curve of the thoracic impedance signal and assigned them to certain

events in the heart cycle. The points are marked in Figure 2.20 and corresponding events

are shown in the table below the figure.

Z

ECG

(R-Z)

A BXY

Zo

Figure 2.20: Thorax impedance curve based on [32]

30


A End of P-wave and starting of QRS complex(Atrial contraction)

B Closure of tricuspid valve

X Closure of aortic valve

Y Closure of pulmonic valve valve

O Opening snap of mitral valve

Z Third heart sound

Table 2.3: Events in cardiac cycle according to different notches in impedance curve

2.6. Stroke Volume (SV)

SV is the amount of blood pumped out of the left ventricles in each cardiac cycle. When

determining the SV from the thorax impedance curve some assumptions were made by

Kubicek et al. [26]. As in each cardiac cycle the blood is flowing towards and away from the

heart, thus stroke volume can be determined by extrapolating the value of impedance(4Z).

Therefore SV can be calculated by the following equation.

SV = ρbl2

Z2 |dZdt|min · te (2.7)

Where,

SV = Stroke volume (ml)

ρb = Blood resistivity (Ω · cm)

l = Mean distance between the inner electrodes (cm)

Z = Mean impedance of the thorax (Ω)

| dZdt |min = Absolute value of the maximum deviation of the first derivative signal dur-

ing systole.

te = Ejection time (s)

31


The resistivity of the blood is of the order of 100-160 Ω cm. Its value depends on hematocrit

[32].

2.7. Wavelet Transform

The signals acquired from the hardware are generally afflicted with noise and for para-

meter extraction it is valuable to mitigate this noise. There are a number of techniques that

can be implemented based on filters (Finite Impulse Response (FIR), Infinite Impulse Re-

sponse (IIR), zero phase shift, etc.). Moreover there is a need to preserve the fidelity of the

original signal in terms of amplitude and time. For this purpose wavelet transform tech-

nique is employed which is less complicated and is supported by wavelet toolbox from

Mathwork MATLAB.

A transform can be thought as a representation of a signal in some other domain to provide

some extra information which was not evident before. Fourier transform which represents

the signal in frequency domain furnishes new information about the original signal. How-

ever, it lacks the ability to provide both the time and frequency information alongside.

Therefore many new approaches were introduced to fill this void. One of the approach is

called wavelet transform. Wavelet transform could be continuous as well as discrete but,

in the current work Discrete Wavelet Transform (DWT) is used.

Another method called Short Time Fourier Transform (STFT) by Dennis Gabor(1946) was

also proposed, which modified the Fourier transform by using the windowing technique.

So STFT maps the signal in two dimensional function of time and frequency. The drawback

with this method is that it lacks precision, which is dependent on the size of the window.

Wavelet provides a windowing technique with variable size window and allows the use of

long time intervals where there is more precise low frequency information to be extracted,

and shorter regions where high frequency information is needed. The major difference

between the STFT and wavelet is shown in Figure 2.21.

32


Amplitude

Freq

uen

cy

Freq

uen

cy

Scale

AmplitudeTime

Time Time

Time Domain (Shannon) Frequency Domain (Fourier)

STFT (Gabor) Wavelet Analysis

Figure 2.21: Comparison in different domains (based on [34])

A wavelet is a waveform of effectively limited duration that has an average value of zero,

moreover it is irregular and asymmetric. Fourier analysis consists of breaking up a signal

into sine waves of various frequencies. Similarly, wavelet analysis is the breaking up of a

signal into shifted and scaled versions of the original (or mother) wavelet [34].

As it can be easily inferred from Figure 2.21 that the wavelet analysis produces a time-scale

view of the signal. Therefore it is important to know, what scaling and shifting means.

Scaling: It simply means stretching or compressing of the signal. Scaling is done via a scale

factor which is denoted by a.

Shifting: Shifting simply means delaying its onset. Mathematically, delaying a function

f(t) by k is represented by f(t-k).

2.7.1. The Discrete Wavelet Transform

Calculating wavelet transform at every possible scale and shift is a fair amount of work

and would generate a lot of data. Therefore it is very essential to choose subsets of scales

and positions, hence so called dyadic scales and positions (based on powers of two) are

33


used. Such an analysis is possible in DWT. The most efficient way to perform this scheme

using filters was developed in 1988 by Mallat [45].

For physiological signals, low frequency signals are of great importance whereas high

frequency signals are mostly noise. It is for this reason that, in wavelet analysis, we of-

ten speak of approximations and details. The approximations are the high-scale, low-

frequency components of the signal. The details are the low-scale, high-frequency compon-

ents. The filtering process or the decomposition process can be iterated, with successive

approximations being decomposed in turn, so that one signal is broken down into many

lower-resolution components [34]. This is called the wavelet decomposition tree and illus-

trated in Figure 2.22.

S

Low Pass High Pass

Signal

Approximations

Details

Figure 2.22: Filtering process in Multiple-Level Decompostions

While decomposition of the signal downsampling is introduced and DWT coefficients are

obtained. The lengths of detail and approximation coefficient vectors are slightly more

than half the length of the original signal. This happens because of the filtration process,

which is implemented by convolving the signal with a filter. The length of the final signal

depends upon the length of the impulse response. And the length of the output signal is

34


equal to the length of the original signal plus the length of the impulse response minus one.

Hence convolution "smears" the signal, introducing several extra samples into the result.

This is followed by reconstruction of the signal which consists of upsampling and fil-

tering. The MATLAB wavelet toolbox includes commands, like idwt and waverec, that

perform one-level or multi-level reconstruction, respectively, on the components of one-

dimensional signals. Therefore there are many ways to reassemble a signal, which is illus-

trated in Figure 2.23:

S

Signal

Approximations

Details

Figure 2.23: Signal reconstruction in several ways (based on [34])

This provides a lot of flexibility in choosing the characteristic of signals while reconstruct-

ing it. For example a signal can be reconstructed and analysed for different frequencies.

This helps in preventing certain frequencies which are an essential component of the ori-

ginal signal, especially in case of physiological signals.

Different wavelet families are available which could be used for decomposition, as well

as reconstruction of the signal. Moreover new wavelets can also be reconstructed by using

wavelet toolbox in MATLAB for specific applications. Some of the wavelet families present

in MATLAB are Haar, Daubechies, Biorthogonal, Coiflets, Symlets, Morlet, etc. The wave-

let families have different characteristics and provides different results on the same signal.

It is very essential to choose the right wavelet to get the best results. For example for ECG

signals biorthogonal 3.3 wavelet produces most promising results [36].

35

3. System Design

This chapter describes the design and development of the overall system, which consists

of a patient front end for data capturing and a FPGA based control unit. It also explains

the interaction between the different modules, as well as the PCB layout.

3.1. Requirement Analysis of the System

A requirement list is developed for the overall system. The requirement list is illustrated

in table 3.1. It gives the general requirements, which must be implemented in the system

and are essential to achieve the aims of the current thesis.

ECG Module Bandwidth of Signal: 50mHz to 60 Hz

Stopband: 1 kHz (-60 dB)

Expected signal noise sources: 50 Hz noise (EMI and common mode signal) expected 1000 times higher than the signal, movement artifacts, baseline wandering (electrodes)

Filtration: notch filter

D D W

PPG Module Bandwidth of Signal: 0.3 Hz to 20 Hz

Stopband: 100 Hz (-30 dB)

LED current control loop to achieve a constant bias / working point.

Expected signal noise sources: 100 Hz noise (due to ambient light)

D D D

In-Ear Module Bandwidth of Signal: 50 mHz to 30 Hz

Stopband: 50 Hz (-20 dB)

Expected signal noise sources: 50 Hz noise (EMI coupled), motion artifacts

Pressure measurement inside the ear using pressure sensor

To couple PPG and In-Ear measurement inside the ear.

D D W D

Impedance Cardiometery Module

Current range: 50 μA to 5 mA

Frequency range: 10 kHz to 250 kHz

SINAD (Signal-to-noise and distortion ratio) should be higher than 60 dB

Current Source with high output impedance in range of 100 kΩ at 100 kHz

Driven shields should be implemented for current accuracy and to mitigate the effect of EMI

D D D D D

D: Demand W: Wish

Table 3.1: Requirement list for the developed EIT IM_V1.10 system

36

3. System Design

3.2. Block Diagram of the Developed System

By going through the overall requirement one ECG, four PPG, two In-Ear and one im-

pedance cardiometry block is implemented. As the signals are analog therefore different

Analog to Digital Converter (ADC) are required to convert them to digital domain. The

AFE can convert all seven channels simultaneously, which is a major requirement, when

an fixed time reference between the channels is needed. The FPGA also generates the input

clock for the AFE to maintain a synchronization between impedance cardiometry module

and the remaining modules. Every analog block is implemented by using sensors as well

as proper filters. All the designs for analog unit are implemented on bread board, to eval-

uate their behaviour and then optimizing the requirement list further. Furthermore a USB

interface is implemented to transfer the signals to computer.

The block diagram shown in Figure 3.1 consists of various parts, which can be primarily

divided in two categories: analog and digital parts. The analog modules are responsible for

acquisition of the signal from the subject. The control and digital module are responsible

for converting the signal into digital domain and for synchronizing the acquisition.

37

3. System Design

PPG MODULE

(4 Channels)

IN-EAR MODULE

(2 Channels)

CARDIOMETERY

MODULE

SUBJEC

T

ECG MODULE

(1 Channel)

FFT & Averaging

Downsampling

Control logic

SIGNAL GENERATION (DDS)

PGA

PGA

PGA

FPGA

PC

DRIVER

DRIVER

VOLTAGE

CURRENT

R

CURRENT SOURCE

ADS1298

SUBJECT

DAC

ADC

ADC

UI

GENERAL PURPOSE

(1 Channel)

SPI

Figure 3.1: Block diagram of the developed EIT IM_V1.10 system

The analog front end chip (ADS1298, Texas instrument), marked in green, is responsible

for converting analog signals from ECG, PPG and In-Ear modules. Cardiometry unit has

a separate ADC and Digital to Analog Converter (DAC) (marked in blue), because of it’s

inheritance from EIT Impedance meter V1.01 and this system was working exceptionally

fine for impedance measurements [21] [2]. Moreover as stated in the data sheet of ADS1298

38

3. System Design

it’s a front end module for biomedical signal acquisition for low frequency signal upto a

few kHz. From here the signals are fed to the FPGA (marked in black) and from here to

PC via a USB 2.0 interface for further analysis. The whole embedded system and display

is controlled via an interface program in MATLAB on the PC.

3.3. Ensuring Medical Safety

Safety of the subject is the most crucial aspect of bio-instrumentation. Injuries caused due

to electric shock depends upon the magnitude, the path it takes through the body and the

time for which it flows. The current work requires auxiliary currents flowing through the

body for measurement of the cardiac output of the heart. Furthermore ECG also has an

electrical connection to the patient via the ECG electrodes. On the other hand PPG and

In-Ear headphones have no electrical contact. But still they are all applied parts according

to the IEC 60601-1 [17].

It is very important that the device is stable and keeps the currents in the permissible range

as mentioned in the technical standards for the safety and effectiveness of medical electrical

equipment.

According to the standard, allowable patient leakage and auxiliary current (AC) in nor-

mal condition is 100 µARMS for type BF applied part [17]. Additionally it is a frequency

dependent process, so for higher frequency’s higher currents can be administered to the

subject but not more than 10 µARMS as shown in Figure 3.2. The current and frequency

range of cardiometry module is 125 µA to 5 mA approximately and 10 kHz to 250 kHz

respectively. But the software makes sure that the auxiliary current is always in the pre-

scribed range. Therefore our device is in compliance according to the standard.

39

3. System Design

+20

0

-20

-40

-60

10 100 1 k 10 k 100 k 1 M

Re

lati

ve m

agn

itu

de

/ d

B

f / Hz

Figure 3.2: Frequency characteristics of a medical device according to IEC-60601 [16]

According to International Electrotechnical Commission (IEC)-60601 the temperature of

the applied part (made of molded material, plastic, rubber, wood) when the duration of

the contact is more than 10 min should not exceed 43C [16]. The commercial in-ear head-

phones used for In-Ear measurements fall well below the defined limits. Moreover the heat

produced due to Infrared (IR) emitter in the PPG is not expected to be more than 30C.

For powering the device medically approved external AC-DC power adapters are used.

These power adapters have comprehensive overload protection and in-built Electromag-

netic Interference (EMI) Filtering. It also provides high dielectric strength and a very low

leakage current.

As the device is interfaced with a PC, the complete setup even the connections to USB

2.0 becomes a part of the applied parts and hence have to be protected with appropriate

insulation [16]. For making the design of the device simpler an USB isolator, which is an

optical hub is employed, thus preventing direct connection to current from a PC. But the

current device lacks a proper enclosure which provides an easy access to switches, cables

and connectors. This has to be further improved in the coming version.

40

3. System Design

3.4. Analog Part

Analog part consists of all the electronic components responsible for acquisition of the bio-

logical signals from the patient. As the focus of the current thesis is to analyse the vascular

biology and to conceptualize the In-Ear and impedance cardiometry measurement system.

Therefore the analog part consists of ECG, in-ear system and multiple PPG’s. The hard-

ware used and the concepts behind are explained in the following sections.

3.4.1. ECG Module

Many factors have to be considered while designing an ECG circuit such as frequency

components of the ECG signal and different kind of distortions produced due to 50 Hz

noise. The interferences from other electrical devices and other sources also play a vital

role. The differential signal which appears between the electrodes is typically less than ±5mV with a frequency range of 50 mHz and 150 Hz [42].

The input stage of the developed ECG module consists of an instrumentation amplifier

with provisions for a Driven Right Leg (DRL) circuit and a base line wander rejection circuit

(see Figure 3.3). For the design a LT1789-1 (from Linear Technologies), which is a micro

power instrumentation amplifier with rail to rail configuration, for an optimal usage of the

supply voltage is used. The gain can be easily set by using a single resistor as shown in the

equation 3.8 below:

G = 1 +200Rg

(3.8)

For the device the gain is set equal to fifty one and can be further amplified by using the

internal Programmable Gain Amplifier (PGA) present in the ADS1298. The gain is limited

to 51 because the input signal to ADS1298 has a maximum value of ± 2 V and higher gain

may saturate the ADS and also a high gain can lead to a saturation of the Instrumentation

Amplifier (INA) because the difference of the electrodes half-cell potential also amplifies.

To further reduce the common mode voltage a DRL circuit is implemented. In the Figure

3.3, common mode voltage is sensed between the resistors R913 and R914. The first op

41

3. System Design

amp acts as a buffer, then it is inverted and amplified by using the second op amp, and

then feedback through the right leg to the patient. Such a negative feedback mitigates

the common-mode voltage. For the DRL circuitry OPA4134 is used which is an ultra low

distortion and low noise amplifier from Texas Instruments (TI) instruments. Resistor R917

limits the maximal current and protects the patient in case of error. The capacitor C920

limits the gain to higher frequencies and stabilizes the DRL. The overall circuit of the ECG

module is presented below in Figure 3.3 this schematic is drawn with the help of Orcad

Capture.

EKG_LA

ECG_Notch_in

EKG_DRL

EKG_RA

EKG_SHIELD

5P0_Analog

5P0_Analog

5N0_Analog

5N0_Analog

fg=0,16Hz

G=-30

G=1 + 200k / Rg = 51

DRL

LA

RA

Rail-to-Rail

EKG_SHIELD

R917 300k 1%R917 300k 1%

R915

2k00 1%

R915

2k00 1%

IC902LT1789-1/SO8IC902LT1789-1/SO8+

3

-2

V+

7V

-4

OUT6

RG28

RG11

RE

F5

X904X904

C919

10u/25V X5R

C919

10u/25V X5R

R916 100K 1%R916 100K 1%

R919

300k 1%

R919

300k 1%

IC1209DOPA4134/SO-14IC1209DOPA4134/SO-14

+12

-13

OUT14

IC1210COPA4134/SO-14

IC1210COPA4134/SO-14

+10

-9

OUT8

X906X906

R918

10k0 1%

R918

10k0 1%

IC1210BOPA4134/SO-14

IC1210BOPA4134/SO-14

+5

-6

OUT7

X905X905

R914

2k00 1%

R914

2k00 1%C920 100P/50V NP0C920 100P/50V NP0

Figure 3.3: ECG Circuit

As stated earlier 50 Hz noise is a major problem in ECG data recording. Therefore to over-

come this problem an active twin notch filter was proposed by using two op amp OPA4134.

OPA4134 has an ultra-low distortion, low noise operational amplifiers. Moreover they

have very low noise of about 8 nV/√

Hz and a slew rate of about 20 V/µ [49]. But due

to the tolerances of the capacitances it is difficult to implement such a filter or without a

complex adjustment via variable capacitors or potentiometers. Because of this a passive

but buffered 50 Hz notch filter is implemented.

3.4.2. PPG Module

PPG is a simple and low cost technique that is widely being used to detect blood volume

changes in the tissue and it is widely being accepted that it provides valuable information

regarding the cardiovascular system of the body [33]. The system consists of IR emitter

42

3. System Design

and a photodetector. The output from the photodetector is a current so a transimpedance

amplifier which acts as a current to voltage converter is implemented.

As already discussed in the previous section 2.3.1 that deoxygenated blood preferentially

reflects the light at 940 nm (IR) and oxygenated haemoglobin (HbO2) at 660 nm (red).

Moreover from the Figure 2.6, it can be seen that the requirements for the IR emitter are

more relaxed since the ratio of excitation coefficient of Hb and (HbO2) is constant in this

part of the spectrum.

For a better understanding the complete PPG circuit is described below in Figure 3.4 con-

taining the sensing part and signal conditioning part in Figure 3.5 consisting of specific

filters.

Figure 3.4: PPG Sensing Circuit

In the sensing circuit the components marked as G1 and other current sources I1 and I2

with capacitor C2 are used for simulating the IR emitter/photodiode coupling in Linear

Technology (LT) spice1. These are not implemented on the PCB board. As due to changes

in external conditions such as ambient light, skin pigmentation as well as thickness of the

finger there is a change in intensity of the IR emitter occurs. A control loop had to be

implemented in the circuit in which the output from the photodiode is feed back to one

1www.linear.com

43

www.linear.com

3. System Design

of the input of the OPA4134 which controls the IR emitter via a MOSFET. But it was very

diffcult to achieve because either the regulation is very slow or when its fast then it destroys

the signal. The only good way to achieve this is by regulating the loop digitally via a DAC.

DC Feedback and Low Pass Filter

Figure 3.5: PPG Signal Conditioning Circuit

An IR emitter from Vishay Technologies (VSMB3940X01) with a peak wavelength of 940 nm

and an emitting angle of 60 degree is used. The photodiode (VBPW34S) from the same

company is used for detecting this particular IR light. The photodiode has a dark current

of 2 to 30 nA which increases linearly with increase in temperature. The circuit diagram

for IR emitter and photodiode are shown in Figure 3.6.

From the schematics the connector X1008 is connected to the photodiode which in turn

is connected to a transimpedance amplifier. This resistor feedback amplifier circuit is the

most common transimpedance circuit. With this configuration the light shinning on the

photodiode produces a small current that flows to the amplifier through the feedback res-

istor.

44

3. System Design

1

VREF_LED

5P0_Analog

5N0_Analog

5P0_Analog

5P0_Analog

IR-LED - 940nmfg=15.9mHz

Imax=102.77 mA

Cathode

Anode

Dark Current = 1000 pA

Photodiode

D1008

VBPW34S

D1008

VBPW34S

C1023 10u/25V X5RC1023 10u/25V X5R

Q1004MMBF170/SOT23Q1004MMBF170/SOT23

3

1

2

R1010 100k 1%R1010 100k 1%

X1009

STL-02

X1009

STL-02

11

22

X1008

STL-02

X1008

STL-02

11

22

IC1207AOPA2380/MSOP8IC1207AOPA2380/MSOP8-InA

2

+InA3

V+

8

Out1

V-

4 IC1208AOPA4134/SO-14IC1208AOPA4134/SO-14+

3

-2

V+

4V

-11

OUT1

C1027 4.7N/25V NP0C1027 4.7N/25V NP0

D1009

VSMB3940X01-GS08

D1009

VSMB3940X01-GS08

R1044

1M00 1%

R1044

1M00 1%

R1041

110R 1%

R1041

110R 1%

R1043

1k00 1%

R1043

1k00 1%

Figure 3.6: IR Emitter and Photodiode

The PPG sensor used for measurements is shown in the Figure 3.7 with infrared emitter,

photodiode and connectors for connecting it to the PCB board.

Infrared Emitter

Photodiode

Connectors

Figure 3.7: PPG sensor used for measurements

Depending upon the design requirement as transimpedance amplifier OPA2380 from Texas

instruments is employed.

45

3. System Design

OPA2380 is a monolithic combination of two different high precision amplifiers OPA355

and OPA335 to achieve high performance for PPG sensors. It performs as a transimped-

ance amplifier with extremely high precision (25 µV maximum offset and 0.1 µV/C max-

imum drift) [50]. A small capacitor in the feedback loop (C1027) controls the gain peeking

caused by the diode capacitance and for lowering the gain towards high frequency.

The signal conditioning circuit can be further divided into two parts. The first part consists

of a servo feedback filter, the main task of this filter is to remove the DC-offset arising in the

circuit due to dark current in the photodiode. The circuit for such type of filter is shown

below in Figure 3.8

PPG1_UI_out

5N0_Analog

5P0_Analog

Fg=0.0015 Hz

R1014 1k00 1%R1014 1k00 1%

R1057

10k0 1%

R1057

10k0 1%

R1017 10M0 1%R1017 10M0 1%

R1016 10k0 1%R1016 10k0 1%

C1003 10N/25V NP0C1003 10N/25V NP0

R1018R1018

IC1001AOPA4134/SO-14IC1001AOPA4134/SO-14

+3

-2

V+

4V

-11

OUT1

R1015 1k00 1%R1015 1k00 1%

C1011 10u/25V X5RC1011 10u/25V X5R

C100410N/25V NP0

C100410N/25V NP0

IC1001BOPA4134/SO-14IC1001BOPA4134/SO-14

+5

-6

OUT7

Figure 3.8: Servo-Feedback Filter

Servo feedback is a common technique to provide ac-coupling in integrated circuits. High

Pass Filter (HPF)’s has capacitors in series with the forward signal path. But this produces

problems when the signal’s have low frequency components for example in the range of

10 Hz. This may lead to having large capacitor values. Large capacitors are expensive,

non-ideal and tend to destroy the fidelity of the signal. This AC-coupling creates a pole ac-

cording to RC time constant. This servo technique solves the problem of putting capacitors

in series with the signal path [12].

By adding a low offset op amp in the negative feedback configuration, a high pass filter

with cutoff frequency of 15 mHz is implemented. It removes DC offsets applied to the

input of the amplifier as long as the offset on the input stage, multiplied by the gain, is

46

3. System Design

smaller than the maximum output voltage of this stage, and smaller than the output range

of the servo amplifier [44].

The frequency response of the filter is shown in Figure 3.9 below. As it can be easily verified

from the simulation that the particular filter has a cutoff frequency of 1.5 mHz which could

be easily set by using the formula:

fc =1√

2πRC= 15 mHz (3.9)

Figure 3.9: Simulated Frequency Response of Servo-Feedback Filter

The frequency range of the PPG signal is in between few mHz to 20 Hz, hence it is very

important to implement a low pass filter. Moreover the noise from the ambient light which

is at a frequency of about 100/120 Hz can be detected by the photodiode [55]. To achieve

such a kind of filter, a 5th order Sallen-Key filter with Bessel characteristic and a cutoff

frequency of 20 Hz and gain equal to 1 is implemented. The calculation of the cutoff fre-

quency is shown below in equation 3.10. The schematic of the filter is shown in Figure 3.10

and the simulated frequency response of the filter with the servo filter is shown in Figure

3.11.

fc =1

2π√

R1R2C1C2= 30Hz (3.10)

47

3. System Design

AFE_PPG4-

AFE_PPG4+

fg=30Hz, Bessel, G=1

R1073

0R

R1073

0R

R1070

301k 1%

R1070

301k 1%R1072

243k 1%

R1072

243k 1%

C1071 33nF/25V NP0C1071 33nF/25V NP0 C1073 47N/50V NP0C1073 47N/50V NP0

R1068

143k 1%

R1068

143k 1%

IC1209COPA4134/SO-14IC1209COPA4134/SO-14

+10

-9

OUT8

C107522nF/50V NP0

C107522nF/50V NP0

C107222nF/50V NP0

C107222nF/50V NP0

C10744.3nF/50V NP0

C10744.3nF/50V NP0

R1069

243k 1%

R1069

243k 1%R1071

464k 1%

R1071

464k 1%

IC1208DOPA4134/SO-14IC1208DOPA4134/SO-14

+12

-13

OUT14

Figure 3.10: 5th order Sallen-Key Filter

Figure 3.11: Simulated Frequency Response of overall PPG circuit

In the development of all the filters in the signal conditioning part OPA4134 from TI in-

struments is used. Furthermore it has a bandwidth of 8 MHz which makes it suitable for

PPG measurement circuit.

3.4.3. In-Ear Module/Headphone Interface

A new method is used for continuous measurement of the pulse wave in the outer ear.

The requirements for the desired equipment are still at a very early stage and a lot of

improvement has to be done to make the system less noisy. But for initial measurements

in-ear headphones available in the market are used. This becomes the sensing part of the

circuit which not only helps capturing the change in pressure inside the auditory canal

48

3. System Design

due to pulsation of blood vessels but also isolates the ear from external noise. Moreover

ear phones are also cheap, comfortable and widely available in the market and widely used

with commercial mp3 players. For the measurement different headphones from different

manufacturers are used.

Concerning the medical safety of the in-ear headphones no concrete research has been done

which will prevent the long term use. But one of the research points out an increase in the

number of bacteria flora after the use of headset for an hour [6]. But this could be because

of the change in pressure and humidity inside the ear after the application of headphones.

Moreover in this research paper no relationship between the increase in the number of

normal flora and external ear infections is stated. But it can be easily prevented by using

normal disinfectants used for hearing aids before using the in-ear headphones for longer

periods.

In humans the audible range of frequencies is usually 20 Hz to 20 kHz although this varies

from person to person and normally there is decrease at higher frequencies at an older

age. Moreover we are interested in signals which are in the frequency range of 1 Hz1 to

around 40 Hz. The headphones frequency response in this particular range is unknown

and pose a problem in calculating the transfer function. The basic block diagram of the

In-Ear system is shown in Figure 3.12 for better understanding. Sensing part consists of

an in ear headphone which is in turn connected to an amplifier and then connected to a

servo feedback amplifier, to remove the DC offset. Finally the signal is passed through a

low pass filter and fed to ADC present in the system.

160 Heat beats per minute

49

3. System Design

SERVO FEEDBACK HIGH PASS FILTER

LOW PASS FILTER

SENSING PART SHOWING HEADPHONE

ADC

AMPLIFIER

Figure 3.12: Block Diagram of In-Ear system

The hardware implemented for this particular system is explained below in detail starting

with the amplification stage followed by a servo feedback filter in figure 3.13.

InEarLeftUnfiltered

5P0_Analog

5N0_Analog

5P0_Analog

5N0_Analog

G=-500, fg=2kHz

fg=15 mHz

G=-150, fg=2kHz

R1133 499k 1%R1133 499k 1%

C113147p/50V NP0C113147p/50V NP0

R1134 1k00 1%R1134 1k00 1%

R1129150k 1%R1129150k 1%

R1132 150k 1%R1132 150k 1%IC1203AOPA4134/SO-14IC1203AOPA4134/SO-14

+3

-2

V+

4V

-11

OUT1

X1103

STL-02

X1103

STL-02

11

22

C1135 150p/50V NP0C1135 150p/50V NP0

R1128 10M0 1%R1128 10M0 1%

R1130 150k 1%R1130 150k 1%

C1130 47p/50V NP0C1130 47p/50V NP0

IC1202AOPA2735/SO8IC1202AOPA2735/SO8

+3

-2

V+

8V

-4

OUT1

C1132 10u/25V X5RC1132 10u/25V X5R

R1131 1k00 1%R1131 1k00 1%

IC1203BOPA4134/SO-14IC1203BOPA4134/SO-14

+5

-6

OUT7

R1135 0RR1135 0R

Figure 3.13: In-Ear Circuit Diagram

For amplification purposes OPA 2735 is used which has a zero drift amplifier with max-

imum drift of 0.05 µV/10 C. This is a special type of amplifier which uses auto-zeroing

technique to simultaneously provide low offset voltage (5 µV max) and near-zero drift over

time and temperature. Low offset voltage is needed because of high amplification of 500,

normal op amps have a offset of 1 mV which would lead to 500 mV of DC error. The re-

quirement for such an IC is there because the signal is very small in amplitude and could

be completely destroyed due to the noise caused by a normal operational amplifier. The

50

3. System Design

gain for this particular stage was set to 500 as the In-Ear signal has a very small amplitude

of few mV.

OPA 2735 produces an offset in the input signal as the signal from the pulse wave inside the

ear is of very small amplitude thus to remove this offset a servo filter which was previously

used for PPG measurement is employed with a gain of 150 which could be easily set by

using the formula mentioned in the equation (3.6) and changing the value of resistor R

1105 in Figure 3.13.

As the in-ear signal is in the frequency range of 1 Hz to 40 Hz and to compensate motion

artifacts a band pass filter is also implemented in which a high pass filter is located in the

servo feedback filter and a 5th order Sallen-Key filter with Bessel characteristic similar to

the one used in PPG circuit is used. The circuit diagram of the filter is shown in Figure 3.8

and Figure 3.10.

3.4.4. Impedance Cardiometry Module

The ICG is the measurement of change of conductance in the body over the thoracic re-

gion. This change in conductance is caused by the change in the volume of blood across

the body due to the pumping action of heart. Therefore the basic architecture of the im-

pedance meter [21] [22] implemented in the work group was further improved to calculate

the SV and CO. The basic principle of the measurement is explained in detail in the previ-

ous section 2.5. The basic requirement for the system is excitation frequency in the range

10 kHz to 250 kHz. The injected maximum current should not exceed 10 mA for the fre-

quency range till 100 kHz, this is according to DIN EN 60601-1 standards.

This system consists of FPGA SoC where the excitation waveform is generated via Direct

Digital Synthesis (DDS). The DAC used is 16 bit, 50 Mega Samples Per Second (MSPS) in

combination with an AD8130 based voltage controlled current source [4]. To increase the

current accuracy, the injected current is measured over a shunt resistor on the low side of

the impedance under test. The data is acquired with a 14 bit, 25 MSPS dual ADC (LTC2296

from Linear Technology) in combination with a PGA for maintaining an optimal Signal to

Noise Ratio (SNR) [21]. The block diagram with different parts is shown below in Figure

3.14.

51

3. System Design

FFT & Averaging

SIGNAL GENERATOR(DDS)

Downsampling

Control logic PC

PGA

PGA

PGA

DRIVER

UI

CURRENT SOURCE

FPGA

SUBJECT

DRIVER

Electrodes

Electrodes

R

DAC

ADCD

ADC

Figure 3.14: Block Diagram of Cardiometry Module

Few modifications are made to the previous version 1.01 of impedance meter [21] de-

veloped in the work group so only the changes are the new developments related to the

system are described, for the rest of details please follow these references [21] [23] [2] in

which detail of different parts of the impedance meter are explained by different authors.

One of the new developments done is to improve the AD8130 (Analog Devices) based

current source further, because it suffered from ringing, noise and had lot of components.

Therefore the previous design is modified to mitigate the effect of noise and ringing [4].

To make the current source more stable the DC compensation is optimized. Moreover the

feed-forward correction for stray capacities was removed, as it was difficult to control. A

simplified explanation for the current source is explained in Figure 3.15.

52

3. System Design

Input Voltage Offset voltage Output voltage

Shunt resistance

AD8130

+In

-In

1

2

3

4

5

Load resistance Load current

Figure 3.15: AD8130 based Current Source without any offset voltages

In the following the transfer function of basic and the new current source is derived. Ac-

cording to the data sheet of the AD8130 output voltage of the feedback op amp is equal

to sum of input voltage and offset voltage, when there is a feedback from output to pin 4

of the IC as shown in Figure 3.15. To use the IC as a current source, the output is connec-

ted via the shunt resistor (RS) to pin 3 as shown in the Figure 3.15 [1]. The voltage drop

across the shunt resistor is equal to the offset voltage (Vo f f ). The output of the current (IL)

is independent of the load resistor (Rs) therefore:

Vout −Vo f f = Vin ∧Vout −Vo f f = Rs · IL (3.11)

IL =Vin

Rs(3.12)

This calculation holds true when no input-offset voltages are prensent. With input offset-

voltages (V1 and V2) the load current equation will become:

53

3. System Design

IL =Vin

Rs+

V1

Rs+

V2

Rs(3.13)

according to the datasheets input offset-voltage values this could lead to an DC current

big as 38 µA. To overcome this effect an integrator circuit is used to feedback the DC offset

voltages. As indicated in Figure 3.16.

Input Voltage Offset voltage Output voltage

Load resistance Shunt resistanceLoad current

AD8130

R

C

+In

-In1

2

3

4

5

Figure 3.16: AD8130 based Current Source

The integrator is used to remove the DC offset and is connected to the negative pin of the

AD8130. It safes the use of another op amp to invert the signal. For integrator a OPA743 is

used which is a rail to rail operational amplifier with 7 MHz of bandwidth [51].

A simulation for the new current source in comparison to the old current source is shown

in Figure 3.17. As it can be visualized that the output impedance is much lower for smaller

frequencies in the improved current source. The output impedance is not at all important

54

3. System Design

for these particular frequencies as these particular frequencies are not in our application

range. The improved current source is very easy to implement as it requires much lesser

components and does not have the feedforward capacitor [4], which was very tedious to

control.

Old Current Source Improved Current Source

ΩO

hm

/

Figure 3.17: Simulation of output Impedance of the old and new current source

3.5. Digital Part

As it is clear from the block diagram in Figure 3.1, there are two different ADC for digit-

izing signals. One is exclusively being used for the cardiometry module and the other is

being used for ECG, PPG and In-Ear modules. In the following section the circuit design

and schematics for the ADC and AFE are explained further. After the data is acquired and

filtered inside the FPGA it is transmitted via USB to the host PC.

3.5.1. Analog Front End (AFE)

To synchronously digitize the analog signals from the four PPG channels, the In-Ear mod-

ule, and the ECG an AFE (ADS1298, Texas Instruments) with 24 bit resolution is used.

The AFE consists of 8 channels inclusive multiplexer, PGA’s, ADC’s, control unit and

Serial Peripheral Interface (SPI) for easy connection. The block diagram of the ADS1298

is shown below in Figure 3.18. Furthermore the AFE contain some more features such as

DRL, Wilson central terminal for chest leads and augmented leads1.

1But in the current thesis these modules are not utilized.

55

3. System Design

MUX

PGA

CONTROL

SPI

OSCILLATOR

Wilson Terminal

RLD

PGA

PGA

ADC

ADC

ADC

Inputs

CLK

SPI

Figure 3.18: Block Diagram of ADS 1298 based on [47]

In this thesis the approach is to use a programmable gain amplifier with a very high resol-

ution ADC (24 bits) which could simultaneously convert different analog to digital signal.

As due to different modules present in hardware it is very important that all the psycholo-

gical signals must be digitized with a fixed time relation.

The ADS1298 is an impressive device with 24 bit resolution with low latency, high speed,

and good noise performance in combination. The ADS1298 provides 32 kSPS per channel

with 21.6 effective bits, making it an ideal fit for ECG applications [47]. By using this

particular AFE a lot of hardware for the high-pass filter, DC blocking filter, gain stage, and

a steep, active low-pass filter are eliminated. In addition it also relaxes the need for anti-

aliasing filters in the front of ADC as it contains a Delta-sigma ADC’s. The noise of the

ADS1298, when referred to the input of the system, gives 1 mVRMS to 3 mVRMS depending

on the output data rate, with a PGA gain of 4.

3.5.2. Field Programmable Gate Array (FPGA)

To generate excitation waveform for cardiometry module a DDS is used. The DDS is highly

flexible and allows arbitrary excitation waveforms such as sinusoidal, chirp, rectangular,

56

3. System Design

etc. The digital signal is afterwards converted into a current with a 16 bit, 50 MSPS DAC

(LTC1668 from Linear Technology), in combination with an AD8130 (Analog Devices)

based voltage controlled current source, see section 3.4.4. To allow a higher complexity

of the FPGA logic, the size of the FPGA in terms of Look-up Table (LUT)’s and Pins was

enhanced with respect to Version V1.01. by the factor of two.

LatticeXP2 devices have a LUT based FPGA architecture with non-volatile flash cells. This

approach provides numerous benefits like instant-on, infinite reconfigurability, on chip

storage with embedded block memory and Serial TAG1 memory and design security. The

LatticeXP2 FPGA fabric was optimized to have a very high performance and low cost in

mind. LatticeXP2 devices include LUT-based logic, distributed and embedded memory,

Phase Locked Loops (PLLs), pre-engineered source synchronous Input/Output (I/O) sup-

port and enhanced Digital Signal Processing (DSP) blocks [28]. The block diagram of the

basic architecture is shown in Figure 3.19. The major parts are Programmable Function

Units (PFU)s, SPI port (for programming), Random Access Memory (RAM), Flash and

Joint Test Action Group (JTAG) Port for programming and debugging.

1The area in an L2 cache that identifies which data from main memory is currently stored in each cache line.

57

3. System Design

On-chip

Oscillator

Programmable

Function Units

(PFUs)

SPI Port

sysCLOCK PLLs Flexible Routing

Flash

JTAG Port

sysIO Buffers,

Pre-Engineered Source

Synchronous Support

sysMEM Block

RAM

DSP Blocks

Figure 3.19: Block Diagram of the LFXP2-FPGA from Lattice Semiconductor [28]

As design software (Lattice Diamond) is used for efficient implementation of the Very

High speed Hardware Description Language (VHDL) based firmware of FPGA devices.

It handles the complete synthesis process for the FPGA and enables quick programming

and easy debugging. To achieve a good system performance the FPGA is clocked with an

100 MHz external oscillator.

3.6. Power Supply

Power integrity is a very important aspect of circuit design, especially for the high per-

formance of the measurement system. A good design must ensure low noise levels and a

constant and accurate power supply in order to maintain all the parts working within their

optimal parameters. The block diagram of the power supply is shown in Figure 3.20.

58

3. System Design

Medical Power Supply

LT3479

LT3640

TPS73201

5P0 5N0_Analog

3P3

1P2

5N0_Analog

TPS723012N5 Analog

2N5 Analog

FPGA

FPGA

FPGA and ADS 1298

5P0_Analog

FPGA and ADS 1298

Switch Mode Power Supply

Switch Mode Power Supply

Low Drop Out regulators

Low Drop Out regulators

5P0_Analog

Figure 3.20: Block Diagram of Power Supply of the Impedance Meter Version V1.10

A +5V is supplied to the board with a medically approved power supply according to DIN

IEC60601-1. This is fed through a polarity checking circuit. Furthermore it is connected to

an EMI filter beads which removes disturbances (high frequencies) from the power supply

to enable the aimed accuracy as well acts as a fuse and hence protecting the circuit board

from high voltage surges. The schematics for this part can be found in section A.

Moreover to make the power supply more stable and to remove the high frequency spikes

which acts as noise in the circuit, π-filters are implemented. The cutoff frequency of such

filter is set to 10 kHz approx. This +5V is fed to a fixed frequency DC/DC converter (LT3479

from Linear Technology) with an efficiency of about 89 %.

3.7. Layout of the Printed Circuit Board (PCB)

This section presents some basic guidelines which were kept in mind while designing the

PCB board.

59

3. System Design

3.7.1. Part Placement

Part placement is one of the first and most essential aspect of a good PCB design, because

a good placement makes the layout job easier and give the best electrical performance.

It is very essential to place parts in groups according to their functionality, this not only

provides a neat and clean placement but also makes routing easier. Therefore board zoning

is done with high-speed logic placed close to the power supply, with slower components

located farther away, and analog components even further (to reduce EMI effects). With

such a kind of arrangement, high speed logic has less chance to interfere other signal traces.

For example in the case of oscillator it is made sure that it is located away from the analog

circuit, low speed signals and connectors. A four layer PCB which consisted of a top and

bottom layer and in between ground plane and a power plane which are further divided

into different power planes according to the need is employed. As inductance increases

with increasing length and decreases with increasing width of the conductor and it is very

essential that power and ground should be directly over each other, which not only reduces

impedance and minimizes loop area [48] [35].

The power supply which has the ability to produce maximum electrical instability is placed

in one corner and is marked in black. Star configuration which originates from a single

point and all traces originating from this point are of the same length is maintained when

placing the IC’s of power supply making it more stable in terms of electrical performance.

All the digital components are placed on one part of the PCB as shown in Figure 3.21 and

marked with blue.

60

3. System Design

Analog part Connectors Power Supply

ADS 1298 Digital part FPGA

Figure 3.21: Placement strategy for the PCB

The analog part and the connectors for connecting the subject to the device were placed

in the bottom half of the PCB board and the ADS 1298 is placed in the centre of the board

as it should be as close to the FPGA and the analog part of different modules. The analog

61

3. System Design

section is marked in green and the connectors are marked in red. The ADS1298 is marked

in purple in Figure 3.21.

3.7.2. Routing

Nets are kept as short as possible. The longer the track’s length, the greater is the induct-

ance, capacitance and resistance. All the tracks have angles of 45 it does not remove EMI

but make the overall design look aesthetically good and reduces the chance of invisible

trace cuts [19].

Multiple vias are used whenever possible especially under some IC’s where ground plane

was located. This reduces ground impedance, therefore improves the reliability of the

circuit.

Power tracks and other critical tracks are made as big as possible for better electrical per-

formance. As you can see from the Figure 3.21 symmetry in both tracking and component

placement is maintained which gives the board a professional aesthetic point of view. The

Figure 3.22 shows the complete PCB board with the assembly top and silk screen. The

dimensions of the PCB are 134 mm×144 mm. The board contains over 700 components.

62

3. System Design

Figure 3.22: Manufactured Prototype of the IM V1.10 with over 700 components

Digital-Analog Split: A proper grounding is very essential part of a good PCB design.

First and foremost important thing is to dedicate a plane to the ground and then to split

the ground in analog and digital ground. It is very essential to separate analog ground

from the noisy digital ground as this can eventually decrease electrical performance of the

PCB.

63

4. Software and Testing

Biological signals convey information, which may not be perceived just through visual

interpretation but may be hidden in the signal. This information has to be extracted or

displayed in a proper manner before they can be interpreted by the health givers. This de-

coding is sometimes very simple and can be done visually by just looking at the computer

screen or print out of the signal obtained. But sometimes due to the complexity of the

signal there is a need of biosignal processing, which is a vital tool for extracting clinically

significant information from measured signal.

One of the paramount reasons to use biosignal processing is to automate the signal ana-

lysis. Visual interpretation is time consuming and has a high probability of human error

depending upon the complexity of the signal. So far the research in the biomedical signal

processing is concerned with the analysis of one particular signal type at a time (uni-modal

signal analysis) [43]. The signal processing in this research work also focuses on the same

analysis.

The main objectives for using biosignal processing in the current project is described below:

Feature Extraction: It helps to characterize and understand the information present in

the signal. Moreover it reduces manual subjectivity in the analysis of a biomedical

signal. For example a small variation in the ECG signal when analysing heart rate are

very tedious to pinpoint and measure, but contains decisive information about the

physiological condition of the patient. In this thesis feature extraction is employed

to extract some useful sites like different peaks in ECG, PPG, In-Ear measurement

and cardiometry measurements. As well as detecting HRV and respiration rate of

the subjects.

Noise Reduction: Recorded Signals are corrupted by various kinds of noises which

either should be removed or mitigated so that the signal can be analysed with an

ease. For example to remove the 50/60 Hz noise which is inherent in the power-

line. Sometimes the signal is superimposed with so much noise, that it is nearly im-

possible to visualize the signal without any denoising. For denoising signals in the

current work wavelet analysis is employed. The concept behind the wavelet analysis

is already explained in section 2.7.

64


There are basically three major clinical concepts, in which algorithms for biomedical signal

processing are designed - diagnostic, therapeutic and monitoring. For the current thesis

the algorithm is based on diagnostic type.

In Diagnostics there is a need to identify the patient’s condition and to make a prognosis

depending upon the biomedical signal. A diagnostic decision rarely requires immediate

availability of the results from signal analysis, but it is usually acceptable to wait a few

minutes for the analysis to be completed. Hence, signal analysis can be done off-line on

a personal computer, thus relying on standardized hardware and operating system, pos-

sibly supplemented with a DSP board for accelerating certain bottleneck computations.

Algorithms for biomedical signal processing do not define the entire diagnostic computer

system, but their scope ranges from performing a simple filtering operation to forming a

more substantial part of the clinical decision-making [43]. Our system deals with such kind

of measurements and with appropriate efforts a real time system could also be implemen-

ted but its not in the focus of our current research.

4.1. Principle Firmware Buildup

This section describes the interface software between the embedded system and the PC.

The control of the whole system is done by the host PC, which is responsible for the transfer

of commands as well as data between the PC and FPGA. The software development of the

system was whole heartedly done by the research group in the lab. Just for explanation

purpose a small description is provided. The block diagram consisting of different parts is

shown in Figure 4.1.

65


FIFOμC

DDS

PGA

USB Interface

Test Data

FFT Module

MUX and Packet

Building

ADC

AFE

DAC

C

VHDL

PC

DAC Digital to Analog Convertor PGA Programable Gain Amplifier ADC Analog to Digital convertor

AFE Analog Front End μC Microcontroller MUX Multiplexer

FIFO First In First Out DDS Direct Digital Synthesis

FPGA

Figure 4.1: Firmware software describing linkage between different blocks in FPGA

The microcontroller present inside the FPGA is the control unit and is responsible for con-

trolling all other units such as ADC, DAC, PGA, AFE, DDS, FFT module, as well as test

data sequence (for testing the proper communication between FPGA and PC). A multi-

plexer is connected to a First In First Out (FIFO) to write data which could be then passed

to a PC via a USB interface.

FIFO is also responsible for buffering instructions send to the µc from the host PC. A mul-

tiplexer is directly connected to ADC and AFE to transmit data from cardiometry module

and analog module (consisting of ECG, PPG and In-Ear module) via FIFO. This data is

further transmitted to PC for further analysis of the signals through MATLAB. The pro-

gramming language for the microcontroller is C and for other units is VHDL.

4.2. Wavelets

Wavelet analysis has a number of applications such as analysis, feature extraction, com-

pression of signals, denoising and many more. In the current thesis the wavelet analysis

is used for denoising, as well as to remove the base line wandering from the physiological

signals.

66


This is easily illustrated in Figure 4.2. The upper part of the Figure 4.2 contains the PPG

signal with baseline wandering due to the respiration of the subject. The line marked in red

is obtained through decomposition and then reconstruction of the original signal at level

12 (A12). Then this signal is subtracted from the original signal and the result obtained is

shown in the second graph. This method is further employed to denoise and remove the

base line wandering from all the other signals being analysed in this thesis.

0 10 20 30 40 50 60-0.1

0

0.1Smoothed and Baseline wandering removed signal

t\(s)

Am

plitu

de

0 10 20 30 40 50 60-0.3

-0.2

-0.1

0

0.1

0.2Original PPG signal with Baseline Wandering

t\(s)

Am

plitu

de

PPG SignalApproximation A12

Figure 4.2: Denosing and Baseline wandering removal through wavelet in MATLAB

The removal of base line wandering is further employed in calculating the respiration

rate of the patient over the complete measurement time. This is achieved by using sig-

nal marked in red and calculating peaks. The number of positive peaks is equal to the

number of respiration taken by the subject.

The realization of the code for using wavelet analysis is simple. The signal is filtered to

a certain level depending upon the data by using the inbuilt wavelet function wavedec in

67


Mathwork MATLAB. This inbuilt function gives the user ability to select the wavelet, as

well as the level of decomposition. Then the signal is reconstructed at every level by using

a wavelet function called wrcoef.

The reconstructed wavelets are reused to denoise the signal and to remove the base line

wandering. As the wavelet is being used numerous times, therefore four functions in MAT-

LAB are implemented namely Remove_BLW_ecg, Remove_BLW_ppg, Remove_BLW_inear and

Remove_BLW_impedance. These functions have the capability to denoise and remove the

base line wandering by selecting different wavelets as well as the level of decomposition.

As the name suggests different functions are implemented for every signal.

4.3. MATLAB Software

This section describes about the processing of the data and display of the data in MATLAB.

The data is acquired through the USB and Impedance Meter Board (IMB)_V110 board inter-

face and then loaded into MATLAB. This data contains all the signals- ECG, PPG channels,

In-Ear channels and impedance cardiometry.

First of all the signals are denoised and base line wandering is removed through wavelet

function as explained in section 4.2. This baseline wandering removal and denoising is

tailored for each signal. Afterwards the signals are processed according to different needs

and to make it relevant to understand. For example for obtaining the impedance cardi-

ometry signal the impedance is differentiateddZdt

to get the required waveform as shown

in Figure 2.20.

After the required signal processing the peaks and valleys are calculated for every signal

and stored in a separate matrix for further calculations. For example in ECG signal the R-

peaks are detected, and then the amplitude and the time reference are stored in a different

matrix.

PAT values are calculated for every channel of PPG at different locations as well as for the

In-Ear signal from both the ears. PAT values are calculated on the basis of Figure 2.11. The

SV and the CO are calculated according to the equations written in section 2.5.

All the waveforms and the results are displayed in the appropriate figures generated through

MATLAB. A simple flow chart regarding the software is shown in Figure 4.3.

68


Denoise and remove Base-Line wandering

Signal Processing

Import data from IM_V1.10

(Embedded System)

Calculate Peaks and valleysStore Peaks and

valleys in a different Matrix

Display the waveforms and various other calculations

CalculatePulse Arrival Time(PAT)

Respiration Rate(RR) Pulse Wave Velocity(PWV)Heart Rate Variability(HRV)

Stroke Volume(SV)Cardiac Output(CO)

ETC.

Store the data in excel sheets

Headphones

PPG SENSOR

Current ElectrodeVoltage Electrode

ECG electrode

Figure 4.3: Flow chart for the MATLAB software

This is the general flow of program for all the waveforms but some specific processing

is done for cardiometry and In-Ear waveforms. Moreover for peak detection software

uses a special MATLAB function called findpeaks and findvalleys. The parameters for this

particular program has to be highly varied by seeing the initial waveforms. The parameters

which are generally varied are to set the threshold and the number of intervals on which

specific peaks are detected. Thresholding is very important as due to the morphology of

the biomedical signals there are various maxima and minima in the waveform and hence

multiple peaks are detected. But for PAT calculations this will cause error in calculation

and hence has to be avoided. Peak interval is also important as sometimes the acquired

signal has a lot of noise and hence it leads to detection of more than one peak. Therefore

by setting this parameter only one peak is detected at one maxima.

69


For analysing the results obtained through the device three specific programs are written

to provide freedom and independence to calculate and display different waveforms. The

first program IMBV_110.m acquires and displays all the signals in one window both un-

filtered and filtered waveforms. Moreover it displays all the waveforms with all the peaks

detected.

The second program STROKE_VOLUME_CALCULATOR.m just takes the impedance sig-

nal and calculates the SV and CO and displays the results in the command window of the

MATLAB. This is done as the cardiometry (i.e. SV and CO of the heart) is a completely

different waveform in relation to the PPG and In-Ear waveform.

The third and the final program PAT_CALCULATOR_V2 computes the PAT values for all

the channels of PPG and the In-Ear waveform. The program exports the beat by beat PAT

values to an excel sheet. And the program displays the average PAT values over a num-

ber of waveforms in the command window. This program is quite flexible and user can

calculate the PAT for one, two or all the waveforms together and the spreadsheet provides

a good comparison between the different PPG waveforms acquired at different physiolo-

gical sites. HRV and number of respirations are also calculated using the above program.

The beat to beat data obtained is stored in appropriate excel sheet.

4.4. Requirement Verification

All hardware requirements are tested either through simulations or via functional verific-

ation. The requirements for bandwidths and stopband attenuations were simulated via

LTspice. There is a group delay because of various filter stages in the frequency range of

30 mHz to 10 Hz, but the acquired PPG signals are in the range of few Hz where the group

delay is quite constant. Based on this it is assumed as a constant while calculating PAT and

PWV values. The group delays are around 30 ms for PPG and 10 ms for ECG. As in the

In-Ear module the same filter configuration is used as in the PPG, hence the group delay is

equal to 30 ms.

The evaluation of the software is based on signal analysis, obtained through measure-

ments. The signal phase changes, because of the digital filters, are checked via the MAT-

LAB wavelet toolbox before implementing it into the main programs. The peak detection

is checked via a visual inspection of the results.

70

5. Results

In order to show that the device functions properly, various measurements are made on

different subjects not only to display the waveform but also to make specific measurements

related to various physiological parameters. As mentioned earlier for analysing the results

obtained through the device three specific programs are written to provide freedom and

independence to calculate and display different waveforms.

5.1. Development of In-Ear Measurement System

For preliminary studies a simple commercially available in-ear headphone was connected

to an amplifier with a very large gain and signal was analyzed using Mathwork MATLAB

[54]. The in-ear headphones operate in the frequency range of 20 Hz to 20 kHz and has an

almost constant transfer function in this particular range. But the related pulse wave signal

present in the ear is less than 20 Hz. As the headphones are used below their intended

frequency range the exact trend of its transfer function is unknown and the waveform’s

morphology may be deformed. Moreover the sensitivity is bad, because it is based on

the induction law and is therefore frequency dependent. To compensate the inductive

behavior of the headphone, the signal needs to be integrated.

To further evaluate this signal, instead of headphones a small better suited pressure sensor1

from First Sensor GmbH is utilized. Pressure range is about ±1000 Pa (±10 mbar). The

pressure sensor is very lightweight and small sized therefore it can be mounted in front of

the ear.

As explained earlier to acquire this signal the sealing of the auditory canal is very essential.

To make the proper sealing and to provide comfort to the patient an otoplastic silicone

based compound is used. The compound is an optically clear, non-toxic, non-inflammable

flexible material, well known for auditory prosthesis applications. By using this compound

a mould of the ear could be manufactured in few minutes, which is a custom fit for each

and every subject.

1HCEM010-DBE85P, First Sensor GmbH, http://www.sensortechnics.com

71

http://www.sensortechnics.com

5. Results

PPG Sensor

Tube

Ear Mould

Pressure Sensor

Figure 5.1: The hybrid sensor consisting of in-ear pressure and PPG sensors [20]

To take the analysis to one step further a PPG sensor is added and simultaneous measure-

ment is done with pressure and PPG. This particular hybrid sensor consisting of both the

pressure and PPG sensor is shown in Figure 5.1. Results of this particular study will be

in-proceeding at DGBMT/ÖGBMT/SGBT annual conference 2013 [20].

5.2. Standard Measurement Setup and Study Outline

The measurements are taken on different physiological locations. These measurements

are made by using two electrodes for ECG one of them is placed on the upper chest and

the other one is placed obliquely to the first one, below the heart. This is the usual con-

figuration followed for every ECG measurement. For the impedance measurement the

configuration shown in Figure 2.19 but instead of band electrodes, normal ECG electrodes

are used. The PPG sensors can be placed upon a number of locations to get morpholo-

gically different signals but in this particular measurement the locations are index finger

and temple region of the body. The positions of different electrodes and sensors are shown

in Figure 5.2 for a better understanding. The crucial part for the headphones is that they

should fit perfectly in the subject’s ear, so there is a proper sealing.

72

5. Results

Headphones

PPG SENSOR

Current ElectrodeVoltage Electrode

ECG electrode

Figure 5.2: Standard measurement setup

Initially the measurements are done on three healthy subjects. General body parameters of

the subject’s are shown in Table 5.1. The cardiometery distance means the distance between

the outer electrodes and the error estimated in this measurement is around ±2 cm.

S.No Age Sex Weight Height Cardiometry Distance

1 25 M 72 kg 170 cm 44 cm ± 2 cm

2 29 M 130 kg 188 cm 55 cm ± 2 cm

3 36 M 83 kg 194 cm 60 cm ± 2 cm

Table 5.1: General body parameters of the healthy subjects

5.3. Signal Acquisition and Filtering

Figure 5.3 shows the data obtained directly through the IMB_V110.m i.e. it contains the

data from all the channels of AFE as well as from the impedance measurements. As it is

clearly evident from the data that it is highly afflicted by base line wandering which is

due to the continuous breathing of the patient. The signal also has noise which should be

removed before any peak detection or calculations can be made. This particular data was

acquired for 60 seconds from subject 1 and contain measurements for ECG, four channels

of PPG, two In-Ear measurements through headphones and thorax impedance.

73

5. Results

Through the use of wavelet analysis denoising and base line removal is achieved and the

filtered data are shown in Figure 5.4. The difference in the morphology for the PPG wave-

form can be easily interpreted by comparing the signal present on channel 2 and channel

3 which looks similar and channel 6 and channel 7 are similar too. On the other hand the

PPG signals at channel 2 and 3 are completely different to the signal acquired at channel

6 and 7. This is only because of the change of location due to which the reflected wave

changes inducing an overall change in the morphology. A zoomed view of the filtered

signal for the duration of 10 sec is shown in Figure 5.5.

74

5. Results

010

2030

4050

60-16

-14

-12

-10 -8 -6 -4 -2 0 2

t / s

Relative Amplitudes

E

CG

Left Ear

Right T

emple

In-Ear Left

In-Ear R

ightLeft trigger finger tipR

ight trigger finger tipT

horax impedance

Figure 5.3: Unfiltered signals obtained through IMB_V110 for subject no.1

75

5. Results

010

2030

4050

60-16

-14

-12

-10 -8 -6 -4 -2 0 2

t / s

Relative Amplitudes

EC

GLeft T

emple

Right T

emple

In-Ear Left

In-Ear R



horax impedance

Figure 5.4: Filtered signals after removing base line wandering and denoising through wavelet ana-

lysis in MATLAB

76

5. Results

1819

2021

2223

2425

2627

-16

-14

-12

-10 -8 -6 -4 -2 0 2

t / s

Relative Amplitudes

EC

GLeft T

emple

Right T

emple

In-Ear Left

In-Ear R



horax impedance

Figure 5.5: Filtered signals for a period of 10 seconds

77

5. Results

5.4. Stroke Volume and Cardiac Output

One of the major tasks of the thesis is to calculate the SV and the CO of the heart. For this

purpose the impedance signal over the thorax is acquired and then the differentiated signal

is used for calculations. As mentioned earlier in the text in section 2 and shown in Figure

2.20 dZdt as well as Z are estimated through a change in impedance curve. The sampling

rate for ECG is 813 samples per second and for impedance is 466.15 samples per second.

The current administered is 5 mA and the frequency is about 48.8 kHz. The Figure below

shows the impedance signal as well as the differentiated signal with all the peaks detected

in MATLAB.

10 11 12 13 14 15 16 17 18 19 20

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

t / (s)

Am

plit

ud

e/(O

hm

)

FILTERED IMPEDANCEFILTERED IMPEDANCEFILTERED IMPEDANCEFILTERED IMPEDANCE

11 12 13 14 15 16 17 18 19 20

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

t /(s)

Am

plit

ud

e/(O

hm

/s)

dZdZdZdZ


lysis in MATLAB

78

5. Results

The formula used for calculating the SV is mentioned in the equation (2.7) instead of ejec-

tion time te Left Ventricular Ejection Time (LVET) is used. LVET varies from individual to

individual but for these particular calculations it is assumed to be constant and is taken as

250 ms [8]. Moreover ρ is also a constant and the value is assumed to be 100 Ω cm [56].

The CO can easily be calculated by using the formula :

CO = SV × Heartrate (5.14)

The units for cardiac output is liter/min and the average value is between 4.0-8.0 l/min

[30]. The heart rate is calculated via detecting R-peaks in the simultaneous ECG or through

the peaks detected in the impedance cardiometry signal. The SV and CO obtained via

this equipment on the three subjects are shown in Table 5.2 below. As it can be easily

concluded that the values are not in the normal range. By evaluating the equation 2.7, the

length between the outer electrodes as well as hematocrit level plays an prominent role in

calculation. A slight error in these two parameters leads to an error in the final SV values.

Assuming a systematic error of ± 10% in every parameter, the calculated maximum error

with partial derivation is 30%.

Moreover LVET values can also be derived from the impedance curve by acquiring ECG

simultaneously and taking RR peak as a landmark and then calculating te as shown in Fig-

ure 2.20. But the results obtained lack the precision as the negative peaks are not detected

accurately. But for further work this program is also attached in the appendix section A.

S.No Subject 1 Subject 2 Subject 3

Heart Beats 68 BPM 72 BPM 72 BPM

Stroke Volume 112 ml ± 11 ml 109 ml ± 11 ml 152 ml ± 15 ml

Cardiac Output 8.1 l/min ± 0.7 l/min 7.5 l/min ± 0.7 l/min 10.2 l/min ± 1.0 l/min

Impedance Change 3.4 mΩ/sec 2.2 mΩ/sec 2.6 mΩ/sec

Table 5.2: Estimated Stroke volume and cardiac output from different subjects

79

5. Results

5.5. Pulse Arrival Time (PAT) and Pulse Wave Velocity (PWV)

The other task was to calculate the PAT values and to provide some information in finding

the PWV. For this PAT_Calculator.m is used. The input for this program is the PPG from all

the channels as well as the ECG signal. The signals are denoised and the peaks are detected

for every PPG signal as well as for the ECG signal. One of the PPG signals and ECG signal

with the peaks detected are shown in Figure 5.7.

33 34 35 36 37 38

-0.05

0

0.05

0.1

0.15

0.2

0.25

t /(s)

Am

plit

ud

e

ECG and PPG


lysis in MATLAB

After the peak detection, the corresponding time of each and every peak in a PPG is sub-

tracted from the corresponding time of RR peak to get beat by beat PAT values . Then the

average PAT value is detected over the complete recording. For easy visualization and to

save the data properly an excel sheet is generated.

The unfiltered signal can also be used for calculating the respiration rate of the subject. The

base line wandering removed from the signal is used for this calculation. As shown in the

80

5. Results

Figure 5.8 the first signal is a normal unfiltered PPG signal obtained via measurements. As

its clearly evident the signal has a uniform low frequency like sinusoidal present superim-

posed with PPG signal, which is indeed the number of respirations taken by the subject.

As shown earlier this signal can be easily removed using wavelet analysis. The removed

signal is shown in the second part of Figure 5.8. This signal is used to calculate the number

of respirations by detecting the number of peaks and displaying it. As it can be easily seen

from the Figure 5.8 the number of peaks are eight, hence the number of respirations is eight

for the 60 second measurement.

0 10 20 30 40 50 60-0.3

-0.2

-0.1

0

0.1

0.2

t/(s)

Am

plitu

de

PPG SIGNAL

0 10 20 30 40 50 60-0.4

-0.2

0

0.2

0.4

t/(s)

Am

plitu

de

RESPIRATION CURVE


lysis in MATLAB

81

5. Results

For analysis purpose the test is performed on all the three subjects and the average PAT

values and number of respirations obtained via PPG signal are shown in Table 5.3. As

explained earlier, there is a group delay of about 5-10 ms in ECG and a delay of about

30 ms in PPG. So while calculating the PAT values this group delay is taken into consider-

ation. And the maximum uncertainty is estimated to be around 10% while calculating PAT

values. Normally the PAT values are in range of 244-267 ms.

S.No PAT1 PAT2 PAT3 PAT4 Number of Respirations

Subject 1 175 ms ± 17 ms 140 ms ± 17 ms 211 ms ± 21 ms 232 ms ± 23 ms 10



Table 5.3: PAT values of different subjects

This program can also be used to calculate the PAT values from the signal obtained through

the In-Ear sensor. Some of the PAT values obtained are shown in the Table 5.3. But the data

obtained from In-Ear measurements is very noisy (lack of proper fitting of the headphones

in the subject’s ear) due to which an efficient peak detection is very difficult. Hence there

is a high risk of error in calculating the PAT values. As it could be interpreted that the PAT

values are different at different physiological sites.

But the difference between the PAT1 and PAT2 is very small as both the locations are right

and left fingers. This is due to anatomical position of the heart which is located a bit to the

left side. The same reason applies to the left temple and the right temple. It has also been

found that the position of the photodiode sensor and infrared emitting light source plays

a crucial role in the amplitude and morphology of the signal. Therefore repetition of the

experiment is really difficult to achieve and is influenced by many factors. Moreover there

is a big difference between PAT1 and PAT2 for subject 1. This is due to errors which may

be related to wrong positioning of PPG sensor at left index finger.

PWV is also calculated by using the PAT values acquired from the measurements. As

explained earlier that PEP is also an important parameter in calculating PTT values as

shown in equation 2.1. But for our calculations PATpeak is calculated and hence the effect

caused due to PEP could be neglected. As usually the PATf oot is used in calculation and

due to presence of group delay PEP could be neglected. So if there is an error of ± 10%

in all measurements i.e. PAT value and length in equation 2.1, then the maximum error

82

5. Results

is estimated with partial derivation and the error range is from 5% to 20%, whereby 5%

mean the least error and 20% being the maximum error in the estimation. PWV found in

literature is normally in the range of 8-15 m/s, but this values are related to the aorta PWV

whereby in the current thesis average PWV is made at the extremities.

Various PWV values and the maximum error estimated is shown in Table 5.4.

S.No PWVLeft Temple PWVRight Temple PWVRight Finger PWVLeft Finger

Subject 1 1.5 m/s ± 0.30 m/s 1.8 m/s ± 0.36 m/s 3.3 m/s ± 0.66 m/s 3.0 m/s ± 0.60 m/s



Table 5.4: PWV values of different subjects

5.6. Heart Rate Variability (HRV)

HRV is a physiological phenomenon of variation of time intervals between two heart beats

and can be easily detected using R-peaks of the ECG. Still being a physiological phe-

nomenon HRV has proved to be an indicator of mortality after myocardial infraction [38].

In psychophysiology HRV is suggested to be a useful indicator of emotional distress. Re-

duced HRV is constantly observed in patients with cardiac failure and a very reduced

HRV is reported with patients undergone recent heart transplant. Moreover PPG signals

can also be used to calculate the heart rate variability through the time domain analysis.

The formula for calculating HRV is shown below and the signals are shown in Figure 5.9.

4RRm = RRn+1 − RRn (5.15)

4RRm+1 = RRn+2 − RRn+1 (5.16)

83

5. Results

HRV = RRm+1 − RRm (5.17)

R-wave

ECGPPG

Figure 5.9: Heart rate variability through ECG and PPG signals

HRV can be calculated by number of methods, but for current thesis time domain analysis

is employed. The same measurements of 60 seconds obtained through PAT_Calculator.m is

used.

So a beat to beat analysis is done and4RRm and4PPm intervals are calculated for various

signals obtained at different physiological sites as shown in Figure 5.9. Standard deviation

of the intervals is calculated.

This idea can further be propagated for calculating the HRV through the In-Ear signal

which is also periodic and is directly related to the cardiovascular system of the human

body. Therefore the mean of HRV is calculated from every PPG signal from different

physiological sites and mean of HRV from the In-Ear signal is also obtained simultan-

eously. All these measurements are compared with the mean of HRV calculated from ECG

signal. For further comparison with the standard deviation of beat to beat for every signal

is also shown in Table 5.5.

84

5. Results

As it can be easily concluded from the Max and Min values from the Table 5.5 there is a

heart rate variability even when the subjects are totally calm. The standard deviation is

less than 6 5 ms and the variability is not that high. The relative error of HRV extracted

from PPG is calculated in correspondence to ECG (which is a widely used for calculating

HRV). HRV obtained through PPG has a relative error of less than 5 %.

85

5. Results

Subject 1 Subject 2 Subject 3

ECG: Mean 884 ms± 65 ms 867 ms± 64 ms 833 ms± 27 ms

Max 1034 ms 1000 ms 875 ms

Min 775 ms 768 ms 771 ms

PPG1: Mean 884 ms± 64 ms 868 ms± 65 ms 833 ms± 35 ms

Max 1034 ms 985 ms 887 ms

Min 777 ms 767 ms 737 ms


Max 1027 ms 998ms 867 ms

Min 775 ms 761 ms 759 ms


Max 1030 ms 1003 ms 870 ms

Min 774 ms 756 ms 768 ms


Max 1032 ms 1003 ms 875 ms

Min 783 ms 758 ms 772 ms

Table 5.5: Mean heart rate variability and standard deviation for various subjects via ECG, PPG

and In-Ear for 60 seconds

So the results obtained through the HRV analysis are quite accurate and HRV can be easily

derived from ECG and from PPG at different physiological sites.

86

6. Summary and Outlook

Almost all quality improvement comes via simplification of design, manufacturing... layout, pro-

cesses, and procedures.

-Tom Peters

A multi-modal assessment system for evaluating cardiovascular parameters is designed,

developed and tested by performing different measurements. The measurements are sim-

ultaneous and processed through Mathwork MATLAB. The acquired signals are denoised

and the base line wandering is removed through the wavelet analysis in MATLAB. While

denoising and removing the baseline wandering the fidelity of the signal is maintained.

Feature extraction is done on the signals to extract valuable information. This information

is used in estimation of PAT, PWV, HRV, number of respirations, SV and CO.

PAT values are extracted from different locations simultaneously with the help of four

different PPG channels. The maximum uncertainty estimated for the PAT values is ±10%.

Moreover number of respiration is also calculated through the PPG curve. PWV values are

also calculated using the derived PAT values. The maximum uncertainty is estimated to be

around ±20%. It is also found that the PWV is much less in the temple region of the body

in comparison to the extremities like finger. This is due to the physiology and the anatomy

of the body.

Similarly the HRV is also estimated using PPG’s at different physiological locations as well

as from ECG signal. The comparison of the HRV obtained from the ECG is done with the

HRV obtained through PPG. The relative error in these measurements is estimated to be

less than 5%. This shows that HRV can be estimated through PPG waveform also. The

impedance cardiometry curve obtained is also quite accurate but the estimation of the SV

and CO lack precision due to the reasons mentioned in section 5.4. A further study is

required to optimize the results.

Still a lot of improvements could be done on every level. Starting from design of electronics

to the materials used for sensor technology. One of the important improvement would be

to make this system real-time and wireless so that multiple parameters could be captured

and results could be analysed in real time. Specific sensory is needed, instead of head-

phones with a known transfer function or to modify the silicon based autoplastic mould to

87

6. Summary and Outlook

be a sensor itself. More research is needed to locate the genesis of the In-Ear signal through

a very different approach like modelling.

For cardiometry it would be wonderful to see the improvement of signal by using chirp

signals and using different configurations of electrodes. It would also be interesting to

analyse the cardiac output of the heart in stress examinations. This could be achieved by a

wireless system and attaching gyro-meter sensors to subtract the motion artifact. Further-

more research is needed to make algorithms for better signal processing and to do clinical

trials to establish this method as an alternative to gold standards which are invasive or not

even close to real time.

88

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xiv

A. CD Content

The CD1 includes the following material can be found:

Master Thesis

This Master Thesis in PDF format (.pdf) and as Latex document (.tex)

Schematics and Layout

The schematics of the Impedance meter_V110 and the layout of the PCB in PDF format

(.pdf)

Software

Contains various software in Mathworks MATLAB.

Simulations in LTspice

Contains all the electronic simulations for the hardware conceptualization and testing.

Results and Measurements

Contains all the measurements taken and the results format (.mat and .xls)

1The thesis can be found with the thesis supervisors.

xv

Declaration for the Master’s Thesis

I warrant that the thesis is my original work and that I have not received outside assist-

ance. Only the sources cited have been used in this draft. Parts that are direct quotes or

paraphrases are identified as such.

Lübeck, 30th August 2013

Signature

I hereby the Luebeck University of Applied Sciences the right to publish, reproduce and

distribute my work, especially when it is to be presented to a third party for inspection.

Lübeck, 30th August 2013

Signature

Documents

Design, Test and Measurements