Measure Impedance in Congestive Heart Failure Patients

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F

ACULDADE DE

E

NGENHARIA DA

U

NIVERSIDADE DO

P

ORTO

Measure Impedance in Congestive

Failure Patients

José Carlos Coelho Alves

Mestrado Integrado em Bioengenharia Supervisor: Miguel Velhote Correia Fraunhofer Supervisor: Filipe Sousa

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Resumo

A doença cardiovascular é o tipo de doença mais comum em todo o mundo. Insuficiência cardíaca ocorre quando um problema estrutural do coração o impede de bombear o sangue suficiente para satisfazer as necessidades do organismo. Se a falha cardíaca ocorrer no ventrículo esquerdo a acumulação de fluidos nos pulmões pode ocorrer, causando edema pulmonar. Como isto ocorre antes do desenvolvimento de outros problemas mais graves a sua deteção numa fase precoce pode levar à diminuição dos internamentos hospitalares.

Como os fluidos corporais têm uma menor impedância elétrica que os tecidos, a medição de Bioimpedância tem vindo a ser cada vez mais usada como método de diagnóstico de edema pulmonar. A medição da Bioimpedância pode ser efetuada medindo o potencial gerado por uma corrente elétrica que é injetada no organismo. Como a corrente elétrica apresenta comportamentos distintos ao atravessar as células ou os fluidos corporais, as medições de Bioimpedância devem ser efetuadas para um largo espectro de frequências, Espectroscopia de Bioimpedância. A interpre-tação e processamento dos valores medidos de Bioimpedãncia é geralmente efectuada com recurso ao modelo de Cole. Efectuando a regressão não linear dos dados de bioimpedância ao modelo de Cole é possivel extrair os parametros de Cole, R0, R∞, α ou τ, que podem ser usados para estimar

a distribuição de fluidos corporais.

Este projecto de dissertação de mestrado é focado na validação e na melhoria do sistema de Espectroscopia de Impedância desenvolvido na Fraunhofer AICOS. Este dispositivo deverá ser usado conjuntamente com um Aplicação Android de forma a se obter um sistema móvel e barato, dado que ianda não há disponivel no mercado um dispositivo com estas caracteristicas. Os principais objectivos desta tese são calibrar e validar as medições deste dispositivo bem como a implementação e estudo de métodos de extração de parametros de Cole.

Em primeiro lugar foi efectuado um estudo de métodos de extração de parâmetros de Cole usando para isso os modelos de Cole plot, reactância, resistência e impedância no dominio das frequências e usando para regressão os métodos não linear e robusto dos mínimos quadrados e o método de minimização do erro absoluto. Este estudo tinha como objectivo obter o método que apresenta a melhor relação exactidão e poder de processamento necessário. Foi escolhida a implementação em Android dos modelos de resistência e de Cole plot usando o método de regressão não linear dos mínimos quadrados.

Em relação à calibração do sistema foi usado o método que usa três circuitos de referência recorrendo às medições do osciloscópio como medições verdadeiras de impedância. Após cal-ibração foram obtidos erros relativos na magnitude de 9.36% e erros na fase de 0.87 graus dos valores medidos pelo sistema em relação aos valores medidos pelo osciloscópio. Em relação aos valores extraidos dos parametros de Cole foram obtidos erros superiores a 10% na estimativa de R₀e R∞, próximos de 1% na estimativa de α e inferiores a 10% para os valores de τ.

Devido aos erros obtidos, pode-se concluir que o processo de calibração deverá ser novamente efectuado. Antes de passar para os testes com pessoas é necessário validar novamente o sistema.

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Palavras chave: Insuficiência cardíaca, Edema Pulmonar, Bioimpedância, Espectroscopia de Bioimpedância, Modelo de Cole, Parâmetros de Cole, Cole Plot, Regressão, Método dos Mini-mos Quadrados Não Linear, Método dos MiniMini-mos Quadrados Robusto, Método Minimização do Desvio Absoluto, Calibração, Validação.

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Abstract

Cardiovascular diseases are the most common type of diseases worldwide. Heart failure is a type of cardiovascular problem which occurs when some structural heart problem leads to its inability to pump necessary blood to fulfil the needs of the body. If the failure is on the left ventricle it can lead to fluid overload in lungs, causing pulmonary edema. Since it occurs before the development of other symptoms and major health problems, its earlier detection can improve life quality and decrease hospital admissions.

As body fluids have a lower electrical impedance than tissues, the measurement of impedance, also called Bioimpedance, in body or thorax, has increased as an option for pulmonary edema diagnosis. Those measurements can be made injecting a current on the body and measuring the resulting potential. Since electrical current has different behaviour when passing through cells or extracellular fluids, Bioimpedance measurements should be done for a large spectrum of frequen-cies, method called Bioimpedance Spectroscopy. Bioimpedance measured values interpretation is usually done using Cole model equations and its fitting. Cole model fitting allows the extraction of Cole parameters, R0, R∞, α or τ that then can be used for estimation of body or thorax fluids.

Since there is no portable and low cost Bioimpedance Spectroscopy devices for clinical use currently available in the market, developing such a solution is a market opportunity.

This dissertation is focused in the validation and improvement of a mobile Bioimpedance Spectroscopy measurement device that was developed before in Fraunhofer AICOS. This device will be used combinated with a Android Application. Main objectives are calibration and valida-tion of device measurements as well as implementavalida-tion of methods of extracvalida-tion of Cole parame-ters.

Firstly it was made a study of methods of Cole parameter extraction, namely, Cole plot, re-actance, resistance and impedance against frequency models using Non Linear Least Squares, Robust Least Squares and Least Absolute Deviation methods in order to find the method that pre-sented a best relation between accuracy and low processing power for implementation in a smart-phone. Based on the obtained results, it was implemented in the Android Application resistance against frequency and Cole plot models using Non Linear Least Squares method.

Regarding system calibration, it was decided to implement a three-reference circuit method using oscilloscope measurements as ground truth. After calibration, it was verified that average relative error of magnitude of impedance was 9.36% and average error of phase was 0.87 degrees in comparison with oscilloscope measurements. For Cole parameters extracted it was obtained relative errors above 10% for parameters related with R0and R∞, near 1% for α and bellow 10%

for τ estimations.

Due to errors obtained it can be concluded that the calibration process has to be improved and then a new validation of the system have also to be performed before use of the system for human trials.

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Keywords: Heart Failure, Pulmonary Edema, Bioimpedance, Bioimpedance Spectroscopy, Cole Model, Cole Parameters, Cole Plot, Curve fitting, Non Linear Least Squares Method, Robust Least Squares Method, Least Absolute Deviation Method, Calibration, Validation.

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Agradecimentos

Um agradecimento especial ao meu orientador Filipe Sousa e a todo o pessoal da Fraunhofer AICOS pelo apoio dispendido e pela inteira disponibilidade. Um obrigado ao professor Miguel Velhote Correira por toda a ajuda fornecida e pelas deslocações às instalações da Fraunhofer a fim de apenas dar o seu contributo para este trabalho.

O meu principal agradecimento vai para o meu Pai. Ele que tanto lutou durante todo o meu percurso académico e cujo seu maior desejo era ver o filho formado. Essa tua luta e vontade fez-me nunca desistir do objectivo e lutar cada dia mais para o alcançar. Está quase! À minha mãe pelo sacrificio feito a trabalhar para me conseguir sustentar durante os 5 anos da faculdade! Aos meus irmãos pelas palavras de "Este gajo não faz nada!" que me faziam rir mesmo quando a situação dentro na faculdade estava mais apertada. Ao resto da familia um obrigado também pela preocupação e pelas explicações que tive de dar sobre o que era um Engenheiro Biomédico.

Agora para os meus irmãos emprestados, ao Carlos Guimarães, Carlos Ferreira e João Araújo, um muito obrigado pelos momentos, pelas "estibas", pelas queimas, pelas massas com canela e pelo chão das vossas casas quando precisava de um tecto para dormir. Ao pessoal do meu ano que entrou comigo para a Faculdade, aos outros 6 Jabardos para os quais entrei tarde mas a bom tempo, e ao pessoal mais velho e mais novo que me deu a conhecer um pouco do espírito académico um obrigado!

Aos "Matrecos da Fraunhofer" especialmente ao meu outro irmão emprestado, Pedro Santos, um agradecimento especial por tornarem as nossas pausas e matraquilhadas em momentos de galhofa e festa constante. Ao Pedro Santos obrigado pelas tuas piadas.

À minha companheira de Erasmus e amiga para sempre, Sofia Silveira, um agradecimento especial por ter tornado esses 5 meses especiais e por me ter iluminado o caminho do fitness e do exercicio físico. Sem ti ainda tinha mais 20 kg!

Ao pessoal de Paços, que uma vez por ano vão tornando uma noite numa noite completamente aleatória e de nostalgia total, o meu muito obrigado!

Acho que não me esqueci de ninguém, mas se esqueci peço desculpa.

José Carlos Coelho Alves

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“ Obstacles don’t have to stop you. If you run into a wall, don’t turn around and give up. Figure out how to climb it, go through it, or work around it. ”

Michael Jordan

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List of Figures

2.1 Current flow through biological cells and circuit schematic representation of tissue

Bioimpedance . . . 8

2.2 Fitted Cole plot model example . . . 9

2.3 Typical Cole plot and geometric measures . . . 10

2.4 Relation between the length of cords Ui and Vi and the frequency of measurement 11 2.5 Cole plot with representation of the centre of the semi-circle (x0,y0), its radius (r) and the error (∆i) for an measured point used for solving the LS method. . . 11

2.6 Single and Double-dispersion Cole impedance models . . . 14

2.7 Proposed filter structure with Cole-Cole impedance terminated by load resistance RL 15 2.8 Schematic representation of the BIS measurement system. . . 17

2.9 BIS Measurement system . . . 18

2.10 MRPDD technique measurement principle . . . 19

2.11 Typical RC circuit used for calibration. . . 22

3.1 Equivalent circuit of electrode-electrolyte skin interface (a) and equivalent circuit of a textile interface of electrode-electrolyte skin interface (b) . . . 24

3.2 Different arrangements of electrodes placement. a) Foot - Foot; b) Foot - Hand; c) Hand - Hand; d) thoracic. . . 25

3.3 Representation of 16 possible measurement electrode positions in a 2D thorax representation . . . 26

3.4 Different leads of placements of electrodes used in Impedance measurements . . 27

3.5 Posture changes used for sensitivity assessment . . . 28

3.6 XiTRON Hydra 4200 device. . . 35

3.7 SFB 7 device . . . 36

3.8 MultiScan 5000 kit . . . 36

3.9 Body Composition Monitor device . . . 37

3.10 InBody devices-InBody S10 Body water analyzer and InBody 770 Body compo-sition Analyzer . . . 38

3.11 InBody Band and TomTom Touch Fitness Tracker . . . 38

4.1 Results in prediction of α and τ for dataset 1 with SNR of 40 dB . . . 47

4.2 Results in prediction of Cole parameters for dataset 1 with SNR of 40 dB . . . . 48

4.3 Results in prediction of cole parameters using Cole plot Model for dataset 1 with 10% of random noise . . . 49

4.4 Results in prediction of cole parameters using Cole plot Model for dataset 1 with 6 outliers . . . 50

4.5 Results in prediction of cole parameters for dataset 2 with SNR of 40 dB . . . 51

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4.7 Results in prediction of cole parameters using Cole plot Model for dataset 2 with

6 outliers . . . 52

4.8 Results in prediction of cole parameters using Cole plot Model for dataset 2 with 10%random noise . . . 53

4.9 Results in prediction of Cole parameters for dataset 3 with SNR of 40 dB . . . . 53

4.11 Results in prediction of Cole parameters using Cole plot Model for dataset 3 with 6 outliers . . . 55

4.12 Results in prediction of Cole parameters using Cole plot Model for dataset 3 with 10%random noise . . . 55

5.1 Circuit of measurement used with the BIS measurement device . . . 58

5.2 Circuit of measurement used with the oscilloscope . . . 59

5.3 Starting app activity asking permission to use BLE . . . 61

5.4 Left - Graph Select activity menu; Right - Pop-up that appears to allow access to external memory of the device . . . 63

6.1 Error results of phase using Oscilloscope . . . 68

6.2 Error results of phase using RCL meter . . . 68

6.3 Relative error results of magnitude using RCL meter . . . 69

6.4 Relative error results of magnitude using Oscilloscope . . . 69

6.5 Results of study of different ways of construction of the circuits . . . 71

6.6 Relative error results of magnitude using Oscilloscope with best type of circuits . 72 6.7 Error results of phase using Oscilloscope with best type of circuits . . . 72

6.8 First results of validation of the system regarding Cole parameter extraction . . . 73

6.9 Example of validation circuit . . . 74

6.10 Final error results of Cole parameters extracted . . . 74

6.11 Magnitude relative error in the measurement of the validation circuits . . . 75

6.12 Phase error in the measurement of the validation circuits . . . 75

6.13 Comparison between standard, oscilloscope and BIS measured values of phase for the three circuits . . . 76

6.14 Comparison between standard, oscilloscope and BIS measured values of magni-tude for the three circuits . . . 77

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List of Tables

3.1 Summary of reviewed BIS techniques and method studies available in literature . 32

3.2 Summary of reviewed BIS techniques and method studies available in literature

-Continuation . . . 33

3.3 Summary of reviewed BIS techniques and method studies available in literature -Continuation . . . 34

4.1 Standard datasets used to study cole parameter extraction methods . . . 43

5.1 Parameters of three calibration reference circuits . . . 58

5.2 Parameters of three validation circuits . . . 64

A.1 Average time and cycles results when using set 1 and resistive, impedance and reactance models . . . 83

A.2 Results of Cole extraction study for impedance model for standard dataset 1 with random noise . . . 84

A.3 Results of Cole extraction study for resistive model for standard dataset 1 with random noise . . . 85

A.4 Results of Cole extraction study for reactance model for standard dataset 1 with random noise . . . 86

A.13 Results of Cole extraction study for Cole plot model for standard dataset 1 with outliers . . . 93

A.14 Results of Cole extraction study for Cole plot model for standard dataset 1 with random noise . . . 94

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A.15 Average time and cycles spent when using Cole plot Model with random noise and outliers for set 1 . . . 94

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Abbreviations and symbols

AHF Acute Heart Failure

AICOS Assistive Information and Communication Solutions BCS Body Composition Spectroscopy

BFGS Broyden–Fletcher–Goldfarb–Shanno BIA Bioimpedance analysis

BIS Bioimpedance spectroscopy BLE Bluetooth Low Energy BMI Body mass index CHF Congestive heart failure CVD Cardiovascular disease ECW Extracellular water ExF Excess of fluid GPD Gain-Phase Detector HF Heart Failure HR Hydration ratio

IA Instrumentation Amplifier ICW Intracellular water

ITI Internal Thoracic Impedance LAD Least absolute deviation

LS Least-squares regression analysis

MRPDD Magnitude-ratio and phase-difference detection NLLS Non Linear Least Squares

OS Operative System

PE Pulmonary edema

RLS Robust Least Squares SEE Standard Error of Estimate SNR Signal-to-noise Ratio TBW Total body Water

TTI Transthoracic Impedance

List of Symbols:

ωc Angular characteristic frequency

ω Angular frequency

Z Bioimpedance

D Body density

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ρT BW Body resistivity

τ Characteristic time constant cf Correction factor

α Dimensionless value of characteristic of Cole dispersion model R₀ Extracellular resistive

ρECW Extracellular resistivity

R∞ Intracellular resistive

ρICW Intracellular resistivity

H Magnitude Ratio θ Phase difference

X Reactance

y Reactive coordinate in Impedance plane

R Resistive

x Resistive coordinate in Impedance plane r Semi-Circle radius

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Chapter 1

Introduction

1.1 Context

Cardiovascular diseases (CVD) are one of the most common diseases worldwide and the most common cause of mortality. In 2013, it was estimated that CVD have caused around 17.3 million of deaths all around the world [1].

Among the CVD, the heart failure (HF) still remains a major health problem. It is a debilitating problem which incapacitates people and also has a high mortality rate. In the United States (US), the estimated prevalence of this problem in population older than 20 years old is 5.8 million of Americans, approximately. In Portugal, it is estimated that 260 000 people suffer from this condition [2]. Additionally, it is estimated that around 915 000 new cases (2009-2012) of HF are diagnosed every year. Also, one of each nine death certificates mentioned HF as a cause [3]. Besides the mortality, HF is a cause of around 1 million of discharges in US. Worldwide the costs related with this health problem are estimated of being of 108 ×109 (billion) dollars (60 and 70 % are direct costs). Specifically, in the US (2013) the costs with this problem are around 30.7 ×109 _{(billion) dollars which 68 % of them are direct costs. The first cause of hospitalisation of}

people older than 65 is Heart Failure and, in Europe, it is estimated that two thirds of its costs are hospitalisation costs. Also, this problem is increasing all around the world due to several factors. For instance, the ageing population increase the prevalence of risk factors such as diabetes, high blood pressure or improvement in the post myocardia infraction survival [4].

HF occurs when there is some functional or structural cardiac disorder that leads to the inability of the heart to pump the rate of blood necessary to fulfil the needs of the organs. There are two main forms of HF: systolic and diastolic heart failures.

Systolic heart failure is characterised by an impairment in the myocardia contraction which leads to an insufficient blood output. It can cause an accumulation of blood in the ventricle which may lead to dilatation of the heart and increase of ventricular diastolic pressure.

The diastolic heart failure is the opposite from the systolic. In this one, the ventricle has not the capability to relax causing its impaired filling. It will also cause an increase of ventricular systolic pressure at any given diastolic volume. Depending on the failing ventricle the HF can be

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diagnosed as left, right side or both sides. If the disorder is in the right ventricle it usually leads to problems in the systemic circulation. On the other hand, patients in whom the left ventricle is failing can suffer from pulmonary congestion, one of the aftereffects of Congestive Heart Failure (CHF). The increasing of pulmonary venous and capillary pressures will increase the transudation of fluid into the air spaces and tissues. If the body is not capable to remove those fluids, it will cause its accumulation, which is called pulmonary edema (PE).

The accumulation of fluids in the body generally occurs before occurrence of other HF symp-toms, like dyspnea, fatigue or weakness, so its detection should be an indicator of CHF [5].

1.2 Motivation

This dissertation is developed in collaboration with Fraunhofer Portugal Research Center for As-sistive Information and Communication Solutions (AICOS). It is inserted in the international project SmartBEAT. This project has as objectives optimization of the quality of care, the improve-ment of diagnosis and reduce of costs and mortality of patients with HF. Having these purposes, SmartBEAT proposes the development of a mobile solution that will allow autonomous monitor-ing of the patient condition. The patient condition should be also provided to its carers. Thus, the solution should acquire vital signals that will be processed by a smartphone and retrieve as output the condition of the patient [6,7].

In a patient with CHF, body hemodynamic can suffer significant changes leading to accumu-lation of body fluids (edema). Usually, this accumuaccumu-lation occurs before the presence of another symptoms as dyspnea or feeling of weakness [5].

The measurement and analysis of the electrical impedance of parts or of whole body have been used for accessing the pulsating blood volume, cardiac output and stroke volume. As body fluids have lower impedance than tissues, the accumulation of fluids will lead to a decrease in the measured impedance. Techniques of monitoring using Bioimpedance measurement, like Bioimpedance spectroscopy (BIS) or Bioimpedance analysis (BIA) have started to be used in clin-ical environment to estimate body fluids volume status and hemodynamics [8].

Thus, rises as an opportunity the creation of a mobile solution that uses thorax Bioimpedance monitoring in patients for estimation of volume status of lung fluids. This way, it will allow an earlier detection of a possible HF and, therefore, its prevention and treatment. Since one in each four people waits more than a week to ask for medical advice, the early detection at home and the share of that information with the doctors would turn diagnosis of this problem easier and quicker. Then it would reduce hospital admissions, associated costs, mortality and morbidity resulting from HF [9].

1.3 Objectives

The main objective of this study, is to aim a solution to measure the accumulation of fluids in the lungs through measurement of the electrical impedance in the thorax. The solution should be

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1.4 Initial plan for development of the solution 3

mobile and friendly, allowing the self-use and monitoring. The access of monitoring should be provided to carer (doctor or familiar), facilitating the earlier diagnosis and post work in hospitals and clinics.

In this study, it will be continued the previous work of Ricardo Silva that developed all the electronic part of a BIS measurement system (more details in section2.5). It has all the necessary components to implement it, including current source, signal acquisition, gain-phase detector and Pandlet core which includes a microcontroller and a Bluetooth module to communicate with the smartphone [9]. However, one important missing part of the development of this measurement system is its calibration, so this project will be, in a first phase, focused on it. Following its calibration, the next objective should be the electrical validation of the system, in order to advance to subject trials.

This study will also be focused on methods of correlation between the measured phase and magnitude of impedance and the volume status of the thorax. Method of processing the data obtained as well as method of correlation between this data and the accumulation of fluids should be explored. It is also necessary to study the best way of measuring the electrical Bioimpedance, more specifically where excitation and measurement electrodes should be placed and which are the patient positions that allow the acquisition of more accurate results.

The final objective of this project is the implementation/development of an BIS algorithm for measuring changes in thorax fluids using impedance processed data. This algorithm should be implemented in order to run on a smartphone together with a Bluetooth application for connection with the BIS measurement device.

1.4 Initial plan for development of the solution

Taking in a account the work that was developed before by Ricardo Silva, one of the most im-portant step required is the reduction of the systematic and non systematic errors on the system. Ricardo has already studied the influence of the number of measurements, 10, 20, 50 and 100 sam-ples, on the average value and on the error region around it. It was observed that results using 10 samples showed a little error region while for 50 and 100 samples this region almost disappeared. Thus, in order to avoid random and unexpected errors, the acquisition of a high number of samples and execution of their arithmetic average should be performed and implemented in the Android Application. It has to be taken in account also the time that this acquisition would spent in the way to save battery of the measurement system as well as the smartphone.

Another step, and one of the most important of the development of this solution, is the calibra-tion of the system in order to avoid and correct possible systematic errors that occur with this type of system. This way it is necessary to implement one method of calibration and to construct well known and reliable RC circuits in order to measure its impedance with a ground truth and with the BIS system and then calculate calibration coefficients. Then calibration of measured values should be applied in the Android Application before data processing.

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Processing data is the step that should follow calibration. In this period of development, meth-ods of extraction of Cole Parameters should be studied to understand which are the most accurate methods. It is also required to study if methods can be integrated in an Android Application since the processing power of a smartphone is lower than the personal computer one. After this study, the method that provides the best relation power consumption - accuracy should be implemented. Then, Cole parameters extracted will be used in disease status analysis/estimation of lung accu-mulation of fluids.

Before patient or healthy subject trials, the measurement device has to be validated using electric circuits, to verify if it is measuring their impedance with accuracy. So, different RC circuits, similar to ones used to calibrate the system, will be used for measurement and for result analysis.

Then, moving to impedance measurement in subjects, healthy subjects or patients, it should be tested the best way and the best accessories of measurement. Type of electrodes, local of placement and posture of measurement can influence its results. Thus, a study regarding this variables should be done in order to optimise accuracy of measurements.

Lastly, it should be studied the variability of the Cole parameters and impedance inside a group of healthy subjects and then between healthy subjects and patients with heart failure. This will allow to study if there are significant differences in the estimated values between those groups. Then, if there it can also be applied an algorithm to estimate lung fluids. Those results should be compared for instance with a preliminary diagnose made by a doctor, in order to prove if each subject is healthy or not.

1.5 Contributions

The main contribution of this dissertation is the study of Cole extraction parameters in order to check which one of the methods and approaches are best suited for using in an Android Ap-plication, particularly regarding processing power required and accuracy of the methods. The implementation of the Cole extraction method in the Android Applications is also a contribution in order to achieve the main objective of developing a mobile BIS solution. The update of the Android Application to the API 23 is also an important improvement and contribution in order to keep the Application updated with one of the most recent version of the Android OS.

The test of several calibration possibilities, namely in what concerns the ground truth device, and its implementation in order to avoid systematic errors in the measurements of the BIS device, takes also an important role in this project. Study of better circuit design for calibration was also an important step forward in the development of the proposed solution.

In this project a preliminary system validation using electrical circuits was performed in order to check if using electrical circuits the BIS system measurements are accurate and reliable.

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1.6 Structure of the Document 5

1.6 Structure of the Document

Following this introductory chapter, in chapter 2"Background" it is presented a brief introduc-tion about BioImpedance Spectroscopy (BIS) as well as methods for extracting and process data from the BIS measurements, namely Cole parameter extraction methods, . In this chapter some calibration methods for the BIS system are also presented and explained.

In chapter3, a review of literature regarding all variables in BIS measurements was made. Studies of impedance and correlation with body fluids, influence of posture and type of electrodes in Bioimpedance measurements are examples of some subjects addressed in this chapter. Existent solutions in the market are also explored.

Following the chapter "State of the Art", the chapter "Methods" is introduced. In this chapter, calibration methods and circuit designs explored, implementation in Android of calibration and Cole parameter extraction method and, lastly, system validation method are shown and explained. In chapter6"Results", are presented results from the calibration and circuit design methods as well as the results from the electrical validation. A discussion and analysis of those results is also addressed.

Lastly, in Chapter 7, some conclusions about the developed work are presented as well as suggestions for future work in order to improve this solution.

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Chapter 2

Background

2.1 Introduction to Bioelectrical Impedance

Bioelectrical impedance or Bioimpedance measurements have been widely used in estimation of body composition and hemodynamics. Nowadays, new analysis methods and systems to access it have been developed.

Bioimpedance results from a passive response of the body to a current injected into it. Bio-logical tissues obstruct the flow of an electric current, and this ability is called Bioimpedance. In a homogeneous conductive cylindrical material, the resistance is proportional to its length and in-versely proportional to its cross-section area. However, neither the constitution neither the shape of different biological tissues are similar. For instance, both blood and muscle have lower impedance in comparison with bones or fat tissue. A lung filled with air has higher impedance than one filled with fluids, due to their different compositions [10].

For accessing body composition, it is mainly important to access the changes in the water content (fluids) of the body. Biological tissue fluids are divided in two types: the intracellular fluid (ICW) and the extracellular fluid (ECW). The ICW is surrounded by the cellular membrane which separates itself from the ECW. As fluids are composed of water and some electrolytes, they are considered resistive. However, cell membrane is considered an insulator due to its lipid double layer, since lipids are not good conductor materials and then it will have a capacitive behaviour. Therefore, the Bioimpedance (Z) is the combination of the resistance (R), resulting from ICW and ECW and the reactance (Xc), caused by the cell membrane as shows equation2.1.

Z= R + jXc (2.1)

For representing the biological tissue behaviour some different circuits models have been de-signed. A possible approach is the representation of ECW resistive in parallel with the reactance from cell membrane and resistive of the ICW in series, figure2.1. With this design it can be inter-preted that high frequency currents will pass through the ECW and ICW as the cell membrane acts as almost a perfect capacitator. Low frequency currents will only flow through the ECW, because those currents cannot penetrate the cell membrane (Figure2.1) [11,12].

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Figure 2.1: Current flow through biological cells and circuit schematic representation of tissue Bioimpedance. Adapted from [11].

Due to this variation of behaviour for high or low frequency currents, for measuring body composition it is necessary to analyse the body response to an injection of current signals in a large spectrum of frequencies.

2.2 Bioimpedance Spectroscopy and Cole Equations

The Bioimpedance Spectroscopy (BIS) technique is a method that uses analysis of multi-frequency complex impedance of body tissues to access its physiological and pathological state. It is a non-invasive technique and has been widely used as a diagnostic method of, for instance, hydration status or fluid overload in lungs.

In this method tetra-polar or octo-polar arrangements of electrodes (foot/ankle for body anal-ysis or other positions for segmental analanal-ysis) should be used for measuring complex impedance in a wide spectrum of frequencies. The reactance (X) and the resistance (R) or the phase delay (θ ) and magnitude ratio (H) can be obtained. This BIS data can then be fitted into the Cole model in order to extract Cole parameters, which will allow the assessment of the tissue fluid status.

Cole equations were introduced by Keneth S. Cole [13], and they are being widely used in BIS methods. Cole equation represents the tissue impedance as a dispersion given by equation2.2.

Z(ω) = R∞+

R0− R∞

1 + (ωτ j)α (2.2)

where Z(ω) is the impedance measured at the angular frequency (ω), R0and R∞resistances at

very low and very high frequencies, respectively, τ is a characteristic time constant corresponding to the frequency at which the reactance is maximum and α is an dimensionless number character-istic of each tissue representing its dispersion in the Cole model.

After fitting the model (figure2.2) and plotting for instance the phase delay vs the magnitude of impedance or the reactive vs resistive part, different techniques and methods can be used to obtain the parameters of the Cole equation. Minimization of the summed squared of the error or the least absolute deviation error between the data series and the fitted model are some of the

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2.3 Cole parameter extraction 9

possible methods used for Cole model fitting and therefore parameter extraction (explained in next section).

Figure 2.2: Fitted Cole plot model example. Adapted from [8].

2.3 Cole parameter extraction

Different ways of fitting the Cole model and extract the Cole parameters have been studied for many years. Only after their extraction it is possible to estimate segmental or full body fluids. Cole model approximates the electrical behaviour of tissues, as seen in section 2.1, and Cole Model equation is given by2.2. Decomposing equation2.2into resistance (R) and reactance (X), are obtained equations2.3and2.4[14].

R(ω) = R∞+ (R0− R∞) + (1 + (ωτ)αcos(απ₂)) 1 + 2(ωτ)α_cos(απ 2) + (ωτ)2α (2.3) X(ω) = − j (R0− R∞) + (ωτ) α_sin(απ 2) 1 + 2(ωτ)α_cos(απ 2) + (ωτ)2α (2.4)

Many studies have introduced different models and approaches for extraction of Cole param-eters. Extraction of geometric properties of Cole Plot (figure2.2) has been used in [15,16]. In-stead of using the Cole Plot fitting (figure2.2), fitting of the impedance real part, the imaginary part or the complex impedance had started to be used [14]. Different biological tissues electrical models, which better approximate the tissues behaviour, have also been used, for example, the double-dispersion Cole Model used in Freeborn et al. [17]. Methods that had not used impedance estimation, simplifying the necessary hardware (no requiring, for instance, a phase-gain detector) for Implementing the BIS method, were also studied in [18,19].

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Figure 2.3: Typical Cole plot and geometric measures. Adapted from [15]

For fitting of data to the model functions in order to extract Cole parameters, different methods have been proposed and used in many studies. Iterative process as Non-Linear Least Squares (NLLS) curve fitting [14,17–21], Least Absolute Deviation method (LAD) [22], and Stochastic Optimisation Algorithms (SOA) [23] have been explored to find the most accurate method.

In the following subsections, some of the different fitting and extraction methods will be pre-sented.

2.3.1 Geometric methods

In [15,16], it is presented a Cole parameter extraction method, based on the geometric properties of the Cole plot. Plotting the reactance (X) against resistive (R) part of impedance, it is possible to obtain a semi-circle with the centre depressed below the resistance axis of figure2.3[15]. R0and

R_∞will be the intersections of this semi-circle with the resistive axis. Thus it can be observed that the centre on the R-axis can be calculated by (R0+R∞)

2 and the radius will be

(R0−R∞)

2 . Values of R0

and R∞can be determined by least squares regression analysis (LS).

Additionally, the value of frequency in which the value of the reactance is maximum is called the characteristic frequency (ωc). Since τ is given by _ω1

c, by differentiating the equation2.4, in

relation to ω and equating it to zero, it is possible to calculate this parameter [15]. For measuring the Cole parameter α, it can be used the length of the cords uiand vi, see figure2.3. These cords

link the values of R∞ and R0 to the data point of frequency ωi, respectively. By Plotting lnu_vi

i

versus ln (ωi), it is possible to obtain a line with slope equal to −α [16]. Through a simple linear

regression of these points, it can be obtained the slope of this line, estimating the value of the parameter α. The intersection between this line and the frequency axis, will also give the value of the characteristic frequency, ωc. With this value, parameter τ can be, lastly, obtained [15,16].

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Figure 2.4: Relation between the length of cords Ui and Vi and the frequency of measurement. Adapted from [15]

2.3.2 Curve fitting methods

2.3.2.1 Non-linear Least Squares (NLLS) and Least Absolute Deviation (LAD) curve fitting A method proposed by Kun et al. in 1999 [21] is based in the fact that the plot of resistive and reactive parts of impedance in the impedance plane is a semi-circle with depressed centre below the resistance axis. This way, as each measured point should be part or should be near this semi-circle, it is possible to estimate the layout of the measurement points as being a minor arc of circle. Using this as priory knowledge it is possible to use the NLLS method to estimate the centre of (x0,y0) and the radius (r) of the semi-circle. Therefore, it will be possible the estimation of R0,

R∞and α (Figure2.5).

Figure 2.5: Cole plot with representation of the centre of the semi-circle (x0,y0), its radius (r) and

the error (∆i) for an measured point used for solving the LS method. Adapted from [21]

NLLS error equation, considering (xi, yi) measured points and the semi-circle defined by (x0,

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(equation2.5) [22]. F(x0, y0, r) = N

∑

i=1 q (xi− x0)2+ (yi− y0)2− r 2 (2.5) The objective of this method is to get the optimal parameters x0, y0and r for which equation

2.5 is minimised. After differentiating the error function 2.5 in relation to each one of these parameters, equating the results to zero and solving iteratively the system, circle parameters are obtained. Then, the values of the Cole parameters can be easily calculated through geometric equations2.6,2.7and2.8: R0= x0+ q r2_{− y}2 0 (2.6) R∞= x0− q r2_{− y}2 0 (2.7) α = 1 −2 πarcsin |y0| r (2.8)

With these parameters, the missing one, τ, can be obtained through equation2.2. As this equa-tion depends on the measured values, it has to be done the calculaequa-tion with some measured values and then the calculation of the average of the obtained τ values. As it is an indirect calculation, the estimation of this parameter is the main weakness of this method, since it is only reliable for Gaussian measurement noise lower than 2% [21].

The Least Absolute Deviation (LAD) method [22], has grown as a robust alternative to NLLS method since last one is sensitive to noise that is not normally or near-normally distributed. As it is hard to achieve data errors normally distributed using BIS due to small number of samples collected by majority of the measurement systems, LAD method grows as an alternative to NLLS method [22] since it has shown good response to outliers. The difference between NLLS and LAD methods is that the last one uses, instead of the sum of the euclidean distances between measured points and the fitted semi-circle, equation2.5), the sum of the absolute errors, shown in equation

2.9. F(x0, y0, r) = N

∑

i=1 q (xi− x0)2+ (yi− y0)2− r (2.9) The problem with this method is its small amenability for calculation comparing to NLLS method. However, with the increase of computer power and effectiveness of linear programming methods this problem is being overcome. To find the parameter solution that minimises the error function, a method for solving unconstrained nonlinear optimisation problems can be used. In [22], Broyden–Fletcher–Goldfarb–Shanno method was used with this purpose. A comparison between both methods, NLLS and LAD, has been done in [22] and results have shown that LAD

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outperforms NLLS in relation to robustness to outliers presented in the dataset. However, if the majority of data points occur with deviations, both methods show less robustness.

2.3.2.2 Frequency domain Cole models

Frequency domain fitting approaches, [14], are alternatives to previously described impedance domain methods. Ayllón et al. [14] uses NLLS fitting for studying three different approaches of Cole model (fitting of the equation using only imaginary part, only real part and with the complex impedance). The NLLS method aims to obtain the best coefficients that fit the curve, minimising, with this purpose, the summed square of the error between measured points and the fitted model (equation2.10). min N

∑

i=1 = min N

∑

i=1 (yi− ¯yi)2 (2.10)

In work developed in [14], it was used equations2.3and2.4as models for fitting using three different approaches, real part fitting (R(ω)), imaginary part fitting (X(ω)) and complex impedance fitting (Z(ω) = R(ω) + jX (ω)). In those models, the frequency ω is the independent variable and the Cole parameters, R0, R∞, α and τ are coefficients of the model. Using this models and the

NLLS method it is possible to calculate iteratively these parameters.

In [14] it was also proposed a novel method for fitting the Cole Model in the impedance domain, based on the fact that the plot is a semi-circle. The distance between the centre of the semi-circle and data points should be almost constant. Thus, the centre can be estimated as the point which has the minimum variance of those distances.

Both methods, have been tested and studied to estimate parameters of a Cole model with known parameters. The models obtained by each different fitting, were used to estimate function of R(ω), jX(ω), |Z(ω)| and the Cole Plot. The standard error of estimate (SEE) for each of those estimations was used to measure the performance of each of the four methods. It was also compared the accuracy in the estimation of the Cole parameters between methods. The results from this study have shown that all four methods performed relatively well for Cole parameter extraction. However, best results were obtained for modelling using R(ω) and poor performance were achieved using X(ω).

In the study made in [20], it was used the NLLS method using the modulus of impedance and its results were compared with circular fitting on the impedance (reactance vs resistance) plane, that is fitting of Cole plot. The NLLS method has shown advantages in relation to Cole plot fitting due to the fact that the modulus of impedance is less influenced by leakages in capacitance than resistive and reactance. Another advantage of the NLLS method used is that it is not necessary phase detection, simplifying the hardware of the used system.

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Figure 2.6: a) Single dispersion Cole impedance model; b) Double-dispersion Cole impedance model. Adapted from [17]

2.3.3 Double dispersion Cole model

The NLLS method was also used, in [17,23], to fit the double dispersion Cole Model, figure2.6.b, which is a new model that better approximates the behaviour of more complex materials in a larger range of frequencies.

The double dispersion model will be then characterised by the following equation: Z(ω) = R∞+ R1 1 + sα1_R 1C1 + R2 1 + sα2_R 2C2 (2.11) The main difference between this approach and the simple dispersion model for Cole model fitting is the estimation of seven parameters (R∞, R1, R2, α1, α2, C1, C2) instead of the four

param-eters.

2.3.4 Cole Model fitting without direct impedance measurement

A new method, introduced in [18], allows the Cole model fitting without direct impedance mea-surement. It uses the output voltage resulting from the crossing of the current through the tissue and then through an operational amplifier buffer with suitable bandwidth for fitting the Cole Model (figure2.7).

It is presented an approximation without using of direct impedance measurement, and it would simplify the necessary hardware for applying BIS. In [18] and [19] this method is used for fitting

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2.4 Body fluids Estimation 15

the simple dispersion Cole model and in [17] it is used in the modelling of the double dispersion Cole model.

Figure 2.7: Proposed filter structure with Cole-Cole impedance terminated by load resistance RL.

Adapted from [18]

2.4 Body fluids Estimation

Standard methods for assessment of body fluid volumes are dilution methods. Although those methods are accurate they require observation of patient for many hours and assays with blood samples. It will not allow a fast and easy diagnosis in clinical practice. Thus estimation of body fluids using Bioimpedance measurements data rises as a good alternative. For estimating body fluids, different methods have been developed and studied [24–26]. Those methods use Cole parameters, which their extraction was explained in section2.3, in combination with body factors related to each patient like weight, shape or height.

In Moissl et al. [24], two BIS methods are presented. One of them, XiTRON equations, is the method used in XiTRON Hydra 4200, which is one of the most used BIS devices. Its equation model is based in Hanai equations [27] and it has as input parameters, Cole parameters R0and R∞,

body weight (W) and body height (H), as can be seen in equation2.12,

ECWX itron= ρECWKBH2 √ W √ DR₀ 2 3 (2.12) where D is body density (1.05 kg/l), ρECW is the extracellular resistivity (women: 39 Ω.cm,

men: 40.5 Ω.cm) and kB is a shape factor correcting values regarding proportions of body parts

between wrist and ankle (positions used as measurement places) (kBconsidered equal to 4.3). ICW

was calculated according equation2.13that was introduced in [25].

ICWX itron= ECWX itron

ρT BWR0 ρECWR∞ 2₃ − 1 ! (2.13)

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where ρT BW is the body resistivity and is calculated through,

ρT BW= ρICW− (ρICW− ρECW)

R_∞ R₀

2₃

(2.14)

In order to be used in this model, ρICW takes the value of 264.9 Ω.cm for women and 273.9

Ω.cm for men.

Moissl et al. [24] proposed an alternative for XiTRON method, called Body Composition Spectroscopy (BCS), which makes use of body mass index (BMI). The introduction of BMI will influence the method with data from body composition. Those equations2.15and2.16are also based in Hanai mixture theory [27].

ECWBCS= kECW H2√_W R₀ 23 (2.15) ICWBCS= kICW H2√_W R∞ 23 (2.16)

In those equations k parameters are combinations of D, ρICW, ρECW and kB and their values

depend of the BMI of each studied patient (equations2.17and2.18), kECW = a BMI+ b (2.17) kICW = c BMI+ d (2.18)

where a, b, c and d were measured from regression between values of reference of k and BMI, and it was determined by cross validation that their values are, a = 0.188, b = 0.2883, c = 5.8758 and d = 0.4194. To get T BWBCS it is proposed to sum the values of ECW and ICW calculated

before. Matthie [25] also has introduced another model for estimation of body fluids. In this method, ECW volume is given by equation 2.15, although the way of measurement of kECW is

different, which is in this case measured trough equation2.19.

kECW = 1 1000 k2 BρECW2 DB 13 (2.19)

Then, ICW can be calculated by equation2.20.

ICWMAT T HIE = ECWMAT T HIE

ρT BW(R0+ R∞) ρECWR∞ 2₃ − 1 ! (2.20)

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2.5 Description of Measurement System 17

In [26], Jaffrin et al. have presented another equation model for estimation of TBW. From their assumptions it was obtained equation2.21,

T BWJAFFRIN = ρ∞KBH2 √ W √ DR_∞ 23 (2.21) The difference between this method and XiTRON equation model is on ECW volume estima-tion in which ρ values are replaced from ρECW to ρICW (extracellular to intracellular resistivity)

values. From that study it was estimated that ρICW value is 104.3 Ω.cm for men and 100.5 Ω.cm

for women.

Since TBW is the sum of ECW with ICW, using equation2.22, it is possible to extract those two reamaining values.

1 + ICWJAFFRIN ECWJAFFRIN 5₂ =R∞+ R0 R0 1 + Kρ ICWJAFFRIN ECWJAFFRIN (2.22) where kρ can be measured by kρ=

ρICW

ρECW.

Using one of those methods it would be possible to estimate body fluids volumes, ECW, ICW and TBW, using Cole Parameters.

2.5 Description of Measurement System

Previously, in this project it was developed a system (figure2.9) that implements the Magnitude-ratio and phase-difference detection (MRPDD) method which is an approach that turn possible the application of BIS technique [8]. This system allows the measurement of the phase difference and magnitude necessary to fit the Cole Model in order to then evaluate the fluid status of the lungs.

As shown in figure2.8, this system can be divided in four essential parts, voltage excitation source, signal acquisition part, power circuit and Pandlet Core [10].

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In figure2.9, it is represented the final prototype of the described measurement system.

Figure 2.9: BIS Measurement system. Adapted from [9]

2.5.1 Excitation source

The excitation source consists of a signal generator that is based on the Analog Devices AD9833, which is a low power, programmable waveform generator that will be used for generating multi-frequency sine waves. Then the generated signal passes to an anti-aliasing filter to avoid presence of some alias signal. With this purpose, it was used the LTC1560-1 integrated low pass filter. In this system it was used a 5th order elliptic filter with a low pass cut-off frequency of 1MHz.

As is generated a voltage signal and it is necessary a low amplitude current signal to inject into the body. A Voltage Controlled Current Source (VCCS) is implemented in order to do this conversion from a voltage to a current signal. In this system, the Current-Feedback Amplifier (CFA) AD844 is applied which is a current source able to convert the voltage signals into high-quality controlled current sources over a wide bandwidth (10 kHz to 1 MHz). The result of this excitation source will be the generation of an excitation current within frequency range of 20 kHz to 1MHz that can be injected to the body. In this case frequencies generated are 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 800, 900 and 1000 kHz.

2.5.2 Magnitude-Ratio and phase-difference (MRPDD) detection technique

The signal acquisition module implements the MRPDD method. This method uses tetra-polar arrangement of electrodes and compares potential drop in biological tissues (Zxin figure2.10) and

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2.5 Description of Measurement System 19

gain between the input current and the voltage drop measured. With this purpose, four electrodes have to be used. The current will be injected in Zxand Rsthrough the Hcand Lcelectrodes. Then,

the voltage drop will be acquired by another two electrodes and then respectively amplified in two instrumentation amplifiers (IA), IA1and IA2, in figure2.10. As the current does not flow through

the voltage electrodes due to the high impedance of the IA, the voltage drop can be determined through (2.23and2.24), where A1and A2are the gain of each IA:

VAZ= A1Vz= A1I0ZX (2.23)

VAS= A2Vs= A2I0RS (2.24)

Then the Gain-Phase Detector (GPD) compares the two voltage drop signals and gives as results the Magnitude Ratio (|K|) and the Phase Difference (θ ) between them. With those two values it is then possible to estimate the impedance Zx, equation [8].

Zx= RS Vz VS = RS A2 A₁ VAZ VAS = RS A2 A₁|K| θ (2.25)

In order to implement this method, in this system were used two Texas Instruments INA163 instrumentation amplifiers. It was selected a reference resistor of 200 Ω as this resistance is the typical value of resistance of biological tissues [28]. Input buffers (Texas Instruments THS3061) were used to match the impedance of the amplified signals and of the GPD. The gain phase detector used is the Analog Devices AD8302.

Figure 2.10: MRPDD technique measurement principle. Adapted from [8]

2.5.3 Pandlet Core and Analog-to-digital Converter

Since phase difference and magnitude ration are necessary in the smartphone to be processed, they must be converted from analog to digital signal. For this purpose, an Analog-to-Digital Converter (MAX11612) is used.

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For connecting the smartphone and this measurement system, it is used the Pandlet Core. Pan-dlet is a novel architecture of embebbed electronics and it is specially designed to be used in mobile solutions. Pandlet Core is composed by an inertial measurement unit (IMU) and an environment measurement unit (EMU) as well as Bluetooth Smart (Bluetooth module). This Bluetooth module will be used for the transmission of the measured signal to the smartphone.

2.5.4 Power Supply

Lastly, it has a power supply to provide energy to the system. It includes a battery and a µUSB charger as well as all the necessary components to provide the correct voltage to each system component, especially the excitation source and the Pandlet Core.

2.6 Calibration techniques

One of the required steps before experimental use of the measurement system is its calibration. Systematic errors with origin in electrodes, cables or in input/output channels only can be deleted from final results calibrating the system. To reduce punctual errors a smoothing average would be enough to correct them. Therefore multiple measurements should be done, calculating then the average obtained value [8].

One of the most important noise to avoid are stray capacitances with origin in cables that are near each other. Another possible source of noise can be power line interference with origin in some electrical device which can be near the system [8]. In order to avoid these systematic errors a calibration method should be done.

The objective of the calibration method is to find the relation that exists between measured impedance and the true impedance, taking out all possible systematic errors that can occur. This relation is normally expressed by one calibration coefficient (cf) for each frequency all over the range of frequencies.

One of the most common approaches used to calibrate this type of system, is the measure-ment of one known RC circuit as a ground truth and then measured values are corrected using these values. It was used, for instance, in [29] or in the calibration of XiTRON 4000B. In this method it is used the measured impedance of this RC circuit by the ground truth Ztrueand the

ob-tained impedance by the measurement system Zxto calculate the correction coefficients for each

frequency (cf), equation2.26.

c f( f ) = q Ztrue X_Zx2 + R2_Zx

(2.26)

Then, final calibrated values can be achieved correcting measured values with those coeffi-cients for each frequency, through equation2.27.

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2.6 Calibration techniques 21

where Zx is the calibrated value and Zmis the measured value by the measurement system.

Another calibration method, applied by Yang et al. [8], is a method that uses three-reference circuits for calculating the correction coefficients for each frequency. This method uses the con-sideration that there is a quadratic relation between measured and true impedance values, equation

2.28.

Zx= k2× Z2m+ k1× Zm+ k0 (2.28)

where k2, k1and k0are the correction coefficients, Zmare the measured value and Zxare the

cal-ibrated value. Considering true values, namely values measured by the ground truth (Zr0, Zr1, Zr2),

measured values by the BIS system (Zmr0, Zmr1, Zmr2) for the three known RC circuits and Zm

experimental measured value, the real impedance value can be calculated through equation2.29.

Zx= Zr0 (Zm− Zmr1) (Zm− Zmr2) (Zmr0− Zmr1) (Zmr0− Zmr2) + Zr1 (Zm− Zmr2) (Zm− Zmr0) (Zmr1− Zmr2) (Zmr1− Zmr0) + Zr2 (Zm− Zmr0) (Zm− Zmr1) (Zmr2− Zmr0) (Zmr2− Zmr1) (2.29)

Decomposing equation2.28, it can be obtained the correction coefficients for each frequency, namely through equations2.30,2.31,2.32.

k2= Zr0 (Zmr0− Zmr1) (Zmr0− Zmr2) + Zr1 (Zmr1− Zmr2) (Zmr1− Zmr0) + Zr2 (Zmr2− Zmr0) (Zmr2− Zmr1) (2.30) k₁= Zr0(−Zmr2− Zmr1) (Zmr0− Zmr1) (Zmr0− Zmr2) + Zr1(−Zmr2− Zmr0) (Zmr1− Zmr2) (Zmr1− Zmr0) + Zr2(−Zmr1− Zmr0) (Zmr2− Zmr0) (Zmr2− Zmr1) (2.31) k0= Z_r0Z_mr2Z_mr1 (Zmr0− Zmr1) (Zmr0− Zmr2) + Zr1Zmr2Zmr0 (Zmr1− Zmr2) (Zmr1− Zmr0) + Z_r2Z_mr1Z_mr0 (Zmr2− Zmr0) (Zmr2− Zmr1) (2.32)

Replacing each specific coefficient for each frequency in equation2.28and the experimental measured value, it is possible to correct it in order to obtain the true value of impedance Zx. For

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this three reference calibration method as well as for the one reference calibration method, it is used the measurement of impedance of known RC circuits as shown in figure2.11[8].

Figure 2.11: Typical RC circuit used for calibration.

This type of circuit, approaches the behaviour of human cells as explained previously in section

2.1.

As ground truth to measure the true values of impedance of those RC circuits it is usually used a impedance analyser. As an alternative, can be used another devices that can measure impedance values, for instance oscilloscope or RCL meter. However their measurements will not be as accurate as using a impedance analyser which can decrease the accuracy of the calibrated system.

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Chapter 3

State of the Art

3.1 BIS studies overview

In this section it will be described some studies about different Bioimpedance Spectroscopy ap-proaches on detection of changes in body fluids, more specifically on detection of pulmonary edema for predicting heart failure.

Comparing with thoracic tissues, fluids and blood have lower electrical impedance [30] and for this reason pulmonary edema can be detected by changes in measured impedances. However, measured impedances are dependent of several factors. For instance, blood flow through the heart is one factor affecting impedance values. Position of measurement, electrode disposition, patient movement or body composition are factors that also have influence on the measured values of Bioimpedance [11]. Additionally to these factors, the errors of algorithms of estimation of body fluids make the fluid estimation really difficult. Due to this fact it is necessary to find the best method of measurement and estimation of body fluids. With this purpose local of placement and type of electrodes, method of processing Cole parameters and posture of measurement have to be studied.

3.1.1 Types of electrodes, local of placement and posture of measurement

3.1.1.1 Types of electrodes

One of most important factors influencing Bioimpedance measured values are electrodes. De-pending on the application, different type of electrodes can be used. Associated with mobile solutions for fluid status monitoring using Bioimpedance techniques are often textile dry elec-trodes [29,31–33]. However, for clinical settings, gel electrodes are usually used. Since the gel from those electrodes is an electrolyte, skin-electrode impedance will be lower resulting in bet-ter measurement results. The main problem with this type of electrodes for a long-bet-term use is that often they cause skin irritation and itching, which disallows the possibility of using them in long-term applications [29]. Thus, dry electrodes, namely textile electrodes are often used in BIS mobile monitor methods, despite their higher skin-electrode impedance.

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Figure 3.1: Equivalent circuit of electrode-electrolyte skin interface (a) and equivalent circuit of a textile interface of electrode-electrolyte skin interface (b). Adapted from [34].

In figure3.1, it is represented the equivalent circuit of skin-electrode contacts for textile and gel ones.

It can be seen that equivalent circuits to represent electrode and skin impedance are common between textile and gel electrodes. However, as textile electrodes have no gel connecting it to the skin, they have a stronger capacitive behaviour (Ct) which is added to the equivalent model. Since

the gel is an electrolyte its capacitive behaviour is insignificant [34].

Regarding study of textile electrodes, in [35], different textile electrodes of silvered polyamide fibres mixed with other polyamides were tested. It was evaluated the performance of five types of surfaces. Four of them were roughed surfaces since this type of surface improve the connection between skin and electrode. For this performance evaluation they were compared with standard glued electrodes used for measurements with XiTRON Hydra 4200, most common device for measurement of Bioimpedance. Three of the four structured electrodes have shown less average of skin-electrode impedance than the standard gel electrode. It means that rougher surfaces im-prove contact between skin and electrodes reducing the capacitive behavior in the skin-electrode interface.

In [29], electrodes made of conductive two component polysiloxane LR3162A/B with three different surfaces, plain, porous and ribbed surface, were tested. Two experiences were performed where electrodes were tested in dry and sweating simulation measurements. For dry situations, plain surface electrodes have shown better performance but in sweating simulated conditions rough surfaces have performed better, showing lower contact impedance.

Sensitivity of an wearable BIS monitor using textile electrodes (type non-specified) was stud-ied in [31]. In this study, Bioimpedance from HF patients in absence of medication and in normal medication regimen were compared. Additionally, variance of impedance between different pos-tures was also measured. The monitor was sensible to fluid redistribution between pospos-tures and to the omission of medicines.

3.1.1.2 Local of placement of measurement electrodes

Local of placement of measurement electrodes takes also an important role in the measurement of Bioimpedance and therefore in assessment of fluid overload. There are some studies regarding

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3.1 BIS studies overview 25

different arrangements in electrodes placement in Literature.

Configurations as, foot-foot, thoracic, hand-hand or foot-hand, figure 3.2, are explored in [36,37]. In [37] was studied in decompensated HF patients, Cole parameters features that better distinguish between occurrence of pulmonary edema, peripheral edema and both. Better features were then used to built decision trees for assignment of each case of edema.

Figure 3.2: Different arrangements of electrodes placement. a) Foot - Foot; b) Foot - Hand; c) Hand - Hand; d) thoracic. Adapted from [36].

It was concluded that most important features in peripheral edema were baseline value of the regression line of the extracellular resistance from the transthoracic measurement, baseline value of the regression line of the intracellular resistance from the transthoracic measurement and value of the regression line of the extracellular resistance from the foot-to-foot measurement. For detection of pulmonary edema most important features were rate of change of the regression line of the imaginary part of the impedance at the characteristic frequency for thoracic measurement and mean intracellular resistance from the foot-to-foot measurement. Since all of those features are obtained from foot-foot and thoracic arrangement of placement of electrodes, it was concluded that these are the best configurations for detection of pulmonary or peripheral edema.

In Zink et al. [36], were used the same electrode arrangements, figure3.2, as in [37]. In this study, the relation between changes in Cole parameters R0 and R∞ and body fluid changes after

thoracentesis is studied. It was found a significantly decrease in thoracic measured value of R0after

thoracentesis which shows a relation between this parameter and the extracion of fluid of the lungs. Although, a relation between the parameters is observed, no correlation was observed between R0

measured in thorax and the amount of removed fluid. For values of R0obtained through remaining

arrangements of electrodes no significant relation was observed. Also a moderate correlation was obtained between the amount of removed fluid and R0measured with Foot – Hand configuration.

Since that, a combination of thoracic and Foot-Hand measured parameters, namely R0, should be

a promising method for detection of PE.

Measure Impedance in Congestive Heart Failure Patients

F

E

U

P

Measure Impedance in Congestive

Failure Patients

José Carlos Coelho Alves

Resumo

Abstract

Agradecimentos

Contents

List of Figures

List of Tables

Abbreviations and symbols

Chapter 1

Introduction

1.1

Context

1.2

Motivation

1.3

Objectives

1.4

Initial plan for development of the solution

1.5

Contributions

1.6

Structure of the Document

Chapter 2

Background

2.1

Introduction to Bioelectrical Impedance

2.2

Bioimpedance Spectroscopy and Cole Equations

2.3

Cole parameter extraction

∑

∑

∑

∑

2.4

Body fluids Estimation

2.5

Description of Measurement System

2.6

Calibration techniques

Chapter 3

State of the Art

3.1

BIS studies overview