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1 BLOOD PRESSURE ESTIMATION USING PHOTOPLETHYSMOGRAPHY WITH TELEMEDICINE APPLICATION (Centered, double spaced if more than one line, all capitals) (2 inches from title to A Thesis ) A Dissertation Presented to The Faculty of the College of Graduate Studies Lamar University (3 inches from A Thesis to In Partial Fulfillment ) In Partial Fulfillment of the Requirements for the Degree Doctor of Engineering by Logan Porter August 2014

2 BLOOD PRESSURE ESTIMATION USING PHOTOPLETHYSMOGRAPHY WITH TELEMEDICINE APPLICATION LOGAN PORTER Approved: Gleb V. Tcheslavski Supervising Professor Selahattin Sayil Committee Member G.N. Reddy Committee Member Harley R. Myler Chair, Department of Electrical Engineering Victor A. Zaloom Interim Dean, College of Engineering William E. Harn Dean, College of Graduate Studies

3 2014 by Logan Porter No part of this work can be reproduced without permission except as indicated by the Fair Use clause of the copyright law. Passages, images, or ideas taken from this work must be properly credited in any written or published materials.

4 ABSTRACT BLOOD PRESSURE ESTIMATION USING PHOTOPLETHYSMOGRAPHY WITH TELEMEDICINE APPLICATION by Logan Porter The dissertation presents a development of a system that lets a user estimate blood pressure of a human subject without a pressure cuff through the use of the optical sensing technique known as photoplethysmography. This technique allows a pulse waveform to be recorded non-invasively using available non-expensive hardware, and utilizes software packages to identify key features of the waveform to estimate both systolic and diastolic blood pressure. Additionally, the software is capable of saving data to a database, so that information can be retrieved through the internet via web browser or Android smart phone. This allows for a telemedicine feature of the system.

5 ACKNOWLEDGEMENTS I would first like to thank my advisor, Dr. Gleb Tcheslavski for allowing me to work on this project and giving me the feedback necessary to make this work what it is. It is because of him that my methods and results are done with better attention to detail than I could do by myself. Additionally, I want to thank my committee members for their interest in my research and agreeing to help guide it. I am grateful to those who agreed to help validate the results of my research by volunteering their time and effort to help me collect the data necessary. I hope that the results of my work can help contribute to the growing field of biomedical devices and telemedicine thanks to everyone s effort. Finally, I dedicate this dissertation and the end of my academic journey to the best engineer I know, Gary L. Porter. iii

6 Table of Contents... List of Tables... vi List of Figures... vii Chapter Page 1. Introduction Scope of Work Contents of the Dissertation Literature Review Cardiac Cycle and Photoplethysmography Background Cardiac Cycle Cardiac Parameters Mathematical Representations Photoplethysmography Modeling of Cardiovascular System MATLAB Windkessel Model Implementation of Hardware and Software Hardware Design Power Circuit PPG Circuit Microcontroller Printed Circuit Board Software Design LabVIEW iv

7 5.2.2 Database and Web Page Android Phone Application Data and Results Conclusion and Future Work References v

8 List of Tables Table 2.1 Comparison Summary Table 6.1 Results Table Table 6.2 Results Table vi

9 List of Figures Figure 1.1 Conceptual Diagram...5 Figure 2.1 ContiPress Usage...9 Figure 3.1 Cardiac Cycle Figure 3.2 PPG Waveform. 27 Figure 3.3 First Derivative Waveform Figure 3.4 First Derivative with Alternative PPG...29 Figure 3.5 Second Derivative PPG Waveform Figure 3.6 Altered Second Derivative Waveform Figure 3.7 Third Derivative Waveform Figure 3.8 Comparison of First and Third Derivative Figure 3.9 Altered Third Derivative Waveform.. 33 Figure 4.1 Two Element Windkessel Model Figure 4.2 Three Element Windkessel Model Figure 4.3 Four Element Windkessel Model...38 Figure 4.4 Sine Wave Blood Flow.39 Figure 4.5 MATLAB Modeled Blood Flow Figure 4.6 MATLAB Modeled Blood Pressure...43 Figure 4.7 MATLAB Collected Data 44 Figure 4.8 Large R Value...45 Figure 4.9 Small R Value...46 Figure 5.1 Reflective PPG..49 Figure 5.2 PPG Sensor vii

10 Figure 5.3 Phototransistor Output..51 Figure 5.4 Filtering and Amplifying Circuit Figure 5.5 Filtered and Amplified PPG Figure 5.6 DC Offset Circuit..55 Figure 5.7 PCB Block Diagram.57 Figure 5.8 Constructed PCB...58 Figure 5.9 Microcontroller Flowchart Figure 5.10 LabVIEW Flowchart..61 Figure 5.11 Real Time PPG Waveform Figure 5.12 Captured PPG Waveform Figure 5.13 Third Derivative PPG Waveform Figure 5.14 Peak and Valley Locations on Front Panel.64 Figure 5.15 Systole and Diastole Input Figure 5.16 Normalized PPG.65 Figure 5.17 LabVIEW Control and Output of HR and ET 66 Figure 5.18 LabVIEW Control and Output of Blood Pressure Figure 5.19 SQL Table...68 Figure 5.20 Blood Pressure Web Page Graph Figure 5.21 PPG Web Page Graph 71 Figure 5.22 Emulator Android App.. 72 Figure 5.23 Phone Running Android App Figure 6.1 Bland-Altman Systolic Pressure Figure 6.2 Bland-Altman Diastolic Pressure...78 viii

11 Porter 1 Chapter 1 Introduction Cardiovascular health is a major topic in current medical headlines. Heart disease is a chief contributing factor to fatalities around the world, and is a leading cause of death in the United States next to cancer (Center for Disease Control and Prevention 2013). One of the earliest signs of heart disease is related to blood pressure readings. A high blood pressure measurement can indicate potential cardiovascular problems. Early and frequent measurements of blood pressure for at risk people can help alleviate and prevent future complications. With blood pressure measurement being such an important factor for cardiovascular health, there is a lack of new products for monitoring becoming available based on changing technology. Examples of this include new methods of measurements, telemedicine, automated measurements, and integration with sensor networks. Traditional blood pressure measurement involves using a sphygmomanometer. This is a cuff based measuring device wrapped around a person s arm or wrist (La Bella 2011, 9-11). The cuff is inflated until circulation ceases. Pressure is released and recorded at the first sound of blood flowing and again when the sound of blood flow has faded. The recordings can be detected by a physician with a stethoscope, or via mechanisms in a digital blood pressure device. The values obtained are systolic and diastolic numbers. These values correspond to the pressure of blood flow against the artery walls when the heart contracts and the pressure when the heart is at rest, respectively. Measurements are given in mmhg (millimeters mercury).

12 Porter 2 While traditional measurement devices are easily available to purchase, they largely present information only available to the immediate user. The results cannot easily be shared via electronic communication methods because no such interface exists on most devices. A select few devices exist with simple USB interfaces for manual uploading of information, such as Lifespan Fitness BPM However, such features often increase the price point of the device while offering limited sharing methods. In recent research to examine new methods of evaluating blood pressure, a noninvasive signal known as photoplethysmography (PPG) has been used as one of the key factors. PPG is a simple optical technique for non-invasively measuring blood flow or volume in the body with each heartbeat (Elgendi 2012). The waveform generated is simple in shape, but it reveals many characteristics of the cardiac cycle that make it useful in analysis. When combined with an electrocardiogram (ECG), more characteristics of the cardiac system can be determined. The ECG and PPG pair is often used in cuffless blood pressure estimation methods. Additionally, two PPG sensors can be used to replace the ECG. The growing availability of access to the internet through phones, tablets, and microcontrollers has led to a new movement in interfacing sensors to the internet called the Internet of Things (IoT). IoT is the term for interfacing things to the internet. A thing can be any natural or man-made object with a sensor that can send data over a network (Rouse 2014). In healthcare, IoT can be referred to telemedicine. Telemedicine is specifically the exchange of medical data over a network from one site to another (American Telemedicine Association 2013). Telemedicine provides a remote clinical

13 Porter 3 service. This is not to be confused with health information technology (HIT) which is the sending of medical files through a network. The reason for developing a PPG system to estimate blood pressure is largely due to the PPG signal itself representing a full cardiac cycle and presenting information that can be used to deduce other parameters from a single waveform. The PPG signal is also similar in shape to a pressure waveform obtained from a finger cuff with both showing systole, diastole, and reflections from heart valve closure. Additionally, the removal of a cuff eliminates any discomfort, the sensor is small, and only applied to the tip of the finger. Another reason for the development of the PPG system is the possibility of networked capability to allow data sharing with advances in hardware and software technology. The work incorporates telemedicine by saving data from the software to a database over a network connection. The information saved to the database allows for retrieval from a web page or Android phone application. Information saved includes the PPG waveform and blood pressure. The telemedicine component allows for viewing of the data away from the immediate user and allows it to be accessible to multiple people, e.g. doctors, physicians, or family members. 1.1 Scope of Work The scope of work includes the design of hardware and software components necessary to capture a pulse waveform signal from the system user, identify key components of the waveform, estimate blood pressure based on the data given, and make this information available for remote viewing by a web page and Android phone application. The scope of work can be broken down into functional requirements for both

14 Porter 4 hardware and software to outline how the system works and indicate what limits it operates in. Functional requirements of the hardware include the following: Use optical sensor to capture blood volume changes at sensor location. Amplify and filter PPG signal to make coherent. Transmit PPG signal to PC through microcontroller for software processing. The functional requirements of the software include the following: Graph PPG data in real time. Capture short interval to allow identification of key characteristics of PPG waveform. Use data to estimate systolic and diastolic blood pressure. Save information to database. Provide telemedicine capabilities with web page and Android phone application. The system is intended to be used to analyze a PPG waveform to estimate blood pressure. Due to the different shapes and variations of PPG waveforms, it is not a highly autonomous system. Key features must be found manually, but calculations are handled by the software. Therefore, it is not designed to be an in-home system to be used by someone who is unfamiliar with cardiovascular system or PPG theory. An ideal setting for the system would be in a clinical setting, so that a patient can have a sensor connected and a physician can identify the necessary components of the PPG waveform. The results of the calculations could be shared immediately with the patient or others through telemedicine means. A conceptual diagram of the system is shown in figure 1.1.

15 Porter 5 Figure 1.1 Conceptual Diagram The conceptual diagram illustrates the setup and operation of the system. A sensor is connected to the finger of the patient. The sensor is connected to the hardware necessary to amplify and condition the incoming waveform to make it comprehensible on a PC. Software on the PC is then used to estimate blood pressure, and the information is saved to a database which can be viewed by others through the internet. 1.2 Contents of the Dissertation The rest of the dissertation is organized into the following chapters: Chapter 2 Literature Review. Both commercial and academic literature related to blood pressure detection are discussed in this chapter to compare to the work done. This review helps to identify what current research has done and demonstrate the uniqueness of the dissertation. Chapter 3 Cardiac Cycle and Photoplethysmography Background. This chapter explains the basics of the cardiac cycle as well as key terminology that are relevant to understanding the methods of implementation. Photoplethysmography

16 Porter 6 is also detailed in this chapter to present an understanding of using optical sensors to detect blood flow. Chapter 4 Modeling of Cardiovascular System. Discussed in this chapter is a model simulation of the cardiovascular system using a Windkessel model with MATLAB software. The model helps to depict how blood flow and pressure change when different variables of the cardiac cycle are changed. Chapter 5 Implementation of Hardware and Software. The hardware and software details of implementation are outlined in this chapter. Chapter 6 Data and Results. This chapter examines the results of blood pressure estimation based on the variables obtained. Presented are the equations and reasoning for using equations to obtain blood pressure estimates. Chapter 7 Conclusion and Future Work. This chapter summarizes the dissertation s findings along with suggestions for future efforts.

17 Porter 7 Chapter 2 Literature Review This chapter reviews previous works that have been done commercially and academically related to blood pressure devices, monitoring, or calculations. Both commercial and academic research are compared to show how the implemented methods are different from related previous materials. Blood pressure measuring techniques date back to 1773 when British scientist Stephen Hales inserted a tube in a horse s artery to observe the blood flowing up the tube (Britannica 2013). Other invasive measurement methods were tested throughout history on animals, but the introduction of the sphygmomanometer allowed for the first noninvasive measurements on humans (American Diagnostic Corporation 2013). Undergoing various redesigns through the mid-1800s to early 1900s, the device and measuring technique finally achieved accuracy and reliability largely due to the contribution of Russian scientist Nikoli Korotkoff with the development of listening to the sounds of blood flow via stethoscope. His method led to the current use of systolic and diastolic measurements. A cuff was inflated until the sound of blood flow ceased. The pressure recorded was known as systolic pressure. The cuff was then released slowly and the diastolic value was the pressure at which the sound of blood flow resumes. This points at which blood flow ceased and started again also became known as Korotkoff Sounds. Measurement devices were modified as technology advanced, with the development of air cuffs, digital readers, and automated readers developed in the 1980s (Omron Inc. 2013a). The term automated refers to the use of pushing a button to inflate the cuff, instead of using a traditional hand pump.

18 Porter 8 Commercial blood pressure devices have existed since the late 1970s from corporations still selling products today, such as the healthcare division of Omron. Omron is the largest seller of blood pressure devices globally controlling 51% of market share worldwide (Omron Inc. 2013b). Omron blood pressure devices have become popular based on the company s philosophy of home blood pressure devices that are friendly, quiet, and fast. The word friendly refers to the device cuff being inflated to an optimal pressure that gives a reliable reading and isn t uncomfortable for the user. Quiet references the volume of noise generated by the blood pressure device as the cuff is inflating. Fast refers to the amount of time it takes to inflate and deflate the cuff to get a reading while providing less irritation to the user. The philosophy of friendly, quiet, and fast was largely achieved due to the development of a fuzzy logic algorithm to handle the various parameters involved in blood pressure measurement. Examining Omron s developments, they have appeared to optimize a cuff based product in providing optimal time, comfort, and measurement. However, none of their products are cuff-less and additional cardiovascular information is limited to heart rate. A large separating factor between popular blood pressure monitors by Omron and the implemented PPG method is the removal of a cuff and ability to present more cardiovascular information beyond heart rate. Omron s products do have an advantage this method in that they are very user friendly for in-home use. Their products require only setting up the pressure cuff and pushing a button to obtain blood pressure readings. The PPG setup requires a user to operate the software that has knowledge in cardiovascular theory. For example, a licensed physician would be an intended operator. Overall, the removal of a cuff helps to make the accomplished work stand out against

19 Porter 9 Omron products, but adds complexity so that the intended use is in a professional setting and not in a home environment. Denmark startup company Sense has been developing a blood pressure monitoring solution to traditional inflatable cuffs since 2007 when a potential market was identified for blood pressure monitoring (Sense A/S. 2013b). According to Sense research, the number of cardiovascular diseases, primarily hypertension, will increase upwards to 60% of the world population with an even larger percentage in developed countries by the year Therefore, the need for blood pressure monitoring, and the market associated with it, will increase. The system being developed is known as ContiPress. The system contains a sensor cuff that allows for continuous 24 hour monitoring of a user. The cuff contains a sensor patch composed of electrodes that uses a processing unit (PU) to analyze data and save it for future use. The sensor cuff is not reusable and is discarded after use. The PU is removable so that it can be interfaced with a data reader that is connected to a PC. The PC runs proprietary software developed by Sense to display data from the PU. Figure 2.1 shows a conceptual diagram of how ContiPress is used, as shown on Sense website. Figure 2.1 ContiPress Usage (Sense A/S. 2013a)

20 Porter 10 In 2012, Sense performed successful preliminary trials of the system in Denmark. Further trials were conducted in the first half of 2013, with potential commercial launch pending approval from FDA for sales in the United States. At the time of this writing, no commercial launch has been officially announced. While the ContiPress is a novel development compared to traditional blood pressure devices, the present work has several differences. The largest difference is in the primary measuring technique. ContiPress uses a non-inflatable cuff with electrodes worn around the upper arm area. The dissertation work uses one PPG sensor, removing any need of a cuff. ContiPress is developed for continuous monitoring during a 24 hour period saving to a processing unit and later disposing of the sensor cuff. No remote monitoring capabilities are mentioned. The PPG system s function is interval measurements, with nothing to dispose of, allows for direct interfacing to a PC, and uses the internet for telemedicine purposes. Table 2.1 gives a visual comparison summary of Omron products, ContiPress, and the proposed PPG system.

21 Porter 11 Table 2.1 Comparison Summary Attribute Omron Products ContiPress Proposed PPG System Vitals Measured blood pressure, pulse blood pressure blood pressure, pulse Measurement inflatable cuff sensor (noninflatable) optical sensor Method cuff Measurement Period interval (determined by user) continuous 24 hr. period Interval (determined by user) Automated Measurement no, due to need to push button to yes, system is fully no, requires user to identify waveform characteristics inflate cuff automated Discomfort varies, depending on cuff pressure, illnesses, age, etc. minimal, no cuff pressure minimal, no cuff, sensor use requires small amount of contact area Primary Display LCD on device PC graphs PC graphs Remote Viewing Capabilities None mentioned None mentioned Applications clinical or home use clinical use clinical use database and Android app. allow viewing by others In academia, many articles have been published over the last decade describing different implementations of a blood pressure system that does not use a cuff as the primary measurement method. Much of the prior research used a parameter called pulse transit time (PTT), also synonymous with pulse arrival time (PAT) (McCarthy, O Flynn, and Mathewson 2011). PTT, as defined by prior research, is the amount of time it takes for blood to flow from two points in the body during a heartbeat. The starting point for PTT is identified as the R peak on a QRS complex because this is when the heart starts to contract to send blood through the body. The ending point is either a peak or valley waveform from a PPG sensor attached to an extremity of the body (Chen et al. 2008).

22 Porter 12 Some research has attempted to alter the locations of the PTT starting or ending point to observe differences in estimation of blood pressure (Chen et al. 2008; Teja 2012; Kalsi 2009). The relationship between PTT and blood pressure was evaluated by (Chen et al. 2008) to observe how PPG waveforms and the timing difference between them differ for people with varying blood pressure. The research did not attempt to estimate blood pressure, only to observe differences in PTT times and waveform shapes. The results of the experiment showed that PTT varied between higher and lower blood pressures. The PPG waveforms, taken on the ear and toe also varied in shape and size. The PPG waveforms differed because of the amount of kinetic energy that was required by the heart to overcome hypertension problems in high blood pressure subjects. This research concluded that the PPG waveform and PTT changes according to blood pressure, and can be used as an indicator of high or low blood pressure. (Ma and Zhang 2005) described a correlation study between PTT and blood pressure. This study investigated the relationship between various PTTs defined by different locations of PPG sensors and an ECG. A correlation coefficient was used to quantify the relationship between variations among the parameters observed and blood pressure. The results of the study concluded that PTT and blood pressure were closely related because of the high correlation coefficients calculated in the results. Therefore, PTT can serve as a potential blood pressure estimating parameter. Academic research has used PTT as a means of estimating blood pressure (Fung et al. 2004; Deb et al. 2007; Gu, Poon, and Zhang 2008; McCarthy, O Flynn, and Mathewson 2011). While various setups are used, the principle of using timing

23 Porter 13 differences on two waveforms is common throughout. Most research employed an ECG with PPG. A few attempted measurements without an ECG (Chen and Wen 2010; Teja 2012; McCombie et al. 2007; Kalsi 2009; Jonnada 2012). Numerous research only calculated systolic blood pressure as it is easier to deduce because of its more direct relationship with PPG and ECG waveforms. Diastolic blood pressure was calculated in a few studies (Chen and Wen 2010; Kalsi 2009; Jonnada 2012). Studies employing an ECG and PPG often used the PPG on a finger, while one used an ear PPG (Chen and Wen 2010). There is no conclusion on which location is inherently better than another. Both locations simply served as reference points in calculations. (Fung et al. 2004) measured PTT from peak to peak on the ECG and PPG waveforms. Using the timing differences, they defined BP equations using kinetic equations taking into account pulse velocity, gravity, distance from heart to fingertip, and height difference between ECG and PPG sensors. Regression analysis was used to optimize their blood pressure equations. Results of their work showed that their equations produced a mean difference of mmhg with a standard deviation of mmhg. Limits of their system included noise, limited body movement, and synchronization between PPG and ECG. (McCarthy, O Flynn, and Mathewson 2011) studied the accuracy of using the PTT technique for estimating blood pressure. Their methods included a PPG finger sensor with an ECG. Their equations were developed to take into account a calibrating measurement. This measurement was taken every five minutes using a standard cuff blood pressure device. The measurement was used as a variable in their developed blood pressure estimation equations that were calculated using MATLAB software. The results

24 Porter 14 after taking measurements on six patients had an average error of mmhg with a standard deviation of mmhg. Noted was that the accuracy of the acquired PPG signal heavily affects the results of calculated blood pressure. Additionally, their work depended on a recalibration every five minutes and only systolic blood pressure was calculated. Despite these issues, they concluded that PTT could be used to estimate blood pressure with reasonable accuracy. Deb et al. (2007) used the PTT with ECG method, but used a second PPG on a brachial artery along the elbow. A second PPG sensor was used to estimate PTT between the two PPG sensors so that pre-ejection period (PEP) could be estimated. PEP is a delay between the onset of electrical cardiac activity and start of mechanical ventricular ejection. Therefore, PEP affects blood pressure. By estimating it, PEP can be factored into a final blood pressure equation and not be assumed constant for different populations. The PEP equation is dependent on the PTT to the finger, timing between two PPG sensors, subject height, and distance between the two PPG sensors. Results showed an estimation of systolic blood pressure using two different PTT. One is from ECG to finger and the other is PPG to PPG sensor. Five trials for each setup resulted in a mean blood pressure difference of -1.5 (ECG to finger) and 4 (PPG to PPG) mmhg with a standard deviation of 10.5 (ECG to finger) and 19.3 (PPG to PPG) mmhg. Gu, Poon, and Zhang (2008) used a secondary characteristic of a PPG waveform with an ECG and PTT to estimate systolic blood pressure. The secondary characteristic is another amplitude peak, after the dicrotic notch, a valley in the PPG waveform after the highest amplitude of the PPG. This peak is smaller than the primary amplitude and occurs later in the PPG waveform. The study used both peaks to construct a new parameter for

25 Porter 15 blood pressure estimation called relative amplitude of secondary peak (RAS) which is the ratio of the main peak and the secondary peak. The use of RAS is due to the different shape of the PPG signal and location of the dicrotic notch during different cardiovascular states (exercise, heavy breathing, and rest.). Results of the study show that about 66% of measurements on subjects at various cardiovascular states were more accurate with RAS than without, especially after exercise. The blood pressure measurements obtained were only for systolic readings. While a large portion of academic research has been using a PPG and ECG setup, several studies were conducted without an ECG. These studies used two PPG sensors along with timing information, and developed equations to estimate blood pressure. Teja (2012) developed a system to estimate PTT using two PPG sensors. The purpose of the study was to show that PTT could be derived using only PPG sensors instead of PPG with ECG. Although the idea of using PTT to deduce blood pressure was given, the system was not developed for that purpose. The two PPG sensors were placed on the wrist and pinky finger. Analog filters were constructed using operational amplifiers (op. amps.) to filter out high frequency noise and cancel any DC offset that might be present due to artifacts caused by movement. Signal processing was done using MATLAB to detect and measure peaks of the PPG waveforms to estimate PTT. The results indicated that PTT can be calculated using two PPG sensors and that an ECG can be removed. McCombie et al. (2006) developed a method to estimate pulse wave velocity (PWV) as it relates to blood pressure. PWV is defined as the speed a pulse (blood flow) travels from one point of the artery to another. In-line PPG sensors were placed at a fixed

26 Porter 16 distance to measure PWV using the fact that PWV will be equal to the difference of the distance traveled over the difference of the time to travel. Using PWV, blood pressure equations were derived. The defined equations for blood pressure were constructed using measured blood pressure to help define constants, PWV, and sensor height according to the position of the arm. However, results were not given if the equations were accurate as the purpose of the study was to relate changes in PWV to changes in blood pressure. The results did show that PWV and blood pressure were related. A future study by McCombie et al. (2007) used PWV to estimate blood pressure. Two PPG sensors were used as in their previous study and equations were developed to take into account height of a subject s arm as well as hand position being elevated or declined. This allowed for a calibration of the device that didn t require an external blood pressure measurement to define a height constant for derived blood pressure equations. The results presented compared an average blood pressure measurement, not a separate systolic and diastolic reading. Therefore, the results were compared with an average number of systolic and diastolic readings. Comparing measurements taken from PWV and a cuff device, the average difference was mmhg. Kalsi (2009) developed a monitoring device to monitor multiple health parameters of a human. This included heart rate variability, breathing rate, and blood pressure, with blood pressure being the main parameter to measure. The device was developed using PPG sensors without an ECG, so that it would not be obstructive to the user. The two sensors were located on the brachial artery running along the elbow to wrist and ending at the finger. Systolic blood pressure was calculated using PTT to determine the PWV. An equation was derived using regression analysis to estimate systolic blood pressure.

27 Porter 17 Diastolic blood pressure was also estimated. This was done by using the fact that the catacrotic phase of the PPG, or falling edge of the pulse waveform, relates to the diastole action of the heart. Signal processing was done using MATLAB to help digitally filter out noise and estimate blood pressure. In order to calculate blood pressure, actual readings from a cuff device had to be used to help calibrate measurements. Results for blood pressure estimation showed that estimates taken did not vary much from one another. However, comparing the estimated values to the cuff measurements, they were typically found to be lower in value. In addition to PTT, a second method was used to estimate blood pressure. This method involved using the height of the pulse waveform due to the different amplitudes of the waveform being affected by both systole and diastole activities. This method also required a calibrated measurement to work correctly. The results for this technique were closer to the cuff device measurements, while varying little in consecutive measurements. This technique appears to show promise in estimating blood pressure, but it cannot work without a calibration measurement. A novel approach to estimating blood pressure was done by (Jonnada 2012) using a smart phone and customized microphone connected to the phone. This allowed for the removal of PPG and ECG equipment. The technique involves using a phone s camera to estimate pulse levels based on red light detection from the finger and the microphone estimates the heart beats. The camera captures red light intensity from a finger placed on the camera, as more intense light is where a pulse is located. The heart beats recorded by the microphone function similar to an ECG. Therefore, the microphone and phone camera behave similar to an ECG with PPG. A PTT like approach was used to estimate

28 Porter 18 both systolic and diastolic blood pressure. Results were compared using a Bland-Altman plot and showed that estimated measurements were similar in value with differences averaging around 5 mmhg. While the approach does not use PPG sensors specifically, it shows that the theory behind PPG sensors and the cardiovascular system can be used to develop novel applications. Fukushima et al. (2013) developed a system to estimate blood pressure using a single PPG sensor located on a subject s finger. They wanted to eliminate the need for two PPG sensors, ECG, or any other secondary equipment. A key component used in their research was the second derivative of the PPG waveform, also known as the accelerated PPG. The second derivative was used to find inflection points from the original PPG. The inflection points are associated with various indexes derived from previous studies that correlate with cardiovascular health and appear as peaks in the accelerated PPG. The timing differences between the different peaks were used to estimate two parameters known as cardiac output and total peripheral resistance using stepwise multiple regression analysis. After obtaining estimates of these two values, blood pressure was estimated by taking the product of the two. The result is known as mean arterial blood pressure. Results of the study obtained measurements that were close to actual computed mean arterial blood pressure using a cuff. The study demonstrated that a single PPG can be used to estimate a blood pressure parameter. Reviewing current, easy to access products on the market, there is not any reliable cuff-less blood pressure measuring device. Additionally, current devices are not keeping up with technology to allow them to be used in telemedicine applications. While potential is shown for new devices through ContiPress, it has yet to be demonstrated on real

29 Porter 19 consumers. The present work is largely differentiated from consumer products by eliminating a cuff for measurement. Comparing the previous work in academia, it has already been demonstrated that PPG and ECG are closely related to blood pressure and can allow for estimations using PTT. While only a few studies have shown diastolic measuring capabilities, the theory of PPG waveform shape shows that it can be it can be possible to formulate diastolic numbers. Also revealed was that the ECG can be replaced with an additional PPG sensor to estimate blood pressure based on PTT and PWV theory. The implemented PPG system builds on existing research techniques, while incorporating novel methods to estimate both systolic and diastolic blood pressure. Additionally, it allows for telemedicine applications so that it can be distinguishable from previous academic work.

30 Porter 20 Chapter 3 Cardiac Cycle and Photoplethysmography Background 3.1 Cardiac Cycle The cardiac cycle includes events that occur before, during, and after the heart pumps blood throughout the body (Cardiovascular Physiology 2014). Understanding the cardiac cycle is important to knowing why the PPG waveform appears as it does, and identifying where events of the cardiac cycle occur by observing the waveform. PPG operation is described in detail to demonstrate how light interacts with blood when focused through an area of the skin as well the PPG waveform and its derivative waveforms. The cardiac cycle can be summarized as the heart filling with blood, pumping it through the body, and filling back again to start a new cycle. This cycle can be divided into two main phases known as systole and diastole. Systole action is when blood is ejected to the aorta and pulmonary artery. Diastole action is when the heart relaxes and the ventricles refill with blood. There are many valves in the heart associated with each action that open and close. For simplicity, ventricular and aortic actions are primarily described. A pictorial representation of the cardiac cycle is shown in figure 3.1.

31 Porter 21 Figure 3.1 Cardiac Cycle (Cardiovascular Physiology 2011) The figure shows left atrial, left ventricle, and aortic pressure along with left ventricle volume of the heart during the cardiac cycle. Starting just before the beginning of systole, there is blood coming into the heart as seen by the increasing volume into the left ventricle (LV Vol). The increase in left atrial pressure (LAP) pushes the last remaining blood from the left atria to the left ventricle. This explains the quick increase in blood volume before systole. At the start of systole, left ventricle pressure (LVP) starts to build up as valves leading into the left ventricle are closed to not allow any more blood in. During this pressure buildup, the blood volume remains the same. This is known as isovolumetric contraction. When the LVP is greater than the aortic pressure (AP), ejection of blood occurs as the pressure causes the valve leading to the aorta to open due to contraction of the heart. During the ejection period, blood from the left ventricle is going into the aorta. This leads

32 Porter 22 to a decrease in left ventricle volume. The AP increases, as well as the LVP, then reaches a peak and starts to drop as the heart relaxes leading into diastole. Diastole starts when the aortic valve closes due to the pressure no longer being able to keep the aortic valve open. When the valve shuts, it creates a transient pressure increase due to impedance mismatch between the heart and peripheral locations of the body. After the closure of the aortic valve, there is no more decrease of blood volume in the left ventricle. This is known as isovolumetric relaxation. As the diastole action continues after the aortic valve closure, AP drops as well as LVP. The ventricle then starts to fill with blood again and the cycle repeats. Blood pressure measurements given by current devices are measures of the aortic pressure at its highest point during ejection and at the lowest point during relaxation. A typical value associated with blood pressure is 120 mmhg or less for systolic and 80 mmhg or less for diastolic (American Heart Association 2013). Higher values are classified into prehypertension and hypertension which can indicate greater risk of cardiovascular disease. These higher values indicate that the heart is working harder to pump blood out to the body as it needs to build more pressure to circulate blood. This constant increase of pressure can lead to cardiovascular damage and ultimately heart failure. 3.2 Cardiac Parameters The cardiac cycle diagram from figure 3.1 gives a visual indication of several important cardiac parameters. These parameters relate to heart rate, blood volume, flow, and pressure. The first of these parameters is heart rate. Heart rate is how many times the heart contracts in one minute, or beats per minute. Heart rate can affect blood pressure, as

33 Porter 23 usually a higher heart rate can lead to higher blood pressure, for example exercising can increase both. The heart rate is first defined by the period of the heart beat which is the time from one feature of the cardiac cycle to the next. Features used vary depending on if an ECG graph is used, an AP graph, or even a PPG. A common feature used is the lowest point in the AP graph or PPG waveform when the aortic valve first opens as it is easy to detect. On an ECG, the peak or R wave is used at the start of isovolumetric contraction. Multiplying the period obtained by 60 will give the heart rate in beats per minute. Similarly, dividing the heart rate by 60 can give the period. Faster heart rates will have shorter periods as the heart has less time between contraction and relaxation. The period between heart beats can further be broken down into systole time and diastole time. In general, the diastole time is 2/3 of the total period while systole time is 1/3 of the total period (Cardiovascular Physiology 2014). This value can vary depending on cardiovascular health and other conditions. The systole time is the amount of time it takes for the blood to leave the left ventricle from opening to closing of the aortic valve. This time is also referred to ejection time or left ventricle ejection time. Higher ejection times are more common in lower heart rates. Also shown in figure 3.1 are left ventricle end systolic volume (LVESV) and left ventricle end diastolic volume (LVEDV). The difference between these two volumes is known as stroke volume. Stroke volume is the amount of blood that is pumped to the heart with every contraction (Cardiovascular Physiology 2014). From stroke volume, the cardiac output can be calculated. Cardiac output is the amount of blood that the heart pumps through the body in one minute (Cardiovascular Physiology 2014). Therefore, it can be computed simply as the stroke volume multiplied by the heart rate.

34 Porter 24 There are two additional blood pressure parameters that can be calculated from systolic and diastolic blood pressure values. These are pulse pressure and mean arterial blood pressure. Pulse pressure is the change in pressure from the diastolic level to the systolic level. Therefore, it is the difference between systolic and diastolic pressure (Sheps 2014). Pulse pressure is often an indicator of arterial stiffness as it is highly influenced by stroke volume and arterial compliance, or elasticity of the arteries. While individual systolic and diastolic numbers can be in normal ranges, a large separation between these numbers can indicate potential cardiovascular issues. Mean arterial blood pressure is the average level of blood pressure during the course of a cardiac cycle (Cardiovascular Physiology 2014). Mean arterial blood pressure is primarily affected by the cardiac output and systemic vascular resistance, or the resistance of blood flow affected by the entire vascular network. Most of the resistance is observed by peripheral vessels due to being significantly smaller than central vessels. Mean arterial blood pressure can also be calculated from pulse pressure, systolic, and diastolic pressure. 3.3 Mathematical Representations The formulas for calculating the described parameters are summarized below. HR is heart rate, T is the period of the cardiac cycle, Ts is systolic period, Td is diastolic period, SV is stroke volume, CO is cardiac output, PP is pulse pressure, SBP is systolic blood pressure, DBP is diastolic blood pressure, MABP is mean arterial blood pressure, and TPR is total peripheral resistance. From the equations, it can be seen that different values can be calculated from multiple equations through substitution.

35 Porter 25 HR = T * 60 (3.1) T = 60/HR (3.2) Ts = 1/3 * T (3.3) Td = 2/3 * T (3.4) SV = LVEDV LVESV (3.5) CO = SV * HR (3.6) PP = SBP DBP (3.7) MABP = 1/3*SBP + 2/3*DBP (3.8) MABP = DBP + 1/3*PP (3.9) MABP = CO * TPR (3.10) 3.4 Photoplethysmography Photoplethysmography is an optical technique that can detect blood volume changes in a microvascular bed of tissue (Allen 2007, 1). Interaction of biological tissue with light can scatter, absorb, or reflect light. Factors that affect this are blood volume, blood vessel wall movement and orientation of red blood cells. Detected light pulses have a direct relationship with blood perfusion in the tissue. Greater blood volume attenuates more light. Optical wavelength is an important factor in the interaction of tissue with light. The tissue is comprised largely of water that absorbs light mainly in the ultraviolet and the longer infrared wavelengths. However, there is a window in the spectra of water that allows red and near infrared light to pass more easily. Therefore, PPG sensors are incorporated with an emitter and detector at these wavelengths to

36 Porter 26 observe blood flow. Blood oxygen saturation can affect a signal due to absorption differences between oxyhemoglobin and reduced hemoglobin, except at lower infrared range of 805 nm. Tissue depth can affect the PPG signal and varies with wavelength. Usually peripheral sites are chosen such as finger tips, ear, or toe due to a decreased depth that the light must penetrate. The PPG waveform is comprised of two components consisting of an AC and DC element. The AC is the pulsatile component which is attributed to cardiac synchronous changes in blood volume with each heartbeat. The DC is the nonpulsatile component attributed to respiration, nervous system activity and thermoregulation. The DC component is used in blood oxygen saturation calculations. In the AC component, there are two phases of the pulse waveform. The first one is concerned with the systole action while the second is concerned with the diastole action of the heart. A notch can also be observed in the waveform. This notch is the result of the aortic valve closing in the heart causing a transient increase in aortic pressure. The PPG pulse changes based on age and vascular health. The waveform can have varying slope, dicrotic notch locations, or different intervals between waveforms. These changes in the waveform are related to blood pressure changes as well. This makes the AC component of the PPG waveform the best candidate for evaluating blood pressure estimates because it shows a complete cardiac cycle of the heart. Figure 3.2 shows an example PPG waveform.

37 Porter 27 Figure 3.2 PPG Waveform The PPG waveform looks very similar to the AP waveform seen in figure 3.1. This is because the PPG is measuring the volume change which is proportional to the blood pressure change. In the example PPG waveform the systole and diastole periods can clearly be seen. Sometimes, PPG waveforms do not have clear indications of the dicrotic notch to indicate the end of systole. This can be due to various reasons ranging from lack of light penetration to even reduced cardiovascular health. Typically, the dicrotic notch is seen in most healthy subject s PPG (Allen 2007, 8). PPG research has investigated PPG derivative waveforms to identify the locations of the dicrotic notch and even the reflection peak afterwards. Derivative waveforms are able to identify these points, because they are inflection points that are found using derivatives. A standard first derivative waveform for a nominal PPG is shown in figure 3.3.

38 Porter 28 Figure 3.3 First Derivative Waveform From the figure, the important characteristics of the PPG waveform are found when the derivative waveform crosses the zero of the x-axis. These points marked from the left to right of the waveform include begin systolic, systolic peak, end systolic, begin diastolic, location of reflected wave, and end diastolic. For a PPG where the dicrotic notch and reflected wave are not as noticeable, the derivative waveform looks the same in appearance, but the crossing may not always be at the zero of the x-axis. Due to this, it is impossible to know the exact location of systolic end time and the reflection point. An example of this is given in figure 3.4.

39 Porter 29 Figure 3.4 First Derivative with Alternative PPG The second derivative of the PPG waveform has been used more in PPG research than the first (Elgendi 2012). It is also known as the accelerated PPG because it is an indicator of the acceleration of the blood in the finger. The second PPG also has a steady baseline, unlike the first derivative, so it will always be centered on the zero of the x-axis. It is comprised of various peaks and valleys. PPG research has used the magnitudes of these peaks and valleys to try and classify cardiovascular health. The second derivative waveform can also have different shapes due to varying reflections in the original PPG. Figure 3.5 shows a second derivative waveform with a PPG that has a small reflection and dicrotic notch, while figure 3.6 shows a PPG with a similar, but more noticeable reflection and dicrotic notch with the altered derivative waveform. In figure 3.5, the end

40 Porter 30 systolic time and reflection are identified by the crossing at the zero of the x-axis indicated by the vertical dashed lines. Figure 3.5 Second Derivative PPG Waveform

41 Porter 31 Figure 3.6 Altered Second Derivative Waveform The main difference between the two derivative waveforms is that the shifting of the peaks so that the x-axis crossing can be above zero at the end of the systolic phase. The rest of the waveform looks similar in shape, and when the dicrotic notch is less noticeable there is a definitive x-axis crossing at zero. While the second derivative always has a steady baseline and relatively similar shape for any given PPG, the implementation in software of detecting features for locating systolic or diastolic start and stop times from the second derivative is challenging because the variations are difficult to anticipate. The third derivative waveform has not been used extensively in academia, but its properties give it an advantage when trying to identify the dicrotic notch in a PPG. Chan et al. (2007) used the third derivative waveform to calculate left ventricle ejection time using only the third derivative waveform. A point of interest was that the dicrotic notch

42 Porter 32 location was the third major peak of the waveform. The results of their study gave an r value of.89 when estimating ejection time. The error is likely due to their beginning systolic point as the first peak of the third derivative, when it was found in to be at the zero crossing before the first peak when implemented in LabVIEW. By having the dicrotic notch location at a distinct peak, it is easy to identify in software the stop time of systole. The only major morphology to the third derivative waveform is when the original dicrotic notch is present and with a large reflection wave, as with other derivative waveforms. Figure 3.7 depicts a third derivative waveform, 3.8 compares the 1 st and 3 rd derivative waveforms, and figure 3.9 presents an altered third derivative waveform. Figure 3.7 Third Derivative Waveform

43 Porter 33 Figure 3.8 Comparison of First and Third Derivative Figure 3.9 Altered Third Derivative Waveform From the figures, the third peak and following valley match the location of the dicrotic notch and the peak of the reflection wave if the original PPG doesn t contain a

44 Porter 34 prominent dicrotic notch or reflection afterward. The altered third derivative has a varying third peak, but the valley still remains. Although the location of the systolic end time and reflection are different. For implementation purposes, this doesn t create a problem because the dicrotic notch is noticeable to begin with from the original PPG and can be detected by software. As with the second derivative, the third derivative waveform does not have a wandering baseline.

45 Porter 35 Chapter 4 Modeling of Cardiovascular System The cardiovascular system can mathematically be quantified using fluid dynamic equations. Poiseuille s Law is one such example which relates volume flow rate to resistance and pressure. It states that resistance depends linearly upon the viscosity of the liquid and length of the vessel it is traveling in. Additionally, the resistance is forth power dependent upon the radius of the vessel (Walton 2014). Poiseuille s Law works well with uniform liquids, or Newtonian fluids, which have negligible turbulence. The equation for flow expressed by Poiseuille s Law is shown below with r being the radius of the vessel, P the difference between initial and final pressure, L the length of the vessel, and n as the viscosity of the liquid. Q = (4.1) Relating this equation to blood flow, it shows that as the radius of a vessel increases or decreases it can have a huge effect on the flow. A 19% increase/decrease in radius can double or halve the amount of flow. If the body is to maintain a proper flow of blood or provide an increased flow, pressure and vessel radius has to increase to meet the flow requirement. More specifically, the body dilates or contracts blood vessels instead of just increasing pressure alone. A solitary increase in pressure would be unachievable as the required pressure would be more than any blood vessel could handle. However, pressure increase can occur with dilation to assist in achieving an increased flow. For instance,

46 Porter 36 pressure may increase in exercise when the body needs more oxygen. The arterioles are a prime example of how Poiseuille s Law relates to the cardiovascular system. The arterioles are smaller vessels located just prior to the capillaries that provide the most resistance to flow and therefore can greatly affect flow rate. The vessels can contract or expand within small viable increments and greatly alter the flow to different parts of the body based on its need. This allows for the pressure to remain within a reasonable limit that the blood vessels can handle. Mechanical and electrical systems are analogous to one another. For example, pressure divided by the flow can give resistance in a mechanical system. In an electrical system, voltage (pressure) divided by the current (flow) gives the electrical resistance. Using this analogous relationship, the same can be said for the cardiovascular system. It can be classified using mechanical system relations, so can therefore be classified using electrical system relations. This has led to the development of Windkessel modeling for the cardiovascular system. The Windkessel model is a lumped model that simplifies the cardiovascular system into basic circuit elements (Catanho, Sinha, and Vijayan 2013). The model is useful for modeling blood flow and observing flow and pressure effects due to compliance and resistance changes. The basic elements include a current source, resistor, and a capacitor. This is known as the two element Windkessel model. The components are equivalent to the heart pumping blood (current source), peripheral resistance (resistor), and the volume change, or compliance of the vessels (capacitor). A two element Windkessel model is shown in figure 4.1.

47 Porter 37 Figure 4.1 Two Element Windkessel Model Additional elements can be added to the Windkessel model to create a three or four element model. The three element model includes another resistor to account for the resistance that blood flow encounters as it enters the aortic value, or input impedance. This resistance is much smaller than the peripheral resistance and can lead to the simplification of the three element model to become a two element model. A three element model is given in figure 4.2. Figure 4.2 Three Element Windkessel Model The four element Windkessel model adds an inductor in parallel with the input resistance to model the effects of inertia of blood flow. The four element model has been found to be a better representation of a more complete cardiovascular system (Westerhof, Lankharr, and Westerhof 2009) compared with two and three element models. Figure 4.3 shows a four element model.

48 Porter 38 Figure 4.3 Four Element Windkessel Model The Windkessel model problems include ignoring wave reflections and multifaceted regulatory mechanisms beyond compliance. Despite these shortcomings the Windkessel model is still an effective and simple model of a complex system. In the two element model, I(t) is the blood flow and P(t) is the blood pressure. The model can be solved using the fact that the flow into a node must equal flow out of a node. This gives an equation for the flow at any given time which is presented in Equation (4.2). I(t) = I R (t) + I C (t) (4.2) The flow through the resistive element, I R (t), is the pressure divided by the resistance. The flow through the compliance element, I C (t), is dependent on the change in pressure over time. This leads to the following differential equation given below. I(t) = (4.3)

49 Porter 39 During diastole, the aortic valve is closed and there is no flow of blood due to relaxation. Therefore, I(t) is equal to zero. Substituting this in the above equation can give the following solution for pressure during the diastole period. P(t) = for Ts t < T (4.4) This equation is an exponential decay equation that describes the drop in pressure at any time during the diastole phase as exponentially decreasing depending on the peripheral resistance and compliance. P S is the pressure at the end of systole, which is the starting pressure during diastole. For the systolic phase, the flow of blood can be modeled as an impulse, sine wave, or square wave function. These functions only have a magnitude from the start of systole to the end of ejection and the magnitude is zero thereafter as diastole starts. The impulse function is an exception as it only as magnitude at the start of each period. Therefore, it is not often used in modeling of blood flow. Figure 4.4 gives an example of modeling blood flow as a sine wave. Figure 4.4 Sine Wave Blood Flow

50 Porter 40 Q is the magnitude of blood flow, Ts is the ejection time, or systole time, and T is the period between heart beats. Given that the systolic pressure at the start of systole equals the diastolic pressure, or Pt(0) = P D, the following solution can be found for the pressure at any time during the systolic phase. P(t) = for 0 t Ts (4.5) The value for Q is different for various research papers. (Catanho, Sinha, and Vijayan 2013) assume an average value of blood flow in a cardiac cycle, being 90 cm 3. Modeling blood flow as a sine wave and setting the area under the systolic curve equal to the average value resulted in an amplitude of ml. This value has been used in other work (Ballal 2011) as well, using the same sine wave modeling. While the sine wave modeling of blood flow has been used repeatedly, the method by (Fazeli and Hahn 2012) makes more sense as they define the maximum value of Q to be the stroke volume divided by the systolic period. This produces a Q lower in amplitude that doesn t seem as radical as Catanho et al. s method. Additionally, it can allow for Q to change with stroke volume and left ventricular ejection time. 4.1 MATLAB Windkessel Model A two element Windkessel model was constructed in MATLAB to observe the effects of blood flow, pressure, resistance, and compliance given a set of input parameters. A two element model was used due to its ease of implementation and the requirement for having an accurate representation of basic cardiovascular system parameters, not a complete modeling of the entire system. This basic model helps to

51 Porter 41 illustrate cardiovascular changes and presents a visual representation through constructed graphs. The MATLAB model s purpose is to graphically show blood flow and blood pressure given input parameters such as heart rate, stroke volume, ejection time, resistance, compliance, and diastolic pressure. The model can estimate systolic blood pressure but not diastolic, as it is dependent on a starting point and cannot predict both blood pressure values due to a lack of information for given equations. The resistance and compliance values used as the inputs give a general idea of an RC pair that would allow for the corresponding systolic and diastolic blood pressure values. These RC values are not the definitive values of resistance and compliance and cannot conclude that a subject with given input parameters must have a compliance and peripheral resistance of what was estimated. Other research in physiology has attempted to estimate resistance and compliance of human subjects given various input parameters using a Windkessel model (Parlikar et al. 2007), but this is outside the scope of the MATLAB model. However, the RC values estimated are not erroneously given and are generally acceptable values for the corresponding blood pressure values. Resistance is given in units of mmhg/ml/s and compliance is given in units of mmhg/ml. The first part of the MATLAB model was to describe blood flow as a waveform and set up the maximum amplitude for it. Blood flow was defined using a sine wave, given by the equation below. Q(t) = sin( ) (4.6)

52 Porter 42 The sine wave only has a positive magnitude during the systolic period and has a zero magnitude for all time past the systolic period until the next cardiac cycle. The max amplitude was set by dividing the stroke volume by the systolic period giving units of ml/sec. The stroke volume is a user input and the systolic period is taken as a third of the total period as described from equations presented in chapter 2. A graph of the modeled blood flow is given in figure 4.5 depicting three complete cardiac cycles. Figure 4.5 MATLAB Modeled Blood Flow The diastolic and systolic pressure was calculated from Equations (4.4) and (4.5), respectively. The pressure was graphed over an interval of time as set by the number of cardiac cycles to evaluate. Previous models in literature have used a heart rate of 72, stroke volume of 90, Ts of 2/5 the total period, diastolic pressure of 80, systolic pressure between 120 and 135, and resistance and compliance used were.95 and 1.06 (Catanho, Sinha, Vijayan 2013; Ballal 2011). The MATLAB model constructed used the same heart rate and diastolic pressure, but changed Ts to be 1/3 of the total period to reflect more accurately the time spent in systole. Resistance and compliance values of.92 and 1.5 gave a systolic pressure of exactly 120. These differences in resistance and compliance

53 Porter 43 are not drastically different from previous literature values. The difference in values is largely due to decreasing systole time and a different maximum amplitude calculation. A pressure graph generated from the MATLAB model is shown in figure 4.6. Figure 4.6 MATLAB Modeled Blood Pressure Another graph was constructed using collected data instead of previous literature values. The collected data included a heart rate of 68, stroke volume of 89, systolic pressure of 134 and diastolic pressure of 71. This allowed an evaluation of R and C at a higher, pre-hypertension, systolic pressure. While diastolic pressure was below 80, the pulse pressure is quite large, suggesting a lower compliance of the arteries. The blood flow and pressure are presented in figure 4.7.

54 Porter 44 Figure 4.7 MATLAB Collected Data Due to the decreased heart rate, the time spent in the systolic period was longer, and the maximum amplitude decreased slightly due to the stroke volume being distributed over a larger period of time. The higher systolic pressure led to an increase in resistance and a decrease in compliance, even with a lower diastolic pressure. This is due to the fact that the pulse pressure is large and an increased resistance is needed to allow for a slower decrease during the diastole phase. The resistance was found to be.99 and compliance.93. Comparing to normal values presented in literature, the resistance is larger and compliance is smaller to account for an increase systolic pressure and pulse pressure. When graphing blood pressure, the MATLAB model graphs systolic and diastolic blood pressure individually on the same plot area. Therefore, with certain RC pairs, the two individual curves may appear as discontinuous instead of a continuous line. This serves the purpose of obtaining reasonable RC pairs. Adjusting the compliance value

55 Porter 45 mainly affects how small or large max blood pressure values are at the systolic value. Although, small changes to the diastolic pressure can occur. A larger compliance will lead to smaller maximums, while smaller compliance increases the maximums. The resistance largely affects the slope of the decreasing pressure curve. Small changes to pressure can also occur, but not as greatly as changing compliance values. Figure 4.8 shows an example of having a RC pair that is chosen at random. The target blood pressure for the model is 120/80 mmhg. Looking at the example, the pressure values are correct, but the diastolic curve is above the systolic start point. This means that for the given RC value, the pressure decrease is too slow given the amount of time for the diastolic phase. A decrease in R will adjust the value to decrease faster. In figure 4.9, the effect of having too small an R value is shown. The pressure decrease is too fast given the time for the diastolic phase. Figure 4.8 Large R Value

56 Porter 46 Figure 4.9 Small R Value The Windkessel model constructed in MATLAB allowed for the cardiac cycle to be understood visually and helped to demonstrate the effects of blood flow, resistance, and compliance on blood pressure. Using data from previous literature, the model was validated and the RC pair obtained was within an acceptable range with the differences occurring because of variation in modeling blood flow. Using collected data, it was observed that a larger systolic pressure can lead to an increase in pulse pressure and resistance, while reducing compliance even with lower diastolic pressure and heart rate.

57 Porter 47 Chapter 5 Implementation of Hardware and Software Specific details of the hardware and software used to construct the PPG system are presented in this chapter. As described previously, the system consists of a PPG sensor that transmits data to a PC and uses software to graph the PPG waveform, capture a short interval, allow the user to select key features of the waveform for blood pressure estimation, saves the data to a database, and utilizes a web page and Android phone app. to view the database information. The hardware design discusses all components that were used to construct the physical PPG circuit including amplification, filters, and the microcontroller. The software design describes software programs implemented along with flow charts to present an understanding of the software behavior. The LabVIEW GUI, database, web page, and Android app. are detailed in software design. 5.1 Hardware Design The hardware as a whole consists of several circuits that are necessary for the PPG sensors to relay useful information to the software. These include the power circuit needed to supply both a positive and negative voltage from a USB connection, amplifying and filtering of the raw PPG signal, and the microcontroller setup. All of the hardware is contained on a two layer printed circuit board (PCB) Power Circuit The power circuit s purpose is to take the USB voltage from the computer and regulate it to a voltage within the operating limits of the other circuit components. In addition, a special purpose integrated circuit (IC) is used to convert the positive voltage

58 Porter 48 to a negative voltage. The negative voltage is necessary for the negative rails of the operational amplifiers so that the negative component of the raw PPG signal can be amplified. The voltage from the USB port of the PC is supplied as the input voltage to the power circuit. The USB cable is connected by a module which contains a USB connection, voltage pin, ground pin, and a USB to UART converter. The voltage pin supplies 5 volts (V) from the PC. The ground pin allows for a common ground between the USB connection and the rest of the circuitry. The USB to UART converter enables communication between hardware and software. The 5 V from the USB is first regulated using a low dropout regulator (LDO). The LDO filters the incoming voltage using filtering capacitors and regulates the output at 5 V should any fluctuations occur from the incoming USB supply voltage. A filtering capacitor is also connected to the output to ensure a power signal with minimal rippling. The negative voltage is created using a switch capacitor voltage converter. The operation of the IC works primarily by switching between two capacitors. The first capacitor charges to the input voltage on the first half of the switching cycle. During the second half of the cycle the voltage is inverted and applied to the second capacitor connected to the output. The switching is controlled by an oscillator internal to the IC. The oscillator frequency controls the ripple of the output and affects the capacitor size needed PPG Circuit The PPG circuit is designed based on reflective operation. This operation uses an infrared emitter to emit light into the skin. As blood flows, light is absorbed and the light

59 Porter 49 not absorbed is reflected to an adjacent photodetector operating at the same wavelength as the emitter. Infrared light is used due to the light absorption of the hemoglobin in the blood producing the best modulation for the PPG signal in the infrared wavelength spectrum (Allen 2007, 3). A reflective operation is advantageous over a transmission operation because it is not limited by the site area and a transmission operation can have distortion due to the bone and depth required for the light to travel through the skin. Figure 5.1 illustrates a PPG sensor in reflective operation. Figure 5.1 Reflective PPG Current is generated from the photodetector based on the amount of light that it captures. The photodetector used was a phototransistor instead of a photodiode because the phototransistor is more sensitive than a photodiode, provides an internal current gain, and does not require the use of a transimpedance amplifier. While a phototransistor does not have a good high frequency response compared to a photodiode, the low frequency nature of the PPG signal does not need a high frequency response photodetector. The photodiode and phototransistor were placed in proximity to each other on an adjustable velcro band to allow for placement on finger tips of varying sizes. The use of a velcro band ensures placing the sensor securely and limiting the amount of ambient light that is received. The case construction of the emitter and detector prevents any unwanted

60 Porter 50 emitted light from going into the detector that is not reflected. The sensor is connected to the other circuit components through a wire connection leading into a header connection on the PCB. The photodiode is biased with 5 V and a 100 ohm resistor to allow for a forward current of 50 ma. This forward current provides a large amount of light emission while not going beyond the max forward current the photodiode is capable of handling or destroying the resistor due to power dissipation. The phototransistor is setup in a common emitter circuit. As light is reflected to the phototransistor s base, it generates a current and amplifies it according to the current gain of the transistor. A voltage is generated at the collector output through a load resistor connecting the collector to the supply voltage. This creates an output that moves form a high voltage to a low voltage when light is detected. A schematic of the photodiode and phototransistor setup is shown in figure 5.2. Figure 5.2 PPG Sensor As stated earlier, the PPG signal has an AC and DC component. The AC component is the actual pulse waveform that is of interest for evaluation by the software.

61 Porter 51 The magnitude of the AC component is small compared to the DC, which it is superimposed on. The AC component is only in the millivolt range, while the DC can have a much larger magnitude in the volt range. Additionally, the small amplitude of the AC waveform makes it susceptible to noise from high frequency, power line interference, and parasitic components of the phototransistor. Therefore, filtering and amplifying circuits are necessary to extract only the AC waveform and prepare it for use by the microcontroller before being sent to the software. A PPG signal directly from the phototransistor output as taken from the index finger on an oscilloscope is shown in figure 5.3. Figure 5.3 Phototransistor Output From the figure, the pulse shape can be seen, as well as the large amount of noise, and DC offset. The amplitude is approximately 10 mv peak to peak. The DC offset has to be removed before any amplification can occur, or the AC waveform will simply be driven to the rail limits of the op. amps. as the DC offset is amplified with the AC

62 Porter 52 waveform. To increase efficiency of the circuit, an active filter was implemented as it can filter, amplify, and isolate the waveform from the other stages of hardware. The high pass filter connects to the phototransistor output and the filter output is connected to the non-inverting terminal of the op. amp. which was configured as a noninverting amplifier. The cutoff frequency was chosen to be 0.2 Hz, effectively removing the DC component. Filtering above 1 Hz could potentially filter out the slower moving components of the PPG waveform. The frequency cutoff equation is given in Equation (5.1) with R being the resistor value and C being the capacitor value of the filter. F cutoff = (5.1) Eliminating the DC offset allowed the AC component to be amplified at the first stage of amplification according to the equation given in Equation (5.2) with Rf being the feedback resistor and Ra the resistor connecting to the negative terminal and Rf. This allowed for the PPG waveform to have a larger magnitude using a gain of Gain = (5.2) After the initial amplification, the PPG waveform is filtered again using a low pass filter to eliminate the high frequency components and amplified to get the minimum and maximum values of the PPG waveform in an appropriate range of the analog-todigital (A/D) converter on the microcontroller. An active filter with non-inverting gain was implemented again at this stage. A cutoff frequency of 48 Hz was selected because it

63 Porter 53 is a frequency that would not attenuate the fundamental components of the PPG waveform and reduce power line interference from 60 Hz electronic sources such as lights. The filtered waveform is amplified by a factor of 3 to set the final magnitude of the PPG to be at usable level. A schematic of the circuit is shown in figure 5.4. Figure 5.4 Filtering and Amplifying Circuit The amplified waveform, although in an acceptable voltage range, contains another DC offset. This is due to the small amount of DC offset voltage from the op. amp. and remaining offset from the PPG at the initial stage being amplified and affecting the output. Another high pass filter is used to negate the new offset. The PPG waveform now contains no offset and high frequency noise has been attenuated. Figure 5.5 presents the PPG waveform as seen on an oscilloscope after all amplification and filtering stages in the hardware.

64 Porter 54 Figure 5.5 Filtered and Amplified PPG The PPG waveform is within an acceptable voltage range for the A/D converter on the microcontroller, but has another stage of signal conditioning before it can be successfully used by the A/D. The PPG waveform is centered at 0 V and contains a negative voltage. The negative voltage is unusable by the A/D and can destroy the converter on the microcontroller. A controlled DC offset is applied to the waveform to shift the entire waveform between the supply voltage and 0 V (ground). This is accomplished using a capacitor in series with a resistor voltage divider. A schematic of the setup is given in figure 5.6.

65 Porter 55 Figure 5.6 DC Offset Circuit During experimentation, several factors were noted that affected the finger PPG signal, primarily being finger location and body movement. Different locations of the finger were noted to give varying readings, although similar in shape the amplitudes could vary for the same amount of gain from the op amps. It was found that slightly lower than the finger tip on the thumb, index, or middle finger gave the most consistent readings Microcontroller The microcontroller used to convert the PPG signal to digital and send to a PC via UART was an AVR controller in the ATMega chipset, specifically the ATMega328P. This is a RISC architecture, 8-bit processor that can operate up to 20 MHz with multiple digital I/O, 6 multiplexed analog channels, 10 bit A/D converter, and one UART communication channel. The ATMega328P s A/D converter can sample up to 15 ksps (kilosamples per second) at maximum resolution. This speed is more than capable of sampling the PPG analog voltage and satisfying the Nyquist Theorem so that aliasing doesn t occur. The Nyquist Theorem states that sampling frequency should be at least

66 Porter 56 twice as high as the largest frequency contained within the signal to avoid aliasing (Olshausen 2014). The UART channel on the microcontroller sends the digital data to the PC via UART frames. The UART channel is configured with a baud rate to setup how fast information is transferred between the microcontroller and the PC. The baud rate determines how many symbols per second are transferred, which may be different than the number of bits per second (bps) as a symbol may contain multiple bits. The UART frame is setup up for one frame containing a start bit, 8 data bits, and a stop bit. Communication is set to 9600 bps or 960 baud (9600 bps/10 bits per symbol) between the hardware and software. An intermediate protocol translation IC, or bridge, is used to translate the UART protocol to USB. The PC ports communicate using USB, which contains different voltage levels, signaling, and difference in clocking. UART is asynchronous and USB is synchronous, and the bridge keeps both the transmitting and receiving information in order between the microcontroller and the PC Printed Circuit Board All of the hardware components were populated on a PCB to create a device that was small in size, organized, and easy to use. Most of the footprints for the components used were surface mount to reduce overall board size and cost. The layout was constructed so that the placement of components presented a logical flow of data from input to output with separation of analog and digital devices. Figure 5.7 presents a block diagram of placement for the components of the PCB.

67 Porter 57 Figure 5.7 PCB Block Diagram The input and output of the circuit board are located on opposite sides to provide convenience for the user. The input is the PPG sensor attached to wires on a velcro strap and the output is the USB cable from the PC. The USB supplies the main voltage to the board before it is regulated, so the voltage regulating ICs were placed close to the immediate source of voltage supply. This did lead to increased lengths for the regulated voltage lines to the other components. To help prevent voltage drops on the supply lines, wider traces were used to reduce resistance, thereby reducing voltage drops on the power supply lines and delivering a voltage as close to the regulated output as possible. All IC components used filtering capacitors at the power supply inputs to reduce noise effects. The amplification and filtering components were set close to the PPG sensor output to keep travel distance to a minimum before the PPG signal was filtered and amplified. Increased travel distance can cause attenuation in the signal. The microcontroller was placed in the middle of the board largely due to access to the input/output pins, allowing space for interfacing, and keeping proximity to other devices

68 Porter 58 small while allowing traces to be manageable when routing. The complete PCB is shown in figure 5.8. Figure 5.8 Constructed PCB 5.2 Software Design The software design consists of three major components which include the LabVIEW graphical user interface (GUI), web page with its associated database, and the Android phone app. The LabVIEW GUI is the primary interface software between the user and the hardware by collecting the PPG data and making it available to the user. In addition, the LabVIEW GUI handles the necessary calculations and saves data to the database. The web page and Android app. primarily serve as access methods to the database.

69 Porter LabVIEW The LabVIEW program issues commands to the microcontroller to instruct when to start or stop sampling the PPG waveform and send the digital data to the PC. In order to understand these instructions, the microcontroller is programmed with a unique program called LabVIEW Interface for Arduino (LIFA) developed by National Instruments. This program allows easy interfacing between ATMega devices and LabVIEW software. It also allows greater control from the PC as the LabVIEW commands control the hardware. Before flashing the LIFA program onto the microcontroller, the microcontroller was first flashed with a bootloader program using an In Circuit Serial Programmer (ICSP) and Atmel Studio Integrated Design Environment (IDE). An ICSP allows for programming of a microcontroller when it is populated with other circuitry. The bootloader sets up the initial clock rates and allows programming through the Arduino IDE, an alternative ATMega programming software, via USB. This is necessary because the LIFA program was written for the Arduino IDE and contains additional libraries associated with the IDE that are necessary for the LIFA program to operate. The LIFA program on the microcontroller is described in the flowchart given in figure 5.9.

70 Porter 60 Figure 5.9 Microcontroller Flowchart The LabVIEW GUI on the PC was constructed in two parts that include the front panel and the block diagram. The front panel serves as the interface that the user sees when running the program. This includes graphs, controls, and indicators to display information and allow for user input. The programming of the front panel was done with the block diagram. LabVIEW is a graphical programming language and functionality is constructed using various blocks contained within the program. These blocks contain prebuilt functions and data types that interface to each other to achieve a desired operation. A high level overview of the LabVIEW operation is presented in the flow chart shown in figure 5.10.

71 Porter 61 Figure 5.10 LabVIEW Flowchart The LabVIEW program first establishes a connection with the microcontroller. Once a connection is established, it stays in a waiting state until the microcontroller is instructed to send the PPG data to the PC. This command starts the A/D converter and PPG voltage values are sent to the PC as it is converted to digital. This creates a real time graph as the array for the graph is constantly updated with a new value. The real time graph stays active until turned off or a capture sample is requested. When a sample is requested from the user, LabVIEW keeps the A/D converter active for a set period of time, saves the values to an array, and turns off the A/D converter. After a sample is captured, it is additionally filtered using a LabVIEW digital low pass filter to eliminate any noise that may have occurred between the hardware and

72 Porter 62 PC on the USB cable. The third derivative waveform is then obtained using LabVIEW math function blocks. The program then identifies peaks and valleys of the original PPG waveform. Threshold voltage levels were set for peaks and valleys. When a value is above the peak or below the valley threshold, that point s x-axis location, or time the point occurs, is saved into an array. A For loop was implemented to test the total waveform array consisting of 3000 data points. The detected peak and valley points are used to identify start and stop points for heart rate calculation as well as identifying the dicrotic notch if it is present on the original PPG. All peak and valley points are graphed with the PPG waveform sample. A peak detector was implemented on the third derivative waveform array to identify the third peak s x-axis value, which is the location of the dicrotic notch should it not be visible on the original PPG. Due to varying peaks on the third derivative, a dynamic threshold was created to allow the user to specify a threshold unique to each third derivative waveform. The third derivative graph contains two cursors that can be adjusted so that points below the maximum and above the minimum can be identified. Peaks are graphed in real time as the cursors are adjusted. The cursors remain active until the user specifies when to collect the detected points. Figure 5.11, 5.12, and 5.13 present examples of a real time PPG waveform, graphed PPG sample, and third derivative waveform with peaks and valleys detected. The yellow and red lines on figure 5.13 respectively show the maximum and minimum threshold for peak detection. The examples shown do not have a clear dicrotic notch to illustrate the use of the third derivative waveform.

73 Porter 63 Figure 5.11 Real Time PPG Waveform Figure 5.12 Captured PPG Waveform Figure 5.13 Third Derivative PPG Waveform

74 Porter 64 After the peaks and valleys are identified on the PPG and derivative waveform as confirmed by the user, the program stays in a waiting state to allow the user to normalize a single PPG pulse, or single cardiac cycle. The normalization is used to fit the pulse between a max amplitude of one and minimum amplitude of zero. This allows for consistent integration values of the systolic and diastolic phases that are necessary for blood pressure calculations. Use of a non-normalized pulse for integration was found to give sporadic results due to varying amplitudes between readings. The pulse for normalization is identified by the characteristics of the cardiac cycle which include systolic start, dicrotic notch, and diastolic end. These features were found with the peak and valley detection and displayed on the front panel. An example is given in figure The derivative locations are the X-axis values for the dicrotic notch. Valleys are the systolic start and diastolic end locations. It is possible that unwanted points are present in the array, but the correct points can be verified using the graphs and the array can be cycled through using the front panel control. These points are then entered into an input specifying the systolic phase and diastolic phase locations as seen in figure Figure 5.14 Peak and Valley Locations on Front Panel

75 Porter 65 Figure 5.15 Systole and Diastole Input Once a pulse is identified by the user, a subset array is created to store only the values from systole start to diastole end, or one cardiac cycle. The array is then normalized by using Equation (5.3), where X new is the normalized value, X is the current value in the array, X min and X max are the minimum and maximum values in the array, respectively. A normalized PPG waveform is shown in figure Xnew = (5.3) Figure 5.16 Normalized PPG

76 Porter 66 This subset array is further divided into two separate arrays consisting of points from systole start to systole end and diastole start to diastole end. These two arrays are then integrated using LabVIEW math function blocks. The results of the integration are used in blood pressure calculations as discussed in chapter 6. Heart rate and ejection time are also parameters that can be calculated using LabVIEW. These are used in calculations to estimate blood pressure and can be obtained from the peak and valley locations found with LabVIEW. Heart rate is calculated according to Equation (5.4) where S 1 and S 2 are different systole start points with S 2 > S 1. Ejection time is simply the difference between a dicrotic notch value and its corresponding systole start value. The LabVIEW program can do up to three different calculations of heart rate and ejection time to obtain an average value. The number of calculations is specified by the user. Figure 5.17 shows an example of the control input and output. HR = (S 2 S 1 ) -1 * 60 (5.4) Figure 5.17 LabVIEW Control and Output of HR and ET

77 Porter 67 A control input is also used for the final blood pressure estimation parameters which include age, weight, height, heart rate, ejection time, and PPG integration values. These values are used to calculate a series of variables using a LabVIEW math function script that estimate systolic and diastolic blood pressure. Results are displayed on the front panel as seen in figure The equations and variables calculated from the math function script are described in detail in chapter 6. Figure 5.18 LabVIEW Control and Output Blood Pressure The database is accessed and updated with LabVIEW using a library called LabSQL. This is an open source library for LabVIEW that allows it to issue SQL statements so that it may edit tables in a database. The tables are accessed by identifying the driver, server, database, access username, and access password to open a connection to the database. The SQL statements are then executed using the UPDATE SQL command to update values of a table according to their identification number. After the execution of a SQL command, the connection to the database is closed to prevent unwanted changes in the database values. LabVIEW does not implement the database, but serves as an access mechanism to it.

78 Porter Database and Web Page The database is the storage element for PPG and blood pressure data as it is taken from LabVIEW. This information is displayed on a web page which updates as the database information is changed. The web page can be viewed by a PC or phone app. and allows for visibility of data beyond the immediate user. The database program used was MySQL, which is an open source database program on a server that allows for operability on a number of systems and with multiple applications. Tables were constructed in the database for PPG and blood pressure data. Each table contains two columns with the first column serving as the identifier and the second column being the data. The identifier is used in the SQL commands to update the correct value from LabVIEW. The blood pressure table has two rows of data, one for systolic blood pressure and the other for diastolic blood pressure. The PPG data table has a number of rows equal to the data points in the waveform. Therefore, each identifier serves as the x-axis value and data value serves as the y-axis value which are necessary for the graphing functions called by the web page scripts. Figure 5.19 shows an example of a SQL table with ID 0 as the systolic blood pressure value and ID 1 as the diastolic blood pressure value. Figure 5.19 SQL Table

79 Porter 69 Due to the large number of data points in the PPG waveform, over 2,000 values with identification numbers, LabVIEW does not directly save these values to the database as this operation would take a large amount of time to open a connection, update the value, close the connection, and repeat until the entire table is updated. Scripts in PHP (Hypertext Preprocessor) are used to handle large amounts of data with SQL statements, but LabVIEW does not support PHP scripting as it is implemented on server side web pages. Therefore, data is exported from LabVIEW to an Excel spreadsheet to do quick updating using an add-on called MySQL for Excel. This enables Excel to directly connect to the database and edit it without the need for individual SQL statements. This allows for large quantities of data to be updated efficiently and flexibility in uploading to the database as the user can specify exactly what data to update into the database. For example, if only part of the PPG waveform were desired to be viewed through the web page, it is possible to only upload that portion of data. The web pages consist of PHP scripts that access the database tables to retrieve the appropriate blood pressure or PPG values. The PHP scripts first connect to the database using the appropriate login information and then select the values from a table via SQL statement contained in a loop. As the loop repeats, it retrieves a value one at a time until there are no more values to return. These values are saved into an array and encoded using JSON (JavaScript Object Notation). JSON encoded values are saved in an array format that is usable by JavaScript for graphing both blood pressure and PPG values. Graphing functions are achieved using a unique JavaScript library called FLOT. FLOT is a JavaScript library designed for handling data interaction with a web page,

80 Porter 70 specifically graphing data through interface with a database. The JavaScript sets up the JSON encoded array as a dataset to be graphed. For blood pressure, diastolic and systolic are set up as a bar graph, and the PPG graph is set up as a line graph. The graph options such as axis spacing and background color are set using FLOT functions, and finally the graph is constructed by calling the plot function of FLOT. The web page graphs are depicted in figures 5.20 and Figure 5.20 Blood Pressure Web Page Graph

81 Porter 71 Figure 5.21 PPG Web Page Graph Android Phone Application The Android app. serves as a remote way of observing information from the database through the use of mobile broadband. This allows for viewing of the data away from a PC and virtually from anywhere in the world where mobile access is available. The phone app. was constructed using App. Inventor for the MIT Center for Mobile Learning. App Inventor uses graphical programming over standard text entry coding and provides a phone emulator for debugging and verification purposes. The graphical programming nature of App. Inventor allows simplified and faster development of Android applications. The layout of the app. was constructed so that buttons could be used to access either the blood pressure or PPG graph easily. These buttons are linked to a web page that

82 Porter 72 displays the selected graph. The web pages are similar to the web pages viewed in a standard web browser on a PC, but are formatted as mobile web pages for display on a mobile device. Figure 5.22 shows an emulator example of the Android app. and the app. on a real phone is shown in figure Figure 5.22 Emulator Android App. Figure 5.23 Phone Running Android App.