Developing a low-cost cardiovascular mobile screening kit

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Developing a Low-Cost Cardiovascular Mobile

Screening Kit

by

Botong Ma

Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of

Master of Engineering in Electrical Engineering and Computer Science at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2019

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now

known or hereafter created.

Author . . . Department of Electrical Engineering and Computer Science

Feb 1, 2019

Certified by . . . Richard Ribon Fletcher

Research Scientist Head, Mobile Technology Group MIT D-Lab Thesis Supervisor

Accepted by. . . Dr. Katrina LaCurts, Chair, Masters of Engineering Thesis Committee

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Developing a Low-Cost Cardiovascular Mobile

Screening Kit

by

Botong Ma

Submitted to the Department of Electrical Engineering and Computer Science on February 1, 2019, in partial fulfillment of the

requirements for the degree of

Master of Engineering in Electrical Engineering and Computer Science

Abstract

Cardiovascular disease (CVD) is the leading cause of mortality worldwide, and 80% of CVD deaths occur in lower and middle-income countries. While many CVD risk factors can be

improved by behavioral change or low-cost medication, a major challenge remains in identifying at-risk patients since most people are asymptomatic. Thus, low-cost non-invasive diagnostic tools are crucial in low-resource areas without routine blood tests or regular clinical exams. This thesis presents a low-cost cardiovascular screening kit that focuses on signs of arterial stiffening, the root issue of many CVDs. Since pulse wave velocity (PWV) and pulse wave analysis (PWA) features were known to be correlated with arterial stiffening, we developed a Python API that would extract these features from the pulse waveforms collected using the devices in our screening kit. Using these features, we also trained a machine learning algorithm to accurately identify patients that are at-risk. We confirm the usefulness of PWV and PWA features for CVD screening, and anticipate that as the number of training data points increase, our machine

learning model will enable individuals to live a healthier lifestyle. .

Thesis Supervisor: Dr. Richard Fletcher Title: Research Scientist

Head, Mobile Technology Group MIT D-Lab

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Acknowledgements

First, I would like to thank Dr. Richard Fletcher, who has been a constant dedicated presence in this project, for introducing me to the world of research in between medicine and technology, and for his encouragement and guidance. I am sincerely grateful for the opportunity of working on this project and for the countless things I have learned along the way.

I would also like to thank the doctors and nurses at the Sengupta Hospital and Research Clinic, in particular Dr. Sengupta and Dr. Kunda, for their help and hospitality.

Thank you to the UROPs that have worked on this project, and made it possible.

Many thanks to my lab mates, who constantly inspire me.

I also thank my friends, for the support and joy they have brought to my life.

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List of Figures

Figure 1.1: Three interconnected vantage points p.17

Figure 1.2: The Human Heart and Cardiac Cycle [Baig 2010] p.18

Figure 1.3: The Wigger diagram p.20

Figure 1.4 : Distribution of CVD deaths due to heart attacks, strokes, and other types of CVDs

[WHO 2011] p.22

Figure 1.5: Causal Chain of CVD Risks [WHO 2009] p.25

Figure 1.6: Example of the WHO Risk Scoring Chart p.27

Figure 3.1: The mobile stethoscope [Pignatelli 2017] p.37 Figure 3.2: The microwave doppler and a collected signal p.38 Figure 3.3: Mobile application and a sample records finger PPG signal p.39

Figure 3.4: External PPG Device p.39

Figure 3.5: The NAJA device, used to collect three different PPG signals p.41 Figure 4.1: Absorption rates of oxygenated hemoglobin (red) and deoxygenated hemoglobin.

[Nitzan et al 2014] p.43

Figure 4.2: Components of a PPG signal [Tamura et al 2014] p.45 Figure 4.3: Examples of Transmission PPG (left) and Reflectance PPG (right) p.45 Figure 4.4: The maximum PPG signal occurs where PTM=0 [Shaltis 2007] p.47

Figure 4.5: Typical PPG waveform p.49

Figure 5.1: A PPG waveform and its components p.52

Figure 5.2: The second derivative PPG waveform and its subsegments. p.54 Figure 5.3: Analogy between Windkessel pump and cardiovascular system

[Vieira et al 2015] p.55

Figure 5.4: The different Windkessel model circuits p.56

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Figure 6.2: Classification of PPG pulse waveforms first proposed by Dawber et al. With increasing age, arterial stiffness, and cardiovascular pathologies, the human pulse

waveform evolves from Class 1 towards Class 4 p.61

Figure 6.3: Seven different categories of waveforms that stratify quality of circulation

[Elgendi 2012] p.64

Figure 7.1: Depiction of a 1863 sphygmograph, along with the recordings it took

[Avolio 2010] p.66

Figure 7.2: SphygmoCor applanation tonometry sensor [Nelson et al 2010] p.67

Figure 7.3: Example Pulse Oximeter p.68

Figure 7.4: The Smartphone Camera PPG p.69

Figure 8.1: Finding PWV using a combination of ECG and PPG [Pilli et al 2012] p.71

Figure 8.2: Intersecting Tangent Method p.73

Figure 8.3: Showcasing the PPG peak method (1), the minimal diastolic pressure method (2), the maximum gradient method (3) and the maximum second derivative method

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Figure 8.4: Diastole Patching Technique p.75

Figure 9.1: Age-Related Changes in Brachial and Aortic PWV [Rourke et al 2002] p.80 Figure 9.2: Clinical conditions associated with increased arterial stiffness

[Laurent et al 2006] p.82

Figure 10.1: Blood Pressure Regulation. The arrows indicate an up- or down-regulation

of parameters. [Lilly 2016] p.85

Figure 11.1: The SphygmoCor in action p.94

Figure 11.2: Complior device for measuring PWV p.94

Figure 11.3: Blumio blood pressure cuff p.95

Figure 11.4: The Seismo app in action p.96

Figure 11.5: NAJA clips with master board and cell phone application p.99

Figure 12.1: Smartphone PPG Screenshots p.101

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Figure 13.1: Using the NAJA device on a patient p.108 Figure 13.2: Collecting data using the Smartphone Camera PPG Application p.109 Figure 14.1: The Workflow for Processing Collected PPG Data p.111 Figure 14.2: Original (red) and Resampled (blue) Waveforms for Ear (right),

Finger (middle), and Toe (right) PPGs p.112

Figure 14.3: Original (blue) and Filtered (green) waveform after filters p.114 Figure 14.4: Correlated waveform with zero crossings and found peaks p.115 Figure 14.5: Separation of individual wavelets expressed as vertical green dotted lines p.116

Figure 14.6: Sample average PPG waveform p.117

Figure 14.7: (a) Systolic and Diastolic Peak in the Original PPG signal, and (b) First

Derivative Wave of the PPG and zero-crossings [Elgendi 2012] p.117 Figure 14.8: An illustration of increasing rising slope with age and reduced arterial

distensibility [Moxham 2003] p.118

Figure 14.9: Example 10-fold cross validation for a data set p.121 Figure 15.1: Median SD Delay over different levels of cardiovascular health p.124 Figure 15.2: Median Rising Slope over different levels of cardiovascular health p.125

Figure 15.3: Median PWV_ET Over Age p.126

Figure 15.4: Median PWV_EF Over Age p.126

Figure 15.5: Graph of the different coefficient values for the best scoring model that

uses all 10 features. p.128

Figure 16.1: Median MAP over different antihypertensive therapies; the normal range for MAP lies between 70-110 mmHg, which all of the measurements do p.134 Figure 16.2: Median PWV over different antihypertensive therapies; note that while

BP readings may be in the normal range, the PWV values show the progression of

arterial stiffness beyond normal ranges p.134

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Chapter 1: The Global Burden of Cardiovascular Disease (CVD)

1.1 The Cardiovascular System

The human cardiovascular system, comprised of the heart and the circulatory system, is essential for life. There are three interconnected vantage points from which to examine the cardiovascular system: mechanical, electrical, and vascular. Put into perspective, the heart serves as a mechanical pump for blood, and is controlled by a series of cascading electrical impulses. The blood travels through the vascular system, which in turn can affect the mechanical action of the heart [Saladin 2001].

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Figure 1.1: Three interconnected vantage points 1.1.1 Cardiac Cycle

The heart remains the center of the cardiovascular system, and the cardiovascular cycle, which is the sequence of events that occurs as the heart undergoes a complete beat. There are two main parts to this cycle, termed systole, which occurs as the heart contracts, and diastole, which occurs as the heart relaxes.

In a typical heart, there are four main chambers: the left ventricle, the left atrium, the right ventricle, and the right atrium. There are also four main valves: two atrioventricular valves termed the mitral and the tricuspid valve which govern the flow of blood between the atria and ventricles, and two semilunar valves termed the pulmonary and aortic valve that govern the flow of blood between the heart and the arteries. The main function for these valves is to prevent the backflow of blood as it moves between different partitions of the heart during the cardiac cycle.

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Figure 1.2: The Human Heart and Cardiac Cycle [Baig 2010]

In Figure 1.2, the cardiac cycle is represented as a series of arrows. The blue arrows represent the flow of deoxygenated blood into the heart during the first diastolic and systolic period; the red arrows represent the flow of oxygenated blood during the second diastolic and systolic period. Although termed the first and second systolic and diastolic phases, these processes actually occur simultaneously during a single systolic and diastolic cycle, just on different sides of the heart [Lilly 2016].

The cardiac cycle can be organized into specific steps, called “periods,” which are described as follows:

1) _{First diastolic period: At the beginning of the cardiac cycle, both the atria and the} ventricles are in diastole, allowing for the passive filling of oxygen-depleted blood into the right atrium. From there, it passes through the atrioventricular valves into the ventricles.

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2) _{First systolic period: Electrically, an impulse had caused both atria to contract,} ensuring that blood moves from the right atrium into the right ventricle during the first diastolic period. Next, another impulse will cause the ventricles to contract, pumping oxygen-depleted blood into the pulmonary artery and the lungs. The oxygen-replenished blood then returns to the heart via the pulmonary veins, which marks the end of the first systolic period.

3) _{Second diastolic period: In this period, the oxygenated blood fills the left atrium. As} the atria contract, the oxygenated blood moves into the left ventricle.

4) _{Second systolic period: During this period, the left ventricle contracts and pumps its} blood into the body.

1.1.2 The Three Frameworks to Describe the Cardiovascular System

As shown in the cardiac cycle, the cardiovascular system is intricate, and comprised of many interconnected components whose effects on each other we will examine in more detail in future sections of this work. The functionality of the cardiovascular system can be described from three different perspectives, described below:

1) From the mechanical perspective, the heart is a pump that takes in deoxygenated blood and outputs oxygenated blood via continuous contractions and relaxation. The blood flow is in part regulated by a series of valves, controlled by the hemodynamic pressures of the heart. For example, as the pressure of the ventricle overrides the pressure of the left atrium, the mitral valve closes, reopening when the pressure differential is reversed. Similarly, the aortic valve opens when the ventricular pressure is greater, and closes

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when its not. This concept is expressed in the Wiggers Diagram below, which attempts to model the mechanical pump-like actions of the heart with its synchronized waveforms.

Figure 1.3: The Wigger diagram

2) From the electrical perspective, the timing and modulation of the heart’s rhythm is controlled by a cascade of electrical impulses, generated by pacemaker cells.

3) From the vascular perspective, the blood vessels serve as the transport component of the cardiovascular system, bringing fresh nutrient-filled blood to the rest of the body, while cycling deoxygenated blood back to the heart.

In order to completely characterize and assess the cardiovascular system, all three perspectives much be examined in order to determine if any pathological condition is present.

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1.2 Pathophysiologies of the Cardiovascular System

1.2.1 What is CVD?

Cardiovascular disease, or CVD, refers to any disease of the heart or the blood vessel. Being such a deeply connected composite of interdependent parts, any falter in a single

component of the cardiovascular system will inevitably lead to issues that spread over the whole.

1.2.2 Types of CVD

For the purpose of this thesis, we will discretize CVDs according to the tripartite framework introduced earlier this section, and broadly generalize cardiovascular conditions as either failures in the mechanical, vascular, or electrical elements of the cardiovascular system.

Mechanical Vascular Electrical

Cardiomyopathies Hypertension Congenital Abnormalities

Valvular Failures (and Rheumatic Heart Disease)

Atherosclerosis Aortic Dissections &

Aneurysms

Arrhythmias Other Conduction Issues

Table 1.1: Different classifications for CVDs

Although this classification suggests that the greater part of CVDs are mechanical issues, vascular issues are the most common amongst the general population. It should be noted that atherosclerosis, a vascular disease in which plaque accumulates inside a patient’s arteries over

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time, are the precursor to heart attacks and strokes, which account for over 80% of CVD mortalities in both men and women according to a WHO 2011 report [WHO 2011].

Figure 1.4 : Distribution of CVD deaths due to heart attacks, strokes, and other types of CVDs [WHO 2011]

1.2.3 Risk Factors for CVD

Known risk factors for CVD include tobacco use, physical inactivity, unhealthy diet, obesity, hypertension, diabetes, as well as aging and genetic factors. These are typically grouped into two categories: modifiable and unmodifiable.

Unmodifiable risk factors include age, gender, and genetic factors. As people age, the risk of heart disease increases. In addition, men have a higher risk for CVD compared to women; however, the risk for women increases after menopause [Yusuf et al 2004]. With regards to genetics - individuals whose close family members have had CVD are at a higher risk for CVD themselves, as well as individuals who are South Asian or African American [WHO 2011].

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The INTERHEART study in 2004, a major Canadian-led global study, identified 9 modifiable risk factors listed below in Table 1.2 that account for over 90% of the risk of a heart attack regardless of gender, ethnicity, or geographic region [Yusuf et al 2004].

Factor Description

Tobacco Usage The increase in risk is directly correlated to the amount of tobacco usage.

Excessive Alcohol Consumption

Excessive alcohol consumption can increase the risk of high blood pressure, which is another risk factor.

Poor Diet A poor diet is often at the root of many CVDs, since it may lead to obesity, abnormal blood lipids, or diabetes, etc. A diet low in cholesterol, saturated and trans fat, and simple sugars will reduce CVD risk in patients.

Low Physical Activity Regular physical activity is recommended for cardiovascular health. Without this, individuals suffer increased risk of diabetes, obesity, and high blood pressure.

Hypertension Hypertension is a condition where an individual’s blood pressure is higher than average. According to WHO’s Global Atlas, 13% of global deaths can be attributed to this risk factor. [WHO 2011] Diabetes Diabetes increases the amount of atherosclerosis-related

inflammation, and patients with diabetes are twice as likely to have a heart attack or stroke. [Hewitt 2012]

Obesity According to WHO’s Global Atlas, 5% of global deaths can be attributed to this risk factor. [WHO 2011]

Abnormal Blood Lipids Abnormal blood lipids, such as high counts of LDL Cholesterol, are directly correlated to an increase in CVD risk.

Psychosocial Factors Examples include depression, stress, and other life events. Table 1.2: Risk Factors for CVD [Yusuf et al 2004, WHO 2011, Hewitt 2012]

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1.3 Defining the Need for CVD Screening

1.3.1. CVD as a Global Burden

Globally, CVDs are a leading cause of death, accounting for around 17.7 million deaths in 2015, a number that is expected to grow beyond 23.6 million deaths by 2030 [WHO 2017, Smith 2012]. In particular, they are a rising issue in lower and middle income countries, with 80% of these deaths occurring in such nations. In 2010 alone, the worldwide direct and indirect cost of CVDs was about $863 billion, and is expected to rise to about $1.044 trillion by 2030. Overall, it is estimated that the cumulative direct cost of CVDs will exceed $7 trillion between 2011 and 2025 [Nordqvist, 2011].

1.3.2 The Existence of Low Cost Medications and Therapies

Cardiovascular disease is generally treated through a combination of medication as well as behavior or lifestyle modifications. This combination varies to come extend depending on the stage of the disease, as well as the country and the socio-economic status of the patient and health care system. Right now, there exists a plethora of low-cost treatments and therapies for CVDs, ranging from lifestyle changes to low cost medications. For patients that are caught in the early stages of CVD, introducing them to such therapies will be a low-cost and effective method of care.

Behavioral changes are a cornerstone of CVD therapy, as it decreases risk factors in addition to addressing the fundamental causes for CVD, while medication can only serve as a reactive strategy to CVDs. Risk factors that can be easily modified via behavioral changes

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include diet, physical activity, and alcohol and tobacco usage, with major benefits to the patients, particularly since they are at near the beginning of the causal chain for CVD risk factors [WHO 2009].

Figure 1.5: Causal Chain of CVD Risks [WHO 2009]

Additionally, individuals who do not have these risk factors tend to have 66% lower all-cause mortality compared to those who did [Alageel 2016]. Below, we have included a table that outlines how changing separate behavioral factors reduces the risk for cardiovascular mortality rates.

Behavioral Change Risk Reduction

Increased Physical Activity 30-50%

Cessation of Smoking 50%

Healthy Diet 31%

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In terms of medication, the most common forms of CVD medication include

beta-blockers, low-dose aspirin, ACE inhibitors, and statins [Wirtz et al. 2016]. According to the World Health Organization (WHO), using these medications together with smoking cessation may prevent nearly 75% of recurrent vascular events [WHO 2017]. In addition, there has been new developments such as the combination fixed-dose pills (poly-pills) that could potentially reduce coronary heart disease events by 88% [Lopez-Jaramillo 2017]. It should be noted that most of the common medications are fairly inexpensive and widely available.

1.3.2 The Need for Screening Technology

While the medication and behavior modification used to treat CVD is readily available and affordable, the greatest challenge in addressing CVD mortality is the fact that the two most prevalent morbidities (heart attack and stroke) are largely asymptomatic. Since many people with early stage atherosclerosis may feel reasonably healthy, this segment of the population does not seek medical care, and this can lead to an unpredicted heart attack or stroke. For this reason, the primary need for preventing CVD is to develop better tools, coupled with policies and public awareness to better identify individuals that are at risk of developing CVD.

Over the past few decades, a variety of different CVD risk assessment methods have been developed. Perhaps the most well known of these is the Framingham score, produced from the famous Framingham study, which was a prospective study of heart disease spanning several decades [Mahmood et al 2014]. The Framingham score takes as input several measurements from a person, including blood pressure, blood cholesterol, and tobacco usage, etc., and

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produced a score from 0 to 60 which predicts the likelihood of developing cardiovascular disease.

More recently, the WHO has developed a multiple factor screening chart that can be used to identify individuals at risk based on their gender, tobacco usage, age, blood pressure, blood cholesterol, and diabetes status [WHO 2007]. A sample of this chart is shown in Figure 1.6 below.

Figure 1.6: Example of the WHO Risk Scoring Chart

While risk assessment scores such as the Framingham and WHO are available for CVD, a significant limitation of these scores is that they require information that is not easily obtained without clinical training or laboratory facilities, such as blood pressure or blood cholesterol

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levels. In low resource regions, there are often a paucity of laboratory equipment capable of extracting all the necessary factors for this screening test to be significant (e.g. blood pressure and cholesterol readings). Thus, there remains a significant need for additional screening technologies for the early detection of CVD that can be used in low-resource settings.

1.4 Scope and Outline of this Thesis

In this thesis, we explore solutions to this problem that have been developed by Dr. Rich Fletcher’s group at MIT. In particular, I explore the use of photoplethysmography (PPG) and discuss various algorithms for applying this tool to the assessment of cardiovascular disease risk.

In Chapter 2, I present a brief overview of various screening tools that would also address the gap in screening for atherosclerosis, and I introduce the prior work done developing a mobile CVD screening kit that makes use of various measurements extracted from the PPG device.

In Chapters 4-9, I present some background on the development of PPG and the fields of Pulse Wave Analysis (PWA) and (pulse Wave Velocity (PWV), before we go into detail about their clinical uses, and factors that may affect analysis done with these metrics.

In Chapter 10-15, I discuss the implementation of these new metrics and their

performance in clinical trials, and conclude with a discussion on our research and its implications not just for global health, but also for CVD screening in general.

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Chapter 2 : The Need for Specialized Screening Tools

In this chapter, we will survey the existing screening tools currently available, and highlight a gap in the conditions that are being addressed by these methods.

2.1 Existing CVD Screening Tools and Risk Scores

Cardiovascular risk scores are one of the primary screening tools for clinicians and healthcare workers, helping them identify individuals at risk or in need of additional screening. Introduced in Chapter 1, one of the first cardiovascular screening scores, the Framingham Risk Score, was derived from the Framingham Heart Study, a long-term study on cardiovascular health assessment which began in 1948 and involved 5209 individuals. It was originally planned

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that the Framingham Study would last for 20 years; however, due to its value in understanding the progression of CVD, the study is still ongoing today [Mahmood et al 2014].

In 2016, a systematic review found 363 proposed models, although many were labeled inadequate due to shortcomings in method, lack of external validation, or lack of model impact studies [Damen et al 2016]. Despite the high number of CVD risk scoring models available, the Framingham Risk Score remains one of the most popular and most often validated models.

Despite its popularity, one of the major limitations of the Framingham Risk Score is its lack of ethnic diversity, with the participants being primarily white males of European descent. As a result additional scoring systems have been developed to address risk in other

demographics. Below, we address some popular alternative models, such as the QRISK2, WHO, and Reynolds Risk Scores.

Risk Score Factors Description

QRISK2 Score Gender, Age, Ethnicity, UK postcode, BMI, HDL Cholesterol, Systolic Blood Pressure, Tobacco Use, Diabetes Status, Past Heart and Kidney Events, Arthritis Status.

Similar to the Framingham Score, the QRISK2 Score is calibrated to the UK population, but also includes socio-economic deprivation. [Cox et al 2007] WHO Risk

Prediction Score

Gender, Age, Blood Pressure, Diabetes Status, Cholesterol

The WHO risk prediction charts provides risk measurements for different regions of the world [WHO 2007]

Reynolds Risk Score

Age, Blood Pressure, Diabetes Status, Tobacco Use, Total Cholesterol, HDL Cholesterol, hsCRP, Family History

Designed to better account for CVD risk in women over age 45; includes family history and C-reactive protein test. Table 2.1: Popular CVD Risk Scoring Systems

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2.2 Shortcomings of Existing CVD Screening Tools and Risk Scores

The major drawback of cardiovascular risk scores like the Framingham Score is their dependency on knowing blood pressure and cholesterol, two measurements that are difficult to obtain without prior clinical training or equipment.

Although blood pressure is used ubiquitously, obtaining an accurate measurement can be difficult. A variety of factors may cause errors in blood pressure measurement, such as the posture of the patient, the location and size of the cuff, or the accuracy of the device itself

[SCENIHR 2009]. Finding an individual’s cholesterol involves performing an invasive blood test and requires access to a laboratory testing facility, a luxury that many people do not have,

especially in low-resource regions. Even though the cardiovascular risk scores were designed to be accessible to a widespread population, the use of blood pressure or cholesterol levels in the calculations has prevented a significant number of people from understanding their

cardiovascular risk.

2.3 Current Mobile Technology Solutions

2.3.1 Wearable Sensors for Cardiovascular Monitoring

Wearable health monitoring devices have become increasingly prevalent for helping both individuals for self-health tracking and clinicians for diagnostics and treatment. Wearable heart health monitors today cover a plethora of biological signals in a variety of ways, from the

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common heart rate monitor band to a ECG monitoring patch for the body. Below in table 2.2, we outline some recently developed wearables for cardiovascular health.

Device Description Measured Signals

Bioharness (Zephyr Inc)

A wearable chest belt that remotely monitors an individual’s performance and condition

ECG, respiration, activity

MyHeart Designed for the prevention and early diagnosis of CVD, the MyHeart project uses conductive yarns and sensors on clothes and a belt to measure an individual’s ECG and respiration.

ECG, respiration, other vital signs, activity

SEEQ Mobile Cardiac Technology (Medtronic)

An external adhesive continuous heart monitor for arrhythmias, this monitor can be worn for up to 30 days and automatically sends

important cardiac data to a monitoring center. Recently, Medtronic has discontinued

production of this to focus on the LINQ System, which is designed to monitor patients for up to 3 years. [Medtronic 2018]

Heart rate

AMON Cardiac monitoring device worn on the wrist for high-risk cardiac-respiratory patients

ECG, blood pressure, temperature, SpO2

Ring Sensor [Asada et al 2003]

A wearable PPG ring sensor that is capable of monitoring a patient’s cardiovascular health.

Heart rate, oxygen saturation, heart rate variability.

Table 2.2: Wearable heart health monitoring devices. [Pantelopoulos et al 2010] 2.3.2 Mobile Apps for Cardiovascular Health

Mobile technology has also become extremely widespread over the past decade as smartphones become increasingly prevalent throughout the world. Generally, mobile tools for

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cardiovascular health are presented either as a sensor and application combination, or as a self-contained application on the mobile phone.

Many of the mobile-only applications typically only measure an individual’s heart rate using the smartphone camera, such as the Instant Heart Rate App by Azumio. While there are apps that claim to measure blood pressure using only the smartphone, validation studies have found these applications to be highly inaccurate, where up to 77.5% of individuals with hypertensive blood pressure levels would be falsely classified as nonhypertensive [Plante et al 2016]. Recently, an mobile-only application named Cardiio Rhythm that screens for atrial fibrillation using the smartphone camera was developed and validated [Yan et al 2018].

Combination mobile apps and external sensors tend to cover a broader range of

conditions. Withings has developed an app that connects to a wireless blood pressure cuff that translates readings into color-coded feedback. Lumify and Phillips both provide the ability to perform an echocardiogram from a mobile device by downloading an app and connecting a transducer, facilitating broader screenings of previously unreachable populations [Ellis 2016]. AliveCor has developed a single-channel cardiac event recorder consisting of a device and an application allowing individuals to view and record ECGs.

2.4 Shortcomings of Current Mobile Technology Solutions

The screening tools and technologies reviewed above tend to focus on blood pressure, heart rate, and ECGs. While helpful, these metrics do not directly screen for atherosclerosis. For

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example, high blood pressure may be correlated with atherosclerosis, but cannot serve as proof that it exists. Similarly, while cardiovascular risk scores may brush against some of the root causes of atherosclerosis, they do not address the whole picture. However, atherosclerosis is the precursor to heart attacks and strokes, which account for about 80% of all CVD mortalities in both men and women [WHO 2011]. Thus, despite the many different types of screening tools and technology currently being developed, the lack of targeted and immediate atherosclerosis screening needs to be addressed.

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Chapter 3: A Proposed Solution

In this chapter, we propose a holistic cardiovascular screening tool that also addresses the gap identified in chapter 2. We will begin by discussing prior work done by our lab on the CVD Screening Kit, then introduce new developments to the work.

3.1 Prior Work from the Mobile Technologies Lab

Our lab, the Mobile Technologies Lab, has developed a CVD Screening Kit that would screen the mechanical, electronic, and vascular health of an individual in order to provide a holistic cardiovascular screening.

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3.1.1 WHO Screening Application

A WHO screening application was developed where clinicians or patients could fill out an individual questionnaire on the mobile phone, and easily understand their level of risk as determined by the WHO CVD Screening Score. But because this is a general screening guide and does not measure the exact extent of CVD progression, and because this guide requires an invasive blood test to determine the level of cholesterol as one of its steps, we kept the WHO score as a low-level reference calculation that can be supplemented with additional devices for a detailed, non-invasive assessment of an individual’s cardiac health.

3.1.2 Heart Sounds

To diagnose mechanical problems of the heart such as valve regurgitations or failures, we designed a mobile stethoscope that listens to and records heart sounds. In addition, we trained a machine learning model to recognize abnormal heart sounds recorded by this device.

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3.1.3 Microwave Doppler

The microwave doppler was developed as a low cost alternative to the heart

echocardiogram. By recording the impedance and reflected power from a probe placed on the chest, we are able to understand the movement of the heart muscle.

Figure 3.2: The microwave doppler and a collected signal

3.1.4 Photoplethysmographic (PPG) Hardware

Our lab has also designed several different versions of PPG hardware and mobile applications. First, we have a mobile application that records a finger PPG signal using just the smartphone camera. We can then perform pulse wave analysis (PWA) on the collected signal.

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Figure 3.3: Mobile application and a sample records finger PPG signal

Second, we have designed an external PPG device, which can be mounted into a clip that can attach to an individual’s finger, ear, or toe. By attaching three of these devices to a central board and connecting it to the smartphone, we are able to measure a patient’s pulse wave velocity (PWV), which is a measure of how fast a blood pressure wave travels. Faster PWVs tend to indicate stiffer blood vessels.

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We will be discussing PPG, PWA, and PWV in further detail later, as the majority of our continued work has focused on this area of cardiovascular diagnostics.

3.2 Contributions of This Thesis

In this thesis, I present several new contributions in the field of cardiovascular screening tools and algorithms that can be implemented using low-cost tools. These individual topics are

described below.

3.2.1 Pulse Wave Analysis (PWA)

In prior work, a smartphone application to collect and record finger PPG signals was developed. Now, we will be investigating the different features that can be extracted from the PPG signal through the process known as Pulse Wave Analysis (PWA), and the implications of these different features for a subject’s arterial health. In Chapter 4, I present the physics and mechanisms of PPG, and in Chapters 5-7, I introduce and review new methods and tools we have developed for PWA.

3.2.2 Pulse Wave Velocity (PWV)

Our lab has developed a device named the NAJA, which is comprised of a main interface board and three external PPG probes described above. It is capable of recording and measuring signals from the ear, finger, and toe, allowing us to calculate the PWV between the three sites. In Chapters 8-10 I discuss PWV in more detail, including its application to CVD diagnostics, and various other factors that may affect PWV.

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Figure 3.5: The NAJA device, used to collect three different PPG signals 3.2.3 Machine Learning

Recently, machine learning has gained popularity as a way to provide computer systems the ability to recognize patterns and learn from data with the end goal of automatic accurate classification. In Chapter 11 and 12, I review the hardware and tools developed by our group at MIT, and I present the clinical study used to evaluate our technology and algorithms in Chapter 13. In Chapters 14-15, I present several machine learning models that I developed using the data from our clinical trials and present the best features to use for a robust classification model for CVD prediction.

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Chapter 4: Photoplethysmography (PPG)

4.1 Overview

A plethysmograph is used to measure changes in blood volume in the human body. There are many different types of plethysmographs: water, air, impedance, strain, and photoelectric. In this chapter, we will discuss the science behind photoplethysmography (PPG), and the different types of analysis that are possible with this technique.

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4.2 Fundamentals of Photoplethysmography (PPG)

The basic PPG measurement configuration consists of a light source and photodetector, in which the optical path is designed to pass through the human tissue containing blood vessels (generally capillaries). The photodetector can be configured to detect the amount of light that is reflected or absorbed by the human blood and tissues.

The absorption spectrum of light depends on the wavelength of the light as well as the optical path length. As blood pulses in the capillaries, the tiny increase in the blood vessel volume can be observed as a change in the amount of light received at the photodetector. In addition, the amount of light absorption also depends on the tissue composition and on the hemoglobin species present in the blood. For example, melanin absorbs shorter wavelengths of light, while water absorbs wavelengths in the ultraviolet or far IR wavelengths but allows red and near-IR light to pass through [Tamura et al 2014]. A typical light absorption spectrum is shown in Figure 4.1 below.

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There are three optical considerations taken into account when choosing the wavelength of a light source - the optical water window, tissue penetration depth, and isosbestic wavelength [Allen 2007]. PPG Measurements are generally taken in the “optical water window”, which is the range of optical wavelengths in which water and human tissue are relatively transparent. This corresponds to wavelengths in the range of 500 nm - 900 nm, which is in the visible and near-infrared color range. The longer wavelengths can penetrate deeper into the tissues (centimeters or more); however, the water and tissue absorption limit the penetration of light beyond the near-infrared range.

For the purpose of measuring the level of oxygen saturation, PPG devices are most commonly used as pulse oximeters. In this case, it is desirable to know the ratio of oxygenated blood to the total amount of blood. For this measurement, two different light wavelengths are used: one wavelength where the light absorption of the oxygenated and deoxygenated blood are equal (also known as the isosbestic point), and another wavelength where the light absorption levels are different. By measuring the ratio of the pulse amplitudes at different wavelengths, the oxygen saturation value can be estimated. Other methods can also be used to estimate the oxygen saturation value.

Most changes in blood flow occur in the arteries during the systolic phase, which

contains more blood flow than the diastolic phase. These changes in blood flow between phases are expressed as the pulsatile “AC component” of the collected PPG signal, while the slowly

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varying “DC component” of the signal represents the structure of the arteries and the average volume of blood [Tamura et al 2014].

Figure 4.2: Components of a PPG signal [Tamura et al 2014]

There are two ways to collect PPG signals - reflectance PPG and transmission PPG. For reflectance PPG, the sensor detects light reflected from tissue and bone, while for transmission PPG, light is transmitted through tissue to the sensor. Generally, transmission PPG sites will be limited to fingertips, earlobes, and other parts of the body where transmitted light would be capable of passing through, while reflectance PPGs have more freedom of placement [Gorczewska 2012].

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4.3 Factors that Affect PPG Measurements

While PPG recordings are convenient and non-invasive, there are several factors that affect the quality of PPG recordings, such as measurement site, relative height, contact force, subject motion, or ambient temperature and noise.

Factor Description

Measurement Site The fingers, palms, face, and ear all have high perfusion values compared to other measurement sites, with the earlobe having the most perfusion.

Height of measurement relative to heart

The relative height between the measurement site and the heart will determine the hydrostatic pressure component of the pulse pressure.

Motion Artifacts It is possible for miniscule motions to cause motion artifacts in the recording.

Contact Force Insufficient force results in low-amplitude PPG recordings, while too much force would distort the signal. Studies have found that optimal contact pressure corresponds to low transmural pressure, or the pressure difference between the inside and outside of the blood vessel.

Ambient Temperature and Optical Noise

Ambient room temperature will affect the recording baseline, as well as any ambient light noise, such as flickering lights or ceiling fans. Typically, this type of noise can be filtered out. Table 4.1: Factors that affect PPG signal recordings [Elgendi 2012, Tamura et al 2014]

4.4 The Relationship Between the PPG Waveform and Blood Pressure

It is important to note, in particular, how the PPG waveform is dependent on blood pressure and on the contact force applied by the light source and detector on the skin. The

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amplitude of the pulsatile PPG signal is dependent on the transmural pressure, P_TM, which is defined as the sum of the arterial pressure, the externally-applied pressure on the skin, and the hemodynamic pressure due to the relative height between the heart and the PPG measurement site. Keeping in mind that the PPG signal amplitude is proportional to the change in volume, it is possible to see that the maximum PPG signal occurs at the point where P_TM=0, as shown in Figure 4.4.

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At this point, the externally-applied pressure exactly balances the internal arterial pressure and hydrostatic pressure. Devices such as the Finapres (Finapres Medical Systems, Netherlands) have exploited this principle by designing a machine that uses the PPG signal to dynamically servo the pressure in a finger cuff in order to maintain zero transmural pressure and measure BP. This method of measuring BP is also known as the Pinaz method [Molhoek et al 1984, Wesseling et al 1980].

4.5 Traditional and Modern PPG Equipment

Traditionally, PPG is measured using a light source and a photodetector. Depending on the measurement site and the type of PPG measurement, there are a variety of different PPG devices designed to optimize for different conditions. PPG devices with infrared LEDs are most often used to blood flow, while PPG devices with green LEDs are used to measure the

absorption of oxygen in an individual’s system. The most common measurement sites for PPG are the forehead, ear, torso, fingertip, wrist, ankle, and toe [Castaneda et al 2018].

There are also a variety of different commercial PPG equipment being developed today, such as the Masimo SET for pulse oximetry, the Finapres PPG system for blood pressure monitoring, and the Nonin pulse oximetry sensors.

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Product Description

Masimo SET Masimo claims to provide the best pulse oximetry

technology due to their use of special algorithms that help process PPG signals, as well as their use of 7 different wavelengths to measure hemoglobin oxygenation.

Finapres PPG Using a double finger cuff system, Finapres claims to have the ability to measure an individual’s blood pressure, blood pressure curve, heart rate, and interbeat interval.

Nonin Pulse Oximetry Nonin also uses red and infrared light, in addition to their own signal processing technology to provide the best readings for each individual.

Table 4.2: Different Commercial PPG Products

4.6 Properties of the PPG Waveform

Once the PPG signal is collected, a variety of different analyses may be performed to

characterize the waveform and apply this to health diagnostics. Such analysis on the PPG

waveform is also known as pulse wave analysis, or PWA. The basic PPG analysis examines the contours of the collected signal, such as the rising edge, known as the anacrotic phase, and the falling edge, known as the catacrotic phase. By examining the peak-to-peak interval in a PPG waveform, it is possible to extract the subject’s heart rate, and heart rate variability.

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In the PPG waveform from a young healthy individual, the systolic and diastolic components of the pulse will be separated by an undulation, which is informally called the “dicrotic notch”. (in a traditional invasive arterial blood pressure measurement, the dicrotic notch is actually defined as dip in the pressure waveform that appears when the aortic valve closes). Typically, a dicrotic notch will be present if the subject has healthy and compliant arteries.

If multiple PPG probes are used, it is also possible to measure how the pulse wave travels along an arterial path in the body. This is often known as pulse wave velocity, or PWV. This topic is discussed in further detail in the following chapters.

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Chapter 5: Pulse Wave Analysis (PWA)

5.1 Introduction

PWA is generally performed on pulse waveforms collected via PPG and other mobile pulse recordings. In this chapter, we will introduce some common PWA features, discuss models that attempt to provide a holistic representation of the cardiovascular system, and mention some conditions that affect these features.

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5.2 Basic Pulse Wave Analysis

5.2.1 Physical Origins of the PPG Waveform

The typical PPG waveform has two phases: the rising edge that represents the systolic contraction, and the falling edge that represents the diastolic relaxation of the heart.

Figure 5.1: A PPG waveform and its components

As mentioned previously, the pulse waveform of a young healthy individual exhibits an undulation in the pulse waveform, which is incorrectly and informally known as the “dicrotic notch”. In addition to being an sign of vascular health, the dicrotic notch also serves as a marker for the end of the systolic period of the cardiac cycle, enabling convenient analysis of the systolic and diastolic phases.

The PPG waveform is actually a superposition of the outgoing systolic wave along the central artery, and the reflection of the pulse wave from the arterial tree at the base of the central artery. In a properly functioning young healthy vascular system, the reflected wave will travel slowly and reach the heart as the heart is relaxing in its systolic phase.

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5.2.2 Basic parameters computed from the PPG Waveform

In table 5.1 below we list some common features used to characterize the PPG waveform.

Feature Description

Systolic Amplitude The systolic amplitude has been found to be correlated with the stroke volume of the heart, as well as the local

distensibility of the blood vessel at the measurement site [Castaneda 2018]. A low systolic peak amplitude may indicate decreased blood volume pulsations, or increased peripheral vasoconstriction, while a high systolic peak amplitude indicates increased pulsation or increased vasodilation.

Augmentation Index Augmentation index, the measure of how much a wave reflection affects the systolic pressure, is found by dividing the height of the diastolic peak by the systolic peak, which are both shown in figure 5.1.

Peak to Peak Interval Peak to peak interval, defined as the distance between two systolic peaks, has been correlated with the R-R interval in the ECG, and can be used to detect the heart rate.

Heart Rate Variability (HRV) Heart rate variability (HRV) is a measure of how much the timing between heart beats vary. Both values that are too low, and values that are too high, serve as a cause for concern. An individual’s HRV can be found either through time-domain analysis by observing the standard deviation between systolic peaks, or frequency domain analysis by observing the percentage of waves with high or low power levels [Acharya et al 2006].

Pulse Area The area measured under the PPG curve, pulse area can be divided into the systolic pulse area and the diastolic pulse area and used to find the inflection point area ratio, which correlates to total peripheral resistance.

Large Artery Stiffness Index The large artery stiffness index can be found by dividing subject height by the time delay between the systolic and diastolic peaks.

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5.2.3 First Derivative Features

Most of the first derivative features are used as supporting rather than stand-alone features. For example, the diastolic point is defined as the point where the first derivative of the collected waveform is zero, and is used as a feature that can help find the inflection point in PPG signals without a recognizable dicrotic notch. One feature of interest found from the first

derivative is the crest time calculation, easily determined by finding the time between two zero crossings in the first derivative waveform, and is correlated with arterial stiffness.

5.2.4 Second Derivative Features

Analysis of the second derivative is typically done by splitting the the wave into five different segments and finding the ratios between the heights of the individual waves.

Name Representation Figure

a-wave Early systolic positive wave

b-wave Early systolic negative wave

c-wave Late systolic re-increasing wave

d-wave Late systolic re-decreasing wave

e-wave Early diastolic positive wave

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5.3 Modeling the Pulse Waveform

In addition to analyzing specific morphological features in the pulse waveform,

researchers have also developed simplified lumped parameter physical models that simulate the blood flow in systemic arteries. The equations from these lumped element models represent the cardiovascular system as a hydraulic network with uniform parameters across all compartments of the network, and can be compared to an electric circuit [Kokalari et al 2013]. By fitting the pulse waveform data to the equations of the lumped parameter models, it is possible to solve for specific elements and variables in the model. The advantages of these models lie in their

simplicity, as they generate simple ordinary differential equations.

Designed in the 19th century by German physiologist Otto Frank, the Windkessel Model is the most common form of lumped model used to describe blood flow of the cardiovascular system. Its name comes from the old-fashioned windkessel pump that firemen used where water is pumped into a high-pressure air chamber then released in a steady jet once the chamber was full, analogous to the pumping action of the heart [Olufsen et al 2004].

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There are three different types of Windkessel models, with different levels of complexity. The 2-element Windkessel model can be represented as a circuit with a capacitor and a resistor, where the capacitor represented the compliance of the larger arteries, and the resistor the resistance of the smaller arteries. The 3-element Windkessel model contains an additional resistor, which represents the impedance of the aorta and larger vessels. Finally, the 4-element Windkessel model contains an additional inductor, which represents the inertia of blood

[Cathanho et al 2012]. There has also been research performed on 5- and 6- element Windkessel models, in addition to multi-compartment models [Kokalari et al 2013].

Below in figure 5.4, we illustrate the different Windkessel models. In the 2-element Windkessel model, P(t) stands for the pressure with respect to time, C the ratio of pressure to volume, R the resistance that relates outflow to fluid pressure. The new resistor in the 3-element Windkessel is represented as Z_c and accounts for the resistance of the aorta, while the new inductor in the 4-element model accounts for the inertia of blood flow. In table 5.3, we have also included the differential equations related to each of the models.

a) 2-element Windkessel b) 3-element Windkessel c) 4-element Windkessel

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Model Equation 2-element Windkessel

3-element Windkessel

4-element Windkessel

Table 5.3: The equations corresponding to the different Windkessel models [Kerner 2016] One issue of note is that the models use parameters that are difficult to estimate from measurements of arterial blood flow and pressure, especially the more complex Windkessel models. While these models provide a useful tool for estimating the cardiovascular system and predicting features for individual patients, the need to tune parameters for each individual poses an issue. For general screening of patients, it appears more conducive to simply extract relevant PWA features instead of attempting to model the entire cardiovascular system.

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Chapter 6: Application of PWA to Health Diagnostics

6.1 PWA as an Indicator of Blood Pressure

Last chapter, we focused on the calculation of PWA features; this chapter, we will examine the implications of these features as they relate to the health of the cardiovascular system. As mentioned in Chapter 4, the PPG waveform is critically dependent on the pulse pressure in the blood vessels as well as the externally applied pressure on the perfused skin. Since blood pressure has become recognized as an important predictor for cardiovascular disease, there has been significant interest in relating PWA features with blood pressure.

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While it is difficult to use PWA features to predict absolute blood pressure, PWA

features have proven useful in assessing relative changes in blood pressure as the pressure waves produce changes in volume that the PPG can pick up. Research has shown that it is possible to estimate blood pressure changes using PWA features [Sola et al 2016]. Furthermore, systolic amplitude has been shown to be related to continuous blood pressure during sleep [Chua et al 2010].

6.2 Central vs Aortic Blood Pressure

Additionally, PWA features have proven themselves to be helpful in supplementing the common cuff sphygmomanometer. The Conduit Artery Function Evaluation study and the Strong Heart Study have both demonstrated the benefits of using central aortic blood pressure over just using the brachial pressure as an assessment of cardiovascular health [Gurovich 2011]. There are two reasons for favoring central aortic blood pressure - first, the central aortic blood pressure affects organs that the brachial pressure would not, and second, the brachial pressure may differ from the central pressure by up to 50% in younger individuals due to the reflected wave pressures. By using the augmentation index (AIx), which represents the pressure of the reflected waves, with the brachial blood pressure, it is possible to estimate the central aortic blood pressure [Peng et al 2016]. Below, in table 6.3, we list a few PWA features related to blood pressure.

PWA Features Related to Blood Pressure Systolic Amplitude

Augmentation Index (AIx) Second Derivative - Ratio b/a

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Table 6.1: PWA features of interest for blood pressure

6.3 Deriving Blood Pressure using the 2-Element Windkessel Model

As mentioned in Chapter 5, it is possible to fit a physical model to the pulse waveform data in order to estimate specific parameters, such as the blood pressure. Below, we have one of the simplest physical models of the cardiovascular system, the 2-element Windkessel.

Figure 6.1: The 2 element windkessel model

Cardiovascular System Windkessel Total peripheral resistance resistance Arterial compliance capacitance

Blood flow from ventricle I(t) sinusoidal current

Arterial Pressure Wave P(t) time varying electrical potential Table 6.2: Analogous elements

We can express the systolic and diastolic pressure as the following equations, where P_ts and P_td are the initial values of P_s and P_d [Choudhury et al 2014].

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(Equation 6.2)

6.4 PWA for Detecting Atherosclerosis and Arterial Stiffness

Arterial stiffness is a major indicator of vascular health, directly correlated with hypertension, atherosclerosis, and other cardiovascular pathologies. Qualitatively, it has been known for decades that the shape of the PPG pulse wave changes with age and increasing atherosclerosis. This can be explained intuitively by noting that increased pulse wave velocity of the reflected pulse wave in the diastolic phase. Due to the higher velocity of this reflected wave, the reflected wave returns to the heart prematurely, resulting in decreased separation between the systolic and diastolic phases of the pulse. This is shown in Figure 6.X. Mathematically, it follows that many PWA features will be affected by the level of arterial stiffness in an individual across the etiology of atherosclerosis.

Figure 6.2: Classification of PPG pulse waveforms first proposed by Dawber et al. With increasing age, arterial stiffness, and cardiovascular pathologies, the human pulse waveform evolves from Class 1 towards Class 4

The deterioration of the dicrotic notch in the different classes of pulse waves can be automatically detected by analyzing the first and second derivatives of the PPG pulse waveform.

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In table 6.3 below, we note the major PWA features capable of determining the amount of arterial stiffness present.

PWA Features for Arterial Stiffness Augmentation Index (AIx) Large Artery Stiffness Index Second Derivative - Ratio b/a Second Derivative - Ratio c/a Second Derivative - Ratio d/a Second Derivative - Ratio e/a

Table 6.3: PWA Features Relevant for Arterial Stiffness

6.5 Other PWA Features and Their Clinical Significance

In addition to the features listed previously several other PWA features have been identified and shown to correlate with clinical symptoms [Elgendi 2012]. These features are listed in Table 6.4 below.

Features Clinical Relevance

Augmentation Index Reduced compliance of elastic arteries causes a higher augmentation index.

Pulse Interval Knowing the pulse interval will help with understanding an individual’s heart rate variability

Large Artery Stiffness Index The time interval between the systolic and diastolic peaks, the large artery stiffness index is affected by the size of the reflected wave and increases with arterial stiffness and age. Table 6.4: Clinical relevance of extracted PWA features

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In addition to the clinical features extracted from the PPG signal, there are also features that can be extracted from the second derivative PPG. Below in table 6.5, we detail the different features derived from the five sub-segments of the second derivative signal, which were

previously introduced in Chapter 5.

Features Clinical Relevance

Ratio _b/a This ratio increases with age, and is positively correlated with the Framingham risk score, suggesting that this ratio might be useful for distinguishing between individuals for high CVD risk. Additional research as also provided evidence that ratio

b/a_{is related to the distensibility of the peripheral artery.}

Ratio _{c/a, e/a} Research has found these ratios are correlated with decreased arterial stiffness and decrease with age.

Ratio _d/a Useful for evaluating left ventricular afterload. Ratio _{(b-c-d-e)/a, ratio}

(b-e)/a

These ratios help evaluate an individual’s vascular age. Ratio

(b-e)/a_{is used when c and d are missing.}

Ratio _(c+d-b)/a Another aging index, this ratio increases with age and is a bit more comprehensive.

a-a_interval This interval represents a complete cardiac cycle, and it is possible to use this interval to also find heart rate variability. Table 6.5: Main features of the second derivative PPG wave [Elgendi 2012, Castaneda et al 2018]

The shape of the second derivative waveform has also been classified into seven different categories that indicate the condition of the subject’s circulation.

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Figure 6.3: Seven different categories of waveforms that stratify quality of circulation [Elgendi 2012]

6.6 Conclusion

With a single-site PPG recording, we can use PWA to determine central pressure wave characteristics that offer general insights into a subject’s cardiovascular health. Important characteristics of the waveform can be captured using specific points in the pulse waveform as well as the first and second derivative of the waveform.

To achieve an in-depth understanding of an individual’s arterial health, we also use multi-site PPG recordings from which we can calculate pulse wave velocity (PWV), which is the gold standard for determining arterial stiffness. This topic is discussed in the next chapter.

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Chapter 7: Tools for PWA

The pulse has always been a symbol of health and vitality, and so the measurement and analysis of the pulse wave has always been a subject of study. In the late 1800’s, various mechanical devices were developed to understand the pulse, using complicated systems of sensors and levers in an attempt to trace out the shape of the pulse. In figure 7.1 below, a sphygmometer was developed to help with clinical diagnosis and description, heralding the beginnings of pulse wave analysis.

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Figure 7.1 Depiction of a 1863 sphygmograph, along with the recordings it took [Avolio 2010]

In this chapter, we will discuss modern methods of measuring the pulse waveform, such as pulse tonometry, or finger photoplethysmography.

7.1 Traditional Tools

7.1.1 Arterial Tonometry

Pulse wave analysis was originally performed on the arterial pressure waveform, which was measured using a tonometry probe. Using the same principles as the ophthalmologic application of applanation tonometry, the arterial waveform can be measured when an external transducer flattens the curved surface of the arteries just enough to reduce wall tension to zero [Avolio et al 2009]. As this requires a rigid support, the majority of measurements obtained using applanation tonometry have been collected from the wrist, where the radial pulse is easily reached. Typical tonometry sensors of this type are pencil-shaped, with a single sensor at the

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end. It is also possible to measure blood pressure using the arterial tonometer if the probe was inserted into the artery.

Figure 7.2: SphygmoCor applanation tonometry sensor [Nelson et al 2010] 7.1.2 Finger PPG

With the widespread availability of low cost components and microprocessors, the finger PPG has become another common method for pulse wave recording. Research has found close correlations between the contours and characteristics of the finger PPG waveform and the arterial tonometry pressure wave [Millasseau et al 2015]. However, the waveforms are not the same - while the tonometry waveform looks at the pressure waves, the PPG is measuring the changes in volume [Shaltis 2007]. Furthermore, additional studies performed on the features extracted from both PPG and tonometry waveforms have found that features derived from the PPG waveform appeared to be a better discriminator between medium and high CVD risk when compared to features found from the arterial tonometer [Clarenbach et al 2011].

Not only is the finger PPG less expensive, it is also much simpler to use compared to the arterial tonometer, which requires correct placement and manually applied steady pressure. The

Developing a low-cost cardiovascular mobile screening kit

Developing a Low-Cost Cardiovascular Mobile

Screening Kit

Botong Ma

Developing a Low-Cost Cardiovascular Mobile

Screening Kit

Botong Ma

Abstract

Acknowledgements

Table of Contents

List of Figures

Chapter 1: The Global Burden of Cardiovascular Disease (CVD)

1.1 The Cardiovascular System

1.2 Pathophysiologies of the Cardiovascular System

1.3 Defining the Need for CVD Screening

1.4 Scope and Outline of this Thesis

Chapter 2 : The Need for Specialized Screening Tools

2.1 Existing CVD Screening Tools and Risk Scores

2.2 Shortcomings of Existing CVD Screening Tools and Risk Scores

2.3 Current Mobile Technology Solutions

2.4 Shortcomings of Current Mobile Technology Solutions

Chapter 3: A Proposed Solution

3.1 Prior Work from the Mobile Technologies Lab

3.2 Contributions of This Thesis

Chapter 4: Photoplethysmography (PPG)

4.1 Overview

4.2 Fundamentals of Photoplethysmography (PPG)

4.3 Factors that Affect PPG Measurements

4.4 The Relationship Between the PPG Waveform and Blood Pressure

4.5 Traditional and Modern PPG Equipment

4.6 Properties of the PPG Waveform

Chapter 5: Pulse Wave Analysis (PWA)

5.1 Introduction

5.2 Basic Pulse Wave Analysis

5.3 Modeling the Pulse Waveform

Chapter 6: Application of PWA to Health Diagnostics

6.1 PWA as an Indicator of Blood Pressure

6.2 Central vs Aortic Blood Pressure

6.3 Deriving Blood Pressure using the 2-Element Windkessel Model

6.4 PWA for Detecting Atherosclerosis and Arterial Stiffness

6.5 Other PWA Features and Their Clinical Significance

6.6 Conclusion

Chapter 7: Tools for PWA

7.1 Traditional Tools