Analysis of Heart Sounds and Murmurs

by Digital Signal Manipulation

by David Theodor Kerr Gretzinger

A thesis submitted in confomity with the requirements of the Degree of Master of Health Sciences, Institute of Biomedical Engineering, üniversity of Toronto.

@ Copyright by David T.K. Gretzinger, 1996.

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Abstract This thesis involves the development and preliminary testing of a device for the collection and

analysis of heart sounds, with a specific focus on heart murmurs. The device was based on a PC

and was programmed using the National Instruments LabWEW programming language.

Modularity and ease of development and distribution were facilitated through this choice of

apparatus.

The first instrument which was prograrnmed was the Heart Sound Simulator. This was

originally developed as a signal source for the testing of the subsequent modules of the program.

However, the opportunity to use it as a teaching tool was also recognized, giving this instrument

a dual purpose.

The heart sound analysis routines begin with data acquisition, for which an existing module was

adapted. A Selective Auscultation instrument was programmed to add flexibility to the standard

ph ysician's auscultation process. Frequency content estimation of heart sounds and murmurs

was facilitated through a Spectral Analysis module. Finally, reseatch into murmur sound

isolation was made in the ptograrnming of the Signal Subtraction module.

Program validation was done using the Heart Sound Simulator, normal, non-pathologic data and

pathological data. Results were encouraging, especially with the data manipulation modules.

Further developments may be done to improve -the peifonnance of the spectral analysis module.

Finally, the murmur isolation routine gave some positive indications, but hirther work is

necessary for producing a robust instrument.

Acknowledgments

The writing of this page signifies the end of an exciting two years which coincided with my t h e in the Clinical Engineering

program. This phase in my life included impact from many people.

Academically, 1 acknowledge the contributions of my two thesis advisors, Dr. John Doyle and Prof. Alf Dolan. Aiso, 1 would like to thank Munay Rice for helping me to get started in the area of heart sounds as well as in the field of Clinical Engineering in general.

Practically, 1 owe heartfelt thanks to many in the Medicai Engineering Department at the Toronto Hospital (Tony, Joe, Jack, Miranda, et. al.), as fine a source of biomedical engineering expertise as can be found.

Spirituaily, 1 have been fortunate to have the challenge and support of Brian Lim as well as Rob Knetsch, who has been my close fiiend and colleague though courses, thesis and intemships. Also, my life has been deeply afTected by the man who knew what life and death are about, Pastor Hero - he will always be remembered.

The unwavering caring presence of my mother, father and sister Michelle have equipped me in so many ways; their influence is written al1 over my life.

1 use the very last words to th&, though she asks for none, my best fiiend and wife Sharon. She has accepted al1 of me, the good and the bad, and is rny cornpanion for this increasingly interesthg adventure.

Glossary Auscult.tion - listening to the sounds of the heart, usually done using a stethoscope.

Determioistic - an event whose properties are determined by preset conditions at the time of event generation.

D U - Dynamic Link Library - this is a set of hinctions which are used by MS Windows programs to perfom various low level cornputer hinctions.

ECC - see electrocardiograrn.

Eiectrocardiogram (ECG) - the graphical dispiay of the eleceical activity of the heart.

Jitter - the variability of the tirne elapsed between the trigger and the target sound.

LibVIE W - Laborato~ Vinual Instrument Engineering Workbench - Graphical progtmm ing language for instrumentation data acquisition, control and analysis.

Murmur - the "whooshing" heart sound caused by turbulent blood flow in the heart.

Object-oriented prognmming - This is a programming method which uses the data-flow concept rather than linear flow through code as in traditional programming.

PCG - see phonocardiogram.

Phonocardiogram (PCG) - the graphical display of the heart vibrations.

Rnndom - an event whose properties are not determined by prevailing conditions at the time of event generation.

SI - first heart sound ("lub"); caused by vibrations set up by the closing of the mitral and tricuspid valves.

S2 - second heart sound ("dubb"); caused by vibrations set up by the closing of the aonic and pulmonary valves.

Sîatioaary - a sipal whose statistical properties Vary with time only.

VI - see Virtual Instrument.

Virtual Instrument - program in LabVIEW, may contain other programs within it which are then called sub-VI's.

Table of Contents

1. INTRODUCTION 8

1.1 Background 8

1.1.1 The World Wide Web 9

13 Hypothesis 9

1.3 Objectives 10

1.4 Limitations of Auscultation 10

1.5 Physiology of the Heart 12

1 S. 1 Sound Characteristics 16

* 1.6 Origias of Heart Souads and Murmurs 16

1.7 Types o f Murmurs 17

1.7.1 Systolic Munnun 17

1.72 Diastolic Murrnurs 19

1-7.3 Other Murmurs 20

2. LABVlEW PROGRAMMING LANGUAGE 21

23 Pmgromming Environment 22

2.3 Data AcquisitiodCoatrol 26

2.4 Applications 26

3. THE HEART SOUND SIMULATOR 28

4. SIGNAL PROCESSING TECHNIQUES 37

4.1 Signal Averaging 37

4.2 Frequency Analysis 38

4.2.1 Fourier Analysis 38

4.2.2 Spectral Analysis 39

4.2.3 Stationary vs. Non-Stationary 39

4.2.4 Filtering 40

4.3 Other Feature Identification Methods 41

5. THE MURMUR ANALYZER 42

5.1 Data Acquisition 42

5.2 Computer Program 43

5 3 Selective Auscultation 45

5.3.1 Description 45

5.3.2 Program Designfoperation 46

5.3.3 Speed Change 48

5.4 Signal Subtrnction 50

5.4.1 Description 51

5.4.2 Program DesigdOperation 52

5.5 Spectral Analysb 56

5 S. 1 Description 56

5 S.2 Program Design/Operation 58

6. RESULTS DISCUSSION 61

6.1 Sources of Raw Data 61

6.2 Processing of Raw Data 63

6.2.1 Heart Sound Simulator Data 63

6.2.2 Non-Pathological Patient Data 66

6.2.3 Pathological Patient Data 69

6.2.4 Recent Pathological Patient Data 72

7. CONCLUSIONS AND FUTURE DIRECTIONS 74

7.1 Conclusions 74

7.2 Future Research 75

7.2.1 Program 75

7.2.2 Analysis 76

72 .3 New Areas 76

9.REFERENCES 77

10, ADDITIONAL READING 80

1 . APPENDICES 82

List of Figures and Tables - --

F i g w 2. Fletcher-Munson curves of equal luuhess I I

Figure 2. Diagram #the human h e m 14

F i p e 3. The Cardiac Cycle 15

Figure 4. Heurt sound charocteristics 16

Figure 5. Front Panel of an example VI fur temperature monitoring 23

Figure 6. Block diagram for Exampie VI 24

Figure 7. Heort Sound Simulator front panei 29

Figure 8. Change Heart Rate VI 31

Figure 9. Sig& Assembly 33

Figure 10. Bufer Siorage 31

Figure l 1. Preser pathoiogy list 36

Figure 12. Spectral Analysis Windows J I

Figure 13. Hardware Apparat us 43

Figure 14. Murmur Anaber Panel 44

Figure 15. Seleetive A uscuItat ion VI 47

Figure 16. Speed Change VI block diagram 49

Figure 1 7. Speed change without frequency drop block diagram 49

Figure 18. Signa2 Subtraction panel 53

Figure 19. Murmur Generutor panel 51

Figure 20. Spectral Anuiysis VI panel 59

Figure 21. Speed change effects on waveform 64

Figure 22. Signal Subtraction resdts for sirnulated data 65

Figure 23. Signal Subtraction results for sirnulated data 66

Figure 24. Signal Subtmction used with normal patients 67

Fijpre 25. Signal Subfmcfion used with nontalpafients 68

Figure 26. Signal Subtruction used with normal patients 68

Figure 2 7. Spectrai Anabsis results for pathological data 70

Figure 28. Spectral Analyss results for pathological data 70

Figure 29. Spectrai Analysis results for patho logka l data 72

Figure 30. Signaf Subiraction with recent patho logical data 73

1. Introduction

1.1 Background

The functions of the human body may be transduced in many ways to give signals which may

then be interpreted to help form an assessment of the human condition. Historically,

transduction methods started with the more obvious parameters, those which could be seen,

heard or felt. Developments in technology have allowed enhancement of these basic methods

through electronic and computer processing. Therefore, the historical number of modalities of

transduction rnay be high. but a select few have suwived the erosion of the measurement

scientists' selection process. This selection is based on many factors, including the degree to

which a particular rnodality was stressed during the education process. the state of technology

relating to that measurement item and the availability of the tool.

Technological advances have been facilitating the research into both the creation of new areas

and the development of existing rnethods of monitoring of physiological signals. The

application of engineering to this biomedical problern is appropriate, as scientific measurement

theory is well in advance of technology used in clinical situations.

The specific area which this thesis addresses is the sound signals produced by the heart. In

particular, pathological conditions of the heart produce sounds which are different fiom those of

the "normal" heart. As such, the transduction of these sound vibrations rnay be used for the

detection and classification of heart pathologies.

Previous efforts in the area of heart sound processing have k e n pursued which provided a

background for this work. The UltraMonitor is a development project of the Advanced Clinical

Instrumentation G~OUP, working under the supervision of Dr. John Doyle at the Institute of

Biomedical Engineering, University of Toronto and the Toronto Hospital. Previous work done

as a part of the UltraMonitor has been published as a study of a non-invasive measure of the

contractility of the heart using the fint heart s~und. '~ and a cornparison of phonocardiographic

techniqued3. These will be discussed hirther. Each portion of research for the UltraMonitor has

been divided into computer program modules which branch out fiom the main Control Panel;

this research stands alone or may form one of those modules.

1.1.1 The World Wide Web

The use of the Intemet, specifically the World Wide Web, is growing as a mearch information

and communication tool. In this project, it was used as a source of background information on

physiology and pathology', as well as a source of clinicai for testing the program designed

as a part of this project. As a natural reaction to having benefited fiom the Internet, the results of

this research will also be made freely available there. This will be done in the form of excerpts

from this document, a visual and audible display of collected waveforms and the ability to

download sound t'les and LabVIEW program files for personal use. The site of interest may be

accessed through the Advanced C linical Instrumentation Group Web Site, wh ich is

http://auryn.rose.utoronto.ca/acig/ acig.htm 1.

1.2 Hypothesis

The heart sound signal contains much useful information which may help the clinician in

diagnoses and the researcher in learning more of the heads function. This information may be

gleaned using the human ear, but often may not. On the premise that then is such useful

infomation, the hypothesis that the hem mumur contains useful information as to the condition

of the heart is proposed. Specifically, it is proposed that much infomation is to be gleaned by

processing techniques borrowed fiom the signal processing and sound processing areas of

engineering. A powerful, yet flexible and easy to use method of utilizing these methods may be

found.

1.3 Objectives

The basic goal of this research is to develop a tool which will provide a usefil analysis of the

unique data which is contained in heart sounds and mumiun. It may then be applied to actual

signals to gain information which is directly evaluated. The placement of this tool on a platforni

which allows wide use by clinicians and researchea is a subsequent objective.

1.4 Limitations of Auscultation

An informal survey held by observing the devices hanging around the necks of many physicians

will reveal that the stethoscope is still a very commonly used device. However, its use in patient

diagnoses is in decline. Modem medicine is moving towards a more evidence-based practice,

wh ich necessitates quantitative and recordable results, neither of which are satisfied by card iac

auscultation. The hesitance to use it for diagnoses also exists because the human ear is poorly

suited for this task in a number of ways which will be discussed.

The automatic and subconscious interpretation of signals makes the stethoscope a very

subjective measurement device4. Also, many of the cardiohemic vibrations lie outside of the

audible range4, and many sounds within the range are severely damped by the signal processing

of the human ear. Fletcher-Munson curves of equal loudness, show in Figure 1, show the fact

that low frequency sounds need to have a much higher amplitude to be perceived at the sarne

loudness as those in the middle frequency range of about 1000 Hz. The very high frequency

sounds also have similar damping, though there are few physiological sounds which reach these

high Requencies.

Figure 1. Fletcher-Munson curves of equal loudness. Sounds of lower frequency are aaenuated greatly by the ear, making cardiac auscultation more difficult for some vibrations originating at the heart (fiorn Fletcher (5) page 1 88).

The perception of sound events to the human brain are influenced by preceding sounds in three

4 ways . Fatigue occun when a sound is not detected afier a sound of higher volume is given to

the ear. It is difficult to hear a sound which is quiet when louder sounds are present; this is

called masking. This quietness may be in actual terrns or the frequency may simple lie in a range

of the spectrum for which the ear is less sensitive (see Figure 1). These factors contribute to

obscure the faint murmur which is often not heard well in the presence of the louder SI and s ~ ~ .

Finally, splitting occun when sounds are too close together to be distinguished by the ear. This

varies according to frequency, but the minimum split detectable by humans is about 30 ms7.

Sound characteristics including timing instants, frequency components and envelope shapes of

murmua are best done using quantitative analysis methods4; these will be discussed more later

in Section 4, Signal Processing Techniques and Section 5, The Murmur Analyzer .

The phonocardiogram (PCG) improves some of these limitations, as it provides a quantitative

and graphical record of the hem sounds. This simple graphical display has limitations as well,

since analysis must be done manually. The pattern recognition ability of the eye is not well

suited to this modality of information display, especially in ienns of extracting details such as

frequency content which are important for characterization of the heart sound8. The

manipulation of these PCG signals is not convenient on traditional platfoms. Finally, the

advantages of cardiac auscultation are no longer available after the PCG is the only temaining

record of the heart sounds.

When analyzing human heart sounds, there is information which may be gained by simply

listening to the heart sounds as in cardiac auscultation, and by seeing the heart sound waveform

as in phonocardiography. However, more extensive analysis using some basic signal processing

methods may provide further insight into the state of the heart. This project attempts to take

advantage of the strengths which each of these three areas add to the field of heart sound

anal ysis.

1.5 Physiology of the Heart

The heart is made up of four chambers: the right and lefi ventricles, and the right and lefi atria.

The right and left sides of the heart are separated by the septum. Circulatory blood flow

proceeds through the following seqwnce: body circulation, vena cava, right atrium, right

ventricle, pulmonary artery, lungs, pulmonary vein, Iefi atrium, left ventricle, aorta, body

circulation (see Figure 2). In order for this flow to occur, the heart muscle contracts and relaxes

in a repeated cycle, with the b l d king forced to follow the correct pattern with the help of

hem valves.

Initially, the atria collect the blood fiom the circulation or lungs, depending on the side of the

hem, in a passive way. This is the filling stage of the cardiac cycle, and is called the diastole. . Dunng this period, the valves separating the atria and the ventricles are open; these are the right

(mitral) and left (tricuspid) atrioventncular (AV) valves. The valves between the right ventricle

and pulmonary artery (pulmonary valve) and the lefk ventricle and aorta (aortic valve) remain

closed during the diastole.

At the very end of the diastole, the atria contract, forcing more blood into the ventricles. The

depolarization of the cardiac cells which caused the contraction of the atria then reaches to the

ventricles. When the ventricles contract, the AV valves close due to the pressure difference, and

the blood i s ejected through the aorta or pulmonary artery. Upon completion of ventricular

contraction, the ventricles begin to relax and the dropping pressure quickly goes below that of

the aorta and pulmonary artery, at which time the aortic and pulmonary valves close.

The cardiac valves are an important feature in this project since they are the primary source of

heart murmun, the other main source being holes in the cardiac septum (see Section 1.6, Origins

of Cleart Sounds and Murmurs, page 16 ). Hem valves operate passively, acting as check valves

to prevent back flow and take no active role in controlling the direction of flow. This design

allows the timing of the cardiac cycle to be controlled by a single source which under normal

conditions is the depolarization of the sinoatrial (SA) node. The SA node has its own timing

sequence, and although the brain may modify its action, it is not under the direct control of the

brain. This makes the heart a very independent organ.

The final discussion point regarding the physiology of the heart relates to the nature of

pathological dysfunction. The heart valves may become calcified with deposits related to blood

contents. Due to the passive nature of their operation, the resulting stiffening is clearly

detrimental to the fiinction of the valves and is therefore the factor of most concem when

diagnosing the heart. The cardiac valves may become weakened by other factors, causing them

to be unable to function appropriately in the high-pressure environment of the heart. The

physical manifestation of these and other pathological conditions will be described later in

Section 1.7, Types of Mumurs, page 17.

Figure 2. Diagram of the human heart (fiom King and Showers (9) page 248).

The comlation of the sounds of the heart and the electrical activity of the heart to the physiology

described above is given in Figure 3. The fint hem sound, SI, is associated with the closing of

the mitral and tricuspid valves. The second heart sound (S3 is associated with the closing of the

aortic and pulmonary valves. This is fùnher subdivided into the aortic valve (Az) and pulmonary

valve (Pz) closures. The sounds are actually slightly separated since the pulmonary valve closes

slightly after the aortic valvet8; this may become more pronounced in pathological situations.

Figure 3. The Cardiac Cycle. The timing of the various cardiac events are displayed hue along with physiological signals attained by various meth&. The PCG C'PHONO") will be the focus of the project (fiom Tavel (1 7) inside cover).

1.5.1 Sound Characteristics

The identification of heart sounds and murmun is made primarily by the relative temporal

location in the cardiac cycle. However, the different sounds comprising the hem cycle also have

different spectral characteristics which assists in characterizing hem6. Figure 4 surnmarizes

some of the basic sounds and their relative locations and fiequency content. Relative locations

may also be observed graphically in Figure 3.

Sound 1 Location (ms) 1 Duntion 1 Approxirnrte 1

common frequency ranges1'.

S 1

s2

s 3

s, diastolic murmur (DM) systolic murmur (SM)

aortic/pulmonary insufficiency (DM)

snaps, clicks

1.6 Origins of Heart Sounds and Mutmurs

There is wide agreement that normal hean sounds (SI and S2) as heard by cardiac auscultation

are not caused directly by the closing of the valves", but by vibrations set up in cardiac blood

vessels by the sudden pressure change caused by the closing of the heart valvef. As such, the

amplitude of the heart sounds are expected to correspond with the size of pressure drop which in

Nni corresponds to the amount of fluid flow before valves closure. Also, the fiequency

characteristics correlate with the size of the valves, with lower fiequency characteristics

corresponding to larger valves.

Figure 4. Hem sound characteristics. . - Heart sounds are described in tems of temporal location and

3-5 ms from ECG - i wave 12-1 8 ms after S2

12-18 ms after ECG - P wave between Sp and S, between S, and SP between Sq and S,

varies

There has been much more debate regarding the origins of heart mumurs, likely as a result of

the wide variety of physiological causes for the murmun themselves. However, studies have

(ms) 10-1 6 8-1 4 4-8 3-6

varies varies varies

varies

~mquency Range (Hz) 30-1 50 225-400 ,

t 0-1 00 10-50 1 0-60

1

60-1 50 150-1 O00

150-1 OOO+

demonstrated that the origins of heart murmurs are the vibrations associated with turbulent

f10w12.13 . nie sound energy density was also found to be proportional to the level of

turbulencei2. Since larninar flow is the normal state of blooâ flow in human circulation, audible

rnurmua are usually associated with some pathological state. They are normally characterized

by a higher fkequency content than any other heart sounds4. Often heart mumurs do not indicate

a pathological state. Any movernent of fluid in a distensible chamber of vesse1 will cause

vibrations of some sort, so everyone has some murmur if the recording instrument is sensitive

enough '4115.

7.7 Types of Murmurs

There are a nurnber of different murmurs which may be detected by cardiac auscultation, PCG or

advanced heart =und processing. While there exist both pathological and normal mumun, both

are of interest to the cardiologist and researcher. A general review of causes of murmurs which

rnay be encountered is given here. Refer to Figure 2 for locations of the various physiological

components discussed.

7 Systolic Murmun

Systolic murmurs are divided into two categories, systolic ejection murmurs and pan-systolic

mumurs. Systolic ejection murmurs occur immediately following SI.

Aortic or pulmonary valve stenosis - This systoiic ejection murmur occun temporally

between SI and s:? The valve becomes defective afier losing some of its suppleness due

to the formation of calcific deposits. Therefore, the valve does not open fully and this results

in interference to the ffow of blood, manifested as turbulence.

The PCG of aortic stenosis is characterized by a syrnmetric diamond shape, and is lacated

near the rniddlc of the systole'! This shape rnay be explained by the fact that murmurs are

caused by turbulent blwd flow, as discussed above in Section 1.6 Origins of Heart Sounds

and Mumurs. In ejection rnurmurs, the velocity of blood increases then decreases as the

pressure ramps up and down as the heart muscle contraction goes through its cycle. In turn,

since the turbulence of blood is dependent on the Reynolds Number, which is proportional to

the velocity, the sound caused by this turbulence goes through the sarne incrpase and

decrease, giving the diamond shape12. Major causes of aortic stenosis are congenital disease,

rheumatic fever, or calcific degeneration of the valve leaflets.

Pulmonic valve stenosis is manifested in similar ways as the aortic valve stenosis described

above, with the exception that it is not symmetric, but peaks closer to the end of the

17 systole .

Mitral or tricuspid valve insufficiency - Regurgitation of blood into the atria occun during

ventricular contraction as the valves do not hoid up to the pressure applied against them4.

Since only a small gap is opened, turbulent flow results through the gap, causing this

deficiency to be made apparent by systolic murmur. The shape of this murmur of the PCG is

flat and it extends from SI to s ~ ~ ~ ?

The ongin of the common mitral insufficiency munnur is usually mitral prolapse syndrome,

which is an overly floppy mitral valve. The valves cusps become thickened. Another less

common cause is rheumatic fever. Tricuspid valve disease is caused primanly by rheumatic

fever, infective carditis or right ventricular dilation".

Innocent Murmurs - These murmun are common, especially in children, and have no

pathological origin. They always occur during the systole, as al1 diastolic murmun are

pathologic. They are caused by a hyper-dynarnic state, which is a results in a high flow rate

dirough the valve.

This condition may also occur during normal pregnancy or during pathological situations not

related to the heart such as anemia, chronic renal failure and liver disease' '.

The second type of systolic mumurs, pan-systolic, occur through the systole and often continue into

the second heart sound. These are described below.

Atrial septal defect - This defect results in a shunt between ventricles due to a hole in the

lower part of the septum which divides the heart chambers. It results in a systolic murmur,

though some diastolic munnur is also usually present. This defect nonnally has congenital

roots and is caused when the septum, which is open when the child is in the womb, does not

completely close afier birth'"1'8.

Ventricular septal defect - This defect is similar to the atrial septal defect, except that the

location of the defect is lower in the heart, in between the ventricles'*'.

1.72 Diastolic Murmurs

Mitrai or tricuspid valve stenosis - Sirnilar to aortic valve stenosis described above in

Section 1 .Tl above, the valve, becoming calcified, dces not allow the larninar passage of

blood. Interruptions cause the flow to be turbulent, resulting in vibrations which are heard as

diastolic murmurs. Mitral valve stenosis is much more cornmon than tricuspid, with the

major cause of both being rheumatic fever".

This mumur occun at the middle to the end of the diastole, and is of decrescending-

crescending shape. In a heavily calcified valve, the murmur appears as a plateau, from S2

through to sI4*I8.

Aonic or pulmonary valve insuffciency - These munnun are dificult to distinguish by

timing, but may be identified with help of the recording location. Aortic insuficiency is

caused by three principle factors, the destruction of the aortic cusps by rheumatic fever,

trauma or infective endocarditis, the dilation of the aortic valve ring preventing the cusps

from meeting properly, and the loss of structural support4*". Infective endocarditis heart

disease caused by bacterial infection and is the second most common source of illness

originated heart damage next to theumatic fever.

Occumng early in the diastole, the murmur starts with high amplitude and fades out,

forming a decrescending pattern on the PCG".

Coronary Arterial Stenosis - The nanowing of the coronary artery causes turbulent flow

which may set up audible vibrations at the onset of diastole. However, there is often not

sufficient flow to create a clearly audible mumur''.

1.7.3 Other Murmurs

O Patent ductus arteriosus, coronary arterio-venous fistulas, pulmonary atresia - These are

continuous murmurs which begin in systole and continue through S2 into diastole4.

2. LabVlEW Programming Language The National Instruments (Austin, TX) LabWEW graphical programming language was used for

the implementation of this project. A background explanation of LabMEW will serve to

illustrate some of the reasons for its use. This section gives only a bief overview; more detail

for interested readen will be found in related books" and the prograrn rnanua~s'~.

2.1 Description

LabVIEW is an ideal tool for biological signal processing projects. The function of the prograrn

is to allow the user to create a '%irtual instrumenty' for the lab environrnent using graphical

Windows programrning techniques. The development of advanced hardware instrumentation

designs is nonnally a lengthy and expensive procedure, but this is cut significantly by replacing

most of the hardware requirements with sofnvare.

The MS Windows-based graphical programming environment is another asset for such

applications as program development time is reduced by virtue of program modularity. Many

functions are included with the basic LabVIEW package and there are numerous add-on

packages such as the Analysis Toolkit and the Joint Tirne-Frequency Analysis Toolkit. These

items, as well as the ability to re-use self-written code, enhances the sophistication of the final

product.

User acceptance of LabViEW prograrns is high due to the familiarity of the conttols used. The

implementation under Microsoft Windows allows the inclusion of other Windows functions; this

feature is used in this project by including the multi-media system DLL for the audio playback of

sounds (see Section 3, The Heart Sound Simulator, page 28).

LabVIE W is an object-oriented language which uses graphical techniques for both programming

and final user interface. The underlying language ( ' G , which stands for Graphical

programming language) tuns at a comparable speed to compiled C code. The graphical

programs formed are called virtual instruments (VI) and are essentially equivalent to a function

in a text-based language. These VIS rnay be used within other VIS, which in tum rnay be used in

others, creating a hierarchical program environment By virtue of this hienuchy, nodes depend

on the lower nodes to be completed before higher nodes receive the results of their calculations.

The VI concept greatly eases the modularity of programming, making it easy to recycle code

within the program and between users. In fact, the VIS may also be used across platfoms, with

most VIS capable of being transportecl unhindered between PC, Macintosh and Sun computer

applications. There are some Vls which interact with the hardware which m u t be specific to

platforms; the Murmur Analyzer and Heart Sound Simulator has one such hinction, which is the

use of the MS Windows multi-media system library.

Programming Environment

Programming of a LabViEW VI is done in the "block diagram" display window, where functions

interface with one another by connectors, equivalent to arguments, by virtual wires. A VI rnay

also be implemented as a function, thus becoming a "sub-VI". The input and output of the

program to and from the user is perfomed on the "front panel" display window, where various

controls and indicators are placed to supply the appropriate information in a suitable format.

This is the view which is visible during the execution of the program. The graphical controls and

indicaton correspond to terminais programmed into the block diagram.

Figure 5 shows the front panel of a simple working VI created to assist in gaining a general

understanding of LabVIEW programs. This VI waits for a prompt fiom the user and then takes a

temperature reading and displays it on the graph. If the temperature is over the limit set, the

LED indicator lights up. The current data point pictured here is over the set limit, so the light is

on. The program is teminated by pressing the "stop" button.

Figure 5, Front Panel of an exampie VI for temperature monitoring. The front panel consists of the buttons, sliders and other controis needed to control the vittual instrument. Also the charts, lights and other indicators are used to display feedback results to the user.

pub-Virtual Instrument]

Figure 6. Block diagram for Example VI. The diagram contains the programming for the VI, and consists o f the fiont panel terminais and functions which manipulate data to form some output.

The biock diagram for this VI, shown in Figure 6, is the underlying program and is more

complicated than the simple front panel. The data flow may be traced through the 'kires" which

connect the control terminals, functions and indicator terminals. In this exarnple, the outer loop

structure is a "while lmp", which means that the code within it continues to execute until the

loop continuation terminal is given the Boolean input "false". This false condition coincides

with pressing the STOP button on the front panel. This button is a Boolean control, meaning it

must have one of two values, ûue or false.

In addition to the STOP button, there are two other control teminals. "Take Temperature

Reading" is the first, and has Boolean control of the case structure, which allows the code to be

executed when a TRUE value is returned. When the button is pressed, a TRUE condition ensues,

and the code which is displayed in Figure 6 is run. Otherwise, the FALSE condition ensues; the

code for this is not shown, but in this case it is empty, so nothing happens. "Maximum

Temperature" is the other control terminal displayed. and corresponds to the slider on the panel.

lnside the case structure, the "Voltage Read" sub-VI is m. In an actual application, this type of

VI would access the data acquisition card analogue-to-digital board and return the voltage given

there. In this case it is simulated data, but the result is the same: a digital voltage reading is

returned. It is then calibrated to degrees Celsius by using the "multiply" function and the

constant 100. It is assumed hen that the voltage reading is 11100 of the Celsius value; actual

applications would likely require more sophisticated calibration techniques. This value is

displayed as a data point on the "Temperature Data" chart.

Continuing to follow the wires from the fumions and tenninals, the current temperature data

point is compared with the "Maximum Temperature" value chosen by the user. The "greater

than?" function is used for this comparison; this hinction has a Boolean output which controls

the LED indicator. In an actual application, it may be imagined that radier than just lighting a

panel LED, this result could control a thermostat as part of a feedback loop controlling some

process meant to maintain an environmental temperature.

This example VI describes the LabVIEW prograrnming environment in a very basic way.

However, the complexity of the actual program is only limited by the fûnctionality of the

hardware; any conceivable fbnction may be implemented and nin. Prograrns created outside

LabVlEW may be accessed as well, increasing greatly the flexibility of LabVIEW. This is done

using Code Interface Nodes or Cal1 Library Function Nodes, which allow extemal code to be

used as LabVIEW functions.

Data Acquisitiorz/Confrol

There are three basic methods of data acquisition in LabVIEW. The fird is using data

acquisition boards, such as those created by National Instruments which are specificaily meant

for use with LabVIEW. GPlB and other hardware interface applications are used to interact with

extemal instruments and take advantage of such things as the existing interfaces, controls and

hardware while providing automation and data collection using the flexible software of

LabVIEW. Finally, the existing computer ports such as the serial port may be controlled directly

from LabVlEW to make custom applications without the use of purpose-made data acquisition

boards.

The programs dixussed in this project use a National Instruments board for data acquisition (see

Section 5.1, Data Acquisition, page 42). Data output is done using another computer card, the

sound card.

2.4 Applications

nie test laboratory environment is one of the most popular LabVlEW applications, due to the

flexibility that the program provides in collecting and analyzing a limitless variety of signals.

An example found in recent literature is a genetic algorithm (artificial intelligence) for

instrument contro12'. In this program, a robotic ami was controlled, and the AI algorithm was

used in optimizing the voltage required to make the deskd movements

In another example, evaluation of rat diastolic heart fùnction was perfonned22. Various

pressures were obtained by data acquisition by the cornputer through pressure tramducen

hooked up to a National Instruments Lab-PC+ data acquisition board. LabVlEW was then used

to analyze the data to obtain Lusitropic properiies of the heart.

Others cite the LabVlEW environment as an excellent example and place to stait in teaching data

acquisition and control with cornputers? This was cited by both researchen as being due to

the flexibility and highly modular programming which aid in development as well as in quickly

gaining an undentanding of this advanceci use of cornputers.

3. The Heart Sound Simulator - - - -

lnitially created to facilitate the testing of the Mumur Analyzer functions (se+ Section 5). the

Heart Sound Simulator program foms a powerfùl tool in its own right. As a teaching tool, heart

sounds may be simulated for demonstration to medical snuients and othen. The user-friendly

interface made possible using LabVIEW facilitates its use by anyone fiom expert to novice.

Along with the Heart Sound Simulator software, al1 that is required in ternis of hardware is a

suficientiy powerful computer equipped with a sound card. The minimum requirements for

LabVIEW on the PC are a 486 processor and 8 megabytes of RAM.

The Heart Sound Simulator program uses idealized data to imitate typical data which rnay be

collected from a monitored patient. This data was collected from the PhonoCardioSimulator

(Humetrics Corporation, CA) and modified digitally to form appropriate signals for the

Simulator. Both ECG and PCG signals are included, with variables including heart rate, sound

amplitude of each component and the addition of different types of murmun including control of

their shape, length and amplitude. The resulting sounds, assembled graphically on the screen,

rnay then be heard thmugh speakers connected through the output channel of the sound card.

3.1 Program Design/Operation

A detailed description of the nature and operation of the Heart Sound Simulator is given next.

Figure 7 on page 28 wifl be an important reference source, as it displays the main control panel

for the Heart Sound Simulator, and in turn, the user fiinctions. For further details of this

program, see Appendix C, Cornputer Program Details, Heart Sound Simulator W.

The ECG is a method of cardiac monitoring which is familiar to those in the cardiac field, and it

has been developed further than other modalities of cardiac monitoring. As such, it is also

important to display this simultaneously with any other form of time-based physioiogical signals.

When the program is smted, the display begins to scroll immediately with an ECG signal. This

is a standard signal and modification is not available, excepting the heart rate (see Figure 8). It is

used as a reference point for the PCG cycle, both for the program itself in the timing of the

cardiac cycle, and for the user, who may observe the relationship between the audible vibrations

and the electrical activity of the heart. The basic data was recorded at 60 beats per minute.

The addition of the PCG also occun upon program startup. Both the first (SI) and second (S2)

hem sounds may be individually adjusted in ternis of amplitude. This amplitude is merely a

relative amplitude for graphical display purposes and does not pertain to any particular physical

units. The sound output volume is a separate control and is adjusted using a slider on the front

panel.

The heart murmurs have more controls associated with them (see Figure 7). They may be

modified in shape (crescending, decrescending, diamond and plateau), which is important for

simulating different pathological situations associated with heart murmun (see Section 1.7,

Types of Murmurs, page 17). The length (short, medium and long) and amplitude (not

discretized) of the murmur may also be adjusted; these parameters generally Vary according to

the severity of the dysfunction or the recording location. Finally, the offset (not discretized) may

be adjusted relative to the rniddle of the span between SI and S?; the location also gives clues as

to the nature of the dysfunction.

Heart sounds are divided into two categories: systolic rnunnurs, which occur between SI and S?,

which coincides with ventricular contraction, and diastolic rnunnurs, which occur after S2 and

before the subsequent SI of the next cycle, which coincides with ventncular relaxation. Pan-

systolic murmurs and continuous murmurs are also indirectly available using the length and

offset features of one of the two existing categories. The configuration of each mumur is done

individually.

Figure 8. Change Heart Rate VI. This function allows the user to select the desired hem rate for the Simulator. Choice of heart rates is not limited to a few discrete choices, but is continuous.

The original signal source for both the ECG and PCG sipals is a clinical training device called

the PhonoCardioSimulator. High level output signals fiom the device were collected using the

analogue-CO-digital conversion function of the Lab-PC+ data acquisition card (National

Instruments, Austin, TX) in the computer. A LabVlEW program was written to collect and

customize these sounds, called the Signal Modifier (see Appendix A for details). For the basic

heart sounds (SI and Sz), this program was used to break up the signal into cardiac cycles using

the ECG as a reference point. These portions are then used to fom the continuous signal which

the Simulator generates (see Figure 9).

Heart mumurs were implemented differently. Since there are so many different types, and their

locations must be made variable through the cardiac cycle, a single file was made which includes

all murmur segments. Another utility was consmicted for piecing together this file, called Create

Mumur File (see Appendix C. Cornputer Prograrn Details). A continuous array containing al1

available combinations of murmur and the indices which separate them are stored in a single file.

The correct segment is extracted and displayed by the Heart Sound Simulator.

Amplitude, heart rate and offset changes are made by modifying source data files afier loading

them into memory (see Figure 9). This allows continuous variability of these parameten, while

the length and shape are available as a number ofdiscrete segments only.

Figure 9, Signal Assembly. (a) Each component of the heari sound is generated individually using the sequence structure, then (b) modifications are made to the standard signal. These segments are then combined togethcr into a single cycle (c).

1 H iqhest Channel

Figure 10. Buffer Storage. A nurnber of cycles, as chosen on the front panel, are stored into global vari~bles for outside use, along with other important parameters.

The constructed sarnple is displayed on the screen, with updates occumng automatically with al1

parameter changes (see Figure 9). Changes are made at the end of each cardiac cycle, as the

computer reconstructs the phonocardiographic cycle. When running the Simulator as a signal

source for the Murmur Analyzer, the number of cycles to be sent Corn the Sirnuiator is selected

using the "Cycles to Retum" slider control. The count is started as the retum button is pressed in

order to ensure that each cycle returned contains the sarne settings as described above. These

cycles are stored in a buffer (a global variable) which is accessed by the various sub-VIS of the

Murmur Analyzer (see Figure 10). Additional data regarding the signal buffer required by the

Murmur Analyzer are also stored in global variables and include the sampling fiequency, the

sampling interval, the channel labels, the number of cycles. the locations of the peaks of the QRS

complexes from the ECG, and a description for information purposes.

6,825 Since SI and S2 are generally deterministic in nature , the same data are used for each cardiac

cycle. This is consistent with the fact that the sounds are caused by the same physioiogical

mechanisrn, part of which is the vibration set up by the closing valve. This wouid be the same

on each cycle given that no other physiological parameters are changing. Conversely, the sounds

for murmurs are caused by the turbulent flow of blood through a valve or septal defect6-13.

Turbulent flow by definition is made up of random fluctuations, leading to different vibration

patterns on each cardiac cycle. Therefore. in the implernentation of hem munnurs in the

Simulator, a different set of data is used on each cycle to simulate this randomness.

In addition to the visual display of the waveform, the computer sound card may be used to listen

to the generated heart sounds. The sound volume is controlled by a slider on the fiont panel.

When the "Hear Current Sound Data" switch is selected, the hem sound waveform is sent to the

Hear Sound VI. This VI converts the LabVIEW array into a standard Microsoft wavefile format.

The resulting tile is then played using a iibrary cal1 hnction, which uses the PlaySound hnction

of the MS Windows multi-media system dynamic link library (mmsystem.dl1). This function is

the single VI which makes the program platform specific; otherwise al1 Vls may be used on PCs

and Macintosh cornputers. However, the rnodularity of the program allows full functionality

afier the Hear Sound sub-VI is replaced according to the platfonn used.

One additional feature was programmed into the Simuiator. In order to circumvent the necessity

of having a user who is knowledgeable about cardiac pathologies, a pathology list is available.

This list contains a number of conditions which rnay be chosen, each one comsponding to a

setting for each of the control knobs, sliders and selectors. Once the features are set, the user

may make modifications in the event that a variation on the preset parameten is more

appropriate, for example to imitate alternate recording locations or dysfunction severity. The list

of pathologies is set simply by programming local variables to adjust the settings of the control

panel; this list in given below in Figure 1 1.

1 Pathology 1 Characteristics

1 1 reduced S, 1

Normal Aortic Stenosis

- - - - L 1 Pulmonary Stenosis 1 systolic ejection munur, diamond shape, from S, through to S2, 1

regular Si, S2 systolic ejection murmur, diamond shape, from ECG R-wave to S2,

Atrial Septal Defect reduced P2, late S2

systolic murmur, decrescending, short length, starts immediately after

Innocent (Functional) S, but later than functional mumur, reduced S2

systolic murmur, decrescending, short length, small amplitude, starts Systolic Ejection Murmur

Dilated Aorta immediately after S, , reduced S,

systolic murmur, decrescending , medium amplitude, closely following L

Mitral lnsufficiency

Tricuspid lnsuficiency Ventricular Septal Defect

VSD & Pulmonary

1 gap between S, and mumur, large S, 1 Mitr-. - -- - - . . - .-, - - , - . - - - . . - - . . - . - - -*

J Figure 1 1 . Preset pathology list. The settings on the Heart Sound Simuiator may also be controlled by

s, pan-systolic murmur, decrescending, '/t amplitude of S,, start at S,

through to A2 diastolic mumur, decrescending, short length

pan-systolic murmur, decrescending, start at Si through to mid-S, pan-systolic murmur, plateau, S,, split S2

same as VSD except diamond shape . . t r - - - - - .

ai Valve Ptolabie 1 click between S. and S, 1

Aortic Valve lnsufficiency 1

Mitral Stenosis '

choosing fiom a list of preset cardiac dysfunctions.

diastolic murmur, decrescending, start immed. after S,, high frequency pre-systolic (Le. late diastolic) mumur, diamond, medium amplitude,

4. Signal Processing Techniques A number of signal processing techniques have been applied to the PCC with the aim of gaining

data usehl in the study of heart disease. Most methods pre-date the advent of the personal

cornputer. As such, there is great opportunity to use the power of digital manipulation and

processing of signals to gain information in a more expedient fashion.

The following sections describe a number of signal processing techniques which have k e n used

in with PCG signals, and more specifically with heart murmur analysis. A brief commentary as

to the suitability of each method is also given.

Signal A veraging

Signal averaging has been used to raise the signal to noise ratio in hem wunds". This is a

useful method, as the contamination of heart sounds by ambient sound and random instrument

noise reduces the quality of the signal. It may also be used to obtain a sound envelope, for use in

feature identification4.

Digital synchronous signal averaging of heart sounds is done by using a reference point to

segment the signal into cardiac cycles, such as the ECG QRS ~ o m ~ l e x ~ ~ , and then adding

together each point in time and dividing by the number of segments. This is a conceptually

simple procedure, and reduces the variance of the noise by a factor equal to the number of

segments used for averaging3'.

One trouble which may be experienced when using signal averaging for heart sounds is due to

variability between cycles. This is referred to as trigger jitter, where the point in time used as the

reference changes with respect to other features in the cycle. This is less significant when the

noise is of low fkquency, but becomes more significant as the fiequency increases2'. This is

more likeiy to occur in high frequency signals due to the cancellation of peaks and valleys which

are in closer proximity.

One source of trigger jitter in humans is the variation between cycles due to the subtle heart rate

change with the degree of inflation of the lung2'. This is due to the pressure placed on the heart

by the lungs.

4.2 Frequency Analysis

4.2.1 Fourier Analysis

The most natural representation of signals is to display their amplitude against tirne. Signal

processing done using this signal is known as tirne-dornain analysis. However, signal

representation in the frequency domain is ofien useful in providing additional information.

The Fourier Transform is the prime method used to transfer a temporal signal into the frequency

domain3'. It allows the signal to be divided into frequency components and their intensities. It

ir represented by the following equation for continuous signais". Note that in the tirne domain,

the signal is real. but in the frequency domain. it is represented by complex values.

The inverse Fourier Transform is as follows:

For waveforms consisting of discrete points, as in a digital signal, the discrete Fourier transfomi

is given by the following equation:

and the inverse is given by:

4.2.2 Spectral Analysis

Power specml analysis consists of a display of the signal power against the fiequency, rather

than the amplitude as in the Fourier Transfomi as described above. The energy spectrum density

is calculated using the magnitude complex Fourier transfonned signal, and is represented by:

When plotted for the discrete case, the energy spectium allows greater understanding of the

signal through viewing of the energy corresponding to the frequencies contained in the signal.

4.2.3 Stationary vs. Non-Stationary

For most biological signals, strict analysis reveals them to be non-stationary signals. That is,

their statistical properties Vary over time. These signals may not be processed using the FFT,

due to the sirnultaneous variation in tirne and freqwncy which must be accounted for.

Therefore, the approach often used is to assume the signai to be stationary within a window of

tirne. This does not require the assumption that the sounds be stationary or priodic". The

window must be chosen as long enough to detect the low-frequency components, but short

enough to be considered stationary. Segment size may either be preset or changed through the

signal based on the characteristics". There may be loss in the fiequency molution using this

technique3'.

There are a number of factors to take into account in order to obtain the ben results when using

the FFT for display of the frequency distribution. The sipal alias effect mua k avoided, in

which the sampling rate is too low, such that a high fmluency signal appears as a lower

fiequency signal when features are missed. Also, the biological signals are finite and made up of

discrete points, necessitating the w of an approximation to the mathematical transfom. This

leads to truncation enor. Finally, a window should be used to reduce the "leakage", or extra side

lobes which appear due to the approximation mentioned above. This can be reduced by

curtailing the ends of the signal by multiplying the data through a window filter, as described in

the following section. With a11 of these factors accounted for, the FFT provides a reasonable

method of converting the signal to the frequency domain2'.

Filtering methods rnay be used to reduce erron and artifacts. Problems arise in the transition

from the continuous to the discrete fourier transfom. since the discrete is only an approximation.

There is a misrepresentation from limitation of the discrete measurement between two finite

points, in contrast with the continuous signal which is integrated from negative infinity to

positive infinity.

Windowing may be used to reduce these artifacts. The signal is multiplied by a shape which

essentially reduces the input at the extreme ends of the signal spectnim, while enhancing those in

the middle. This rnethod should not be used on signals which exhibit multiple spectrum peaks,

but is effective in improving the information displayed by a single peak spectrum. Typical

windows used are displayed below in Figure 12 and their mathematical representations are given

below the charts. The p e n d for each plot i s unity (T=l).

One result of using a filter window which m u a be recognized is the loss of data since some

attenuation is applied to the signal. Specifically, this occurs in the fonn of loss of total spectral

Figure 12. Spectral Analysis Windows. Three examples of windows used in multiplication of specbum data to reduce artifact are shown here.

4.3 Other Feafure Idenfifica fion Methods

The timing of events is ofien used in distinguishing the components of the hean sounds. The

most obvious example is that once SI and S2 are located, the systolic and diastolic portions of the

signal may be located". In one study. the timing of the peak of the systolic murrnur was used to

assess the severity of aortic stenosis". A timing level was established below which aortic

stenosis could be niled out, another above which severe stenosis was deemed to be present, and a

small gap in between where there was some indication of stenosis.

5. The Murmur Analvzer Heart sounds, including munnurs in the case of individuals who possess cardiac pathology,

contain usehl information for the clinician and researcher. Unfortunately, much information

may not be obtained through traditional methods, as was discussed in Section 1.4, Limitations of

Auscultation. The alleviation of limitations given in that section was a goal for the programming

of the Murmur Analyzer which is presented in this section. Further comrnents regarding the

degree to which this objective was achieved will be given later, in Section 6, Results Discussion.

The goal of some researchen in the field of cardiology is the detection and characterization of

murmurs through automated analysis6. This is far from being accomplished at the present time,

but the ability to process PCG signals to aid in such detection and characterization is obtainable

now. Automated murmur detection is complicated by the variation which occurs between

patients. This is partially due to the biological variability which ordinarily exists between

people. Also it is due to the variation of basic measurement parameters including intensity,

frequency content and timing which are al1 affected by the recording site, the extent of cardiac

defect and the ejectate blood velocity4.

The following sections describe the combination of new applications of existing processing

methods and the novel additions to the field attempted in this project.

5.1 Data Acquisition

A number of pieces of hardware instrumentation was used to obtain data for subsequent analysis.

The software used blends seamlessly into the analysis software as described suvting on page 43,

Section 5.2, Computer Program. Since the system used was modified from one already

constructed by ~thers '"~~, it will not be described here in great detail. Some specifications of the

system are given in Appendix B, Hardware Specifications. A diagram identifjing the main

elements is shown in Figure 13.

r\ E,CG Leads

ECG Machine

m m m n

: Data Acquisition 1 0 1 Su bject t I i Card

Am . .. .!plifier I I

1 I

Personal Computer

Figure 13. Hardware Apparatus. The system is based on a high performance PC; other equipment is required for the complete data acquisition package.

The centre of the system is a persona1 computer. Though a desktop version was used for this

particular study, the use of a notebook computer would facilitate portability. m i s is more

appropriate for envisioned applications including clinic and instructional use.

5.2 Computer Program

One of the objectives for this project was to develop a PCG monitor on an accessible platform.

One method by which this was performed was to make the computer-based monitor for the

analysis of heart sounds and munnur a part of the larger UltraMonitor project k ing conducted at

the University of ~oronto'~. This project was implemented on a PC-based system, with

LabVlEW graphical programrning language used for the interface. The Murmur Analyzer

described below utilizes the full power of the modularity of LabVIEW. It may either nin on its

own or as a sub-VI of the larger UltraMonitor program. Since the developmental work of this

project was implemented using a computer program, the most appropriate method to describe

results is to trace through each section of the program. The panels for the central virtual

instruments which were prograrnmed are given in the main text; additional programs rnay be

found in Appendix C, Computer Program Details.

Figure 14. Murmur Analyzer Panel, This is the main screen o f the program created as a part of this work. This central VI opens the data which is subsequently used in the analysis Vls which are started using the buttons along the bottom of the panel.

The panel of the Mumur Analyzer is shown in Figure 14. From this main screen, the test of the

sub-programs may be accessed; this is the central data selection point. Al1 data are displayed on

the labeled graphs, and a row of buttons along the bottom of the panel allow the additional

analysis programs to be opened and nin using the data shown on this main screen.

The block diagram for the program is simple; it is essentially a messenger which gathers data

and sends it for analysis to the sub-programs. New data may be obtained dunng program

execution to replace the existing data. Sources are the Heart Sound Simulator, new data

acquisition and saved files containing trial data. The only other hnction of the Munur

Analyzer is to allow for the current wavefon to be heard through the computer's sound card.

Further details of the items described here, as well as descriptions and details of peripheral

programs not included in the main text of the thesis paper may be found in Appendix C,

Computer Program Details.

5.3 Selecüve A usculfation

5.3.1 Description

On its own, traditional cardiac auscultation is not the most effective analysis method for heart

sounds. A number of its limitations may potentially be resolved by giving the operator full

control of the sound playback. For example, audio fatigue involves the missed detection of a

quiet sound after a sound which is loud in relative tenns. This is a signal processing property of

the human ear which interferes with the detection of certain hem sound features by traditional

auscultation alone. Selectivel y listening to speci fic segments of the heart sound using adjustable

windows allows the quieter features to be heard more distinctly such that more data is included

in the analysis of the patient pathology.

5.3.2 Program Design/Operation

Figure 15 shows the front panel of the Selective Auscultation VI. It provides a visual display of

those iùnctions described in the text below.

The Selective Auscultation VI displays the basic cardiac signals: the PCG and ECG. This

standard display is adjustable in terms of x-axis and y-axis scales. When the 'Wear Sound"

switch is selected, audio playback of the selected waveform is given. Only the displayed PCG

segment is replayed; this allows the isolation and analysis of specific segments, aiding in the

diagnosis of the cardiac state.

The chosen PCG segment may also be made based on the cardiac cycle number within the

current waveform data. This is added to facilitate cycle segmentation instead of relying solely

on user selection. The segmentation is made based on the ECG signal, specifically the peak of

the QRS complex which is used as the beginning of the cardiac cycle. The QRS detection

technique used is an advanced rnethod based on mathematical morphological operators3' and has

been described previously34. Changes to the display made as a result of cycle selector changes

are based on the contents of the variable storing the sound information from its status in the

previous while loop. This allows the modification of the waveform such as speed changes (see

Section 5.3.3, Speed Change) to be displayed in the updated segment.

Selective auscultation also allows the precise calculation of timing events. This is of interest in

the identification and classification of some cardiac pathologies. For exarnple, the PR interval of

the ECG may be shortened in the case of mitral valve stenosis". The graph displayed has an X-

axis given in milliseconds; this is calculated from the input frequency which is stored as a global

variable along with other case information.

5.3.3 Speed Change

One of the key fûnctions of this VI is the speed change option. Using this function, the rate of

playback of the audio sound is adjusted. For example, the selection of 4 for the speed factor (see

Figure 7) doubles the length of the signal, or reduces the rate of playback by 50%.

This change of rate allows more careful analysis of details within the PCG waveform by giving

the ear more time to react to transient features. This may also help address the masking, fatigue

and splitting limitations of conventional auscultation.

As there is no ideal method available, there are two different computations presented. Each has

a compromise, so it is left up to the user at this point to determine the priority in terms of signal

loss.

The first computation of the new waveform is based on interpolation. The algorithm for this

method of speed change is shown below in Figure 16. The appropriate number of additional

points are interpolated between each existing point, according to the degree of speed change

selected. Using this method, the frequency of the sound also changes, as the basic shape of the

sound waveform is lengthened. The frequency of the sound playback decreases by one octave

each time the signal doubles in number of points. This translates into a signal change through

modification of pitch. Since the change is universal across the signal, the distortion is less severe

than a pitch change to one segment of the heart sound. For example, the frequency of a murmur

gives ches as to the underlying pathology, so the change in simply the murmur could result in

incorrect diagnosis. The univenal change means that SI and S2 may be used as reference points

for the analysis of other signal components.

Figure 16. Speed Change VI block diagram. Speed change made by point interpolation includes a corresponding frequency drop. Avoiding the frequency drop requires maintaining the main wavefonn shape &hile doubling segments.

ero cross- e Figure 17. Speed change without fiequency drop block diagram. The block diagram of a different algorithm involving reproducing segments of the waveform is shown.

The second computation maintains the fiequency while changing the speed of playback. To do

this, the dominant hannonic is detected by using the zero crossing points of the M: adjusted

wavefom. This adjustment refen to the centering of the signal over the correct zero amplitude

level, such that zero crossings are more accurately afFecting the dominant fiequency. Enon in

this offset could occur through transient DC drift or slight changes in the calibration of the

amplifier. This is done simply by subtracting the mean of the signal fiom each point in the

sound wavefonn.

The concept of using the zero-crossing for heart sound recording has k e n previously described;

however, a loss of frequencies outside of the dominant frequency was noted40. The segments

created by dividing the waveform by the zero crossings were duplicated according to the new

speed factor selected. Using this algorithm, there is some loss as the zero crossing does not

necessarily indicate the beginning of a new frequency. Sound waves are cornplex, necessitating

further work in order to obtain a better algorithm to perform this signal processing function.

Since the corresponding ECG would no longer be accurate after changes have been made to the

number of points in the PCG, it rnust be changed as well. However, at this point al1

segmentation and other functions for which the ECG was used have been completed; the ECG is

used only for display purposes. nierefore the scaling of the ECG x-axis is changed

programmatically by multiplying the extremes of the PCG graph by the inverse of the scaling

factor. The actual content of the ECG in memory remains unchanged.

5.4 Signal Subtracfion

In applications where it is desired to cancel elements which are common to two signals, signal

subtraction algorithms may be used. This is a novel technique in terms of application to the

phonocardiographic signal. The preparation of segments suitable for subtraction is done in this

pmgram using methods established in signal averaging. A repeated feanup, in this case the QRS

wave of the ECG, is used for synchronization.

This method is susceptible to e m r fiom trigger jitter as described above in Section 4.1, Signal

Averaging. AIso, the same-issue with high fkquency sipals king even more susceptible to

errors caused by trigger jitter applies. Therefore, using this method for extnicting unique

features fiom signal to signal is potentially fiaught with dificulty.

5.4.1 Description

The Signal Subtraction VI utilizes a novel technique For PCG analysis. The intent of this

program was to implement this new technique for the isolation of the heart murmur.

The general concept is based on the fact that the first and second heart sounds are deterministic,

while the rnumurs of the heart are random in nature6**. This may be dernonstrated by averaging

many cycles using a synchronizing point such as the R wave of the QRS complex. The resultant

waveform would reveal the heart sounds SI and Szt while any rnumurs would no longer be

6 present . Therefore, when properly aligned, the subtraction of subsequent first or second heart

sounds should result in the canceling of both heart sound signals. However, due to the random

nature of the heart murmurs caused by turbulent blood flow, any munnur portions in subsequent

cardiac cycles will not undergo the same canceling effect. Instead, a new signal will be

developed. This signal is a combination of the contributing cycles; M e r processing would be

required to provide useful analysis results.

Problems in tuming this conceptual idea into a practical analysis method were identified during

program construction. The extent to which they were successfÙlly overcome is described in

Section 6, Results Discussion on page 61.

Similar to problems experienced in signai averaging, the presence of high fkquency signals

causes error to be magnified. In such a signal, a slight offset may result in the alignment of

points with widely diverging amplitude. This causes features to be canceled in the case of sipal

averaging, or the reverse problem of features not king canceled in the case of signal subtraction.

This is described more fully above in Section 4.1, Signal Averaging. This problem and the

related one of "trigger jitter" also described above were explored in the Signal Subtraction VI.

Trigger jiler refers to the shifiing in tirne of the features used as a temporal marker to establish a

relationship between waveform segments. Two methods were used for synchronizing the cycles

to be subtracted. These are described further below. One is based on the R-wave as a trigger

and the other uses no trigger, but is based on cross-correlation between cornparison cycles.

Another potential problem is the indication in some research that SI and S2 are not

deterministic2'. This is a somewhat unanswered problem, and is discussed further in the Results

Discussion below, where experimental data is used to analyze this issue.

5.4.2 Program DesignIOperation

This program is started from the main screen of the Murmur Analyzer program, which provides

the data to be analyzed. The panel display, pictured in Figure 18, consists of a small chart

containing the full PCG signal for reference purposes, a graph for each of the segments chosen

for subtraction, and the results of the subtraction. A cycle switcher is prominent in the middle of

the screen and allows the user to select cycles from the full PCG signal in memory. The length

and offset of the cycle may be selected in the event that a standard R-wave to R-wave segment is

not sufficient.

The addition of a "synthetic" murmur is also available by pressing a button at the bottom of the

panel. A new panel which enables the design of a murmur segment is opened. This panel is

shown below in Figure 19, and either accesses the same data as that provided in the Heart Sound

Simulator, or creates random Gaussian noise according to specifications of amplitude, duration

and cycle location.

Figure 18. Signal Subuaction panel. Data from the Mumur Analyzer i s delivered to the signal subtraction VI. The current cycle for graph A and B are selected and then subtraction is done after the selected synchronization method is performed.

There are two different methods avaiiable to select from in synchronizing the cycles before

subtraftion. The first uses the R-wave of the ECG. This method of using a trigger is very

reliable when other features of the wavefom occur at f w d points relative to the chosen trigger.

With the PCG, this is normally the case, as the QRS complex of the ECG is the electrical activity

corresponding to the depolarization of the ventricles leading to contraction and systole. When

this contraction takes place, the AV valves (mitral and tricuspid) close, causing vibrations which

contribute to SI. The rest of the cardiac cycle follows along in an automatic progression, making

it predictable and therefore suited for trigger synchronization.

In a resting situation, there is one exception to this regular pattern. During inspiration, the cycle

changes slightly in length due to the additional pressure on the heart provided by the lungs8. As

such, signals used during inspiration must be done with caution.

In order to compare the trigger-synchronized subtraction with a method which uses no trigger, a

second method is made available in the Signal Subtraction program. This program takes the

segments divided by the same method as for the trigger method, but modifies their relative

positions. The appropriate additional offset is determined by a cross-correlation technique. This

is done by multiplying the two segments together point by point then summing the result. This

value is calculated for many possible offset values, and the peak of these values plotted against

the offset produces the optimal offset for signal cancellation. Mathematically, the cross-

correlation is as follows:

where xl artd x2 are the two segments to be correlated, and N is the total number of points in the

segment.

The function C(n) is the cross-correlation function. The maximum point of the curve when this

function is plotteci is identified using a peak detection algorithm; the value comsponding to this

maximum point is the offset where the two segments are most similar. The two segments are

adjusted by this offset before signal subtraction takes place.

Results frorn the signal subtraction are displayed graphically. The option to play the resulting

wavefom through the cornputer sound card is also available here. Either of the selected cycles

for subtraction or the resultant subtracted waveform is available for audio review. Details of this

VI may be found in Appendix C, Computer Program Details.

5.5 Spectral Analysis

5.5.1 Description

Plotting the frequency against the amplitude for heart sounds produces important information

4,16,42 about hean sounds and murmun, and so is used in many studies . In most cases, the fast-

Fourier transform is used to obtain the frequency spectrum, both for the heart sounds and the

munnurs 4.16.36.37 . However, some feel it is not a suitable method, citing that there is not enough

frequency resolution when dealing with heart soundsa, or that the hean sounds are not stationary

(see Glossary), a requirement for using the FFT? The Fourier transform is a linear operator3';

this is what makes it potentially unsuitable for non-stationary signals.

The use of parametric methods is recomrnended by the reason for its use cited as the

ability of this technique to characterize the non-linearity of the signal. Othen used a zero-

crossing detectoPO, but this only captures the dominant fiequency; this is clearly inadequate in

the case of heart sounds and murmun as there are many significant frequencies layered on top of

any dominant frequency. In older studies, moving band-pass filters were used to obtain the

fkquency spectrum as a number of discrete points of energy plotted against frequen~~'~; this has

now been replaced by more advanced cornputer algorithms.

Some of the evidence rnay then cause concem over the use of the Fourier transfom. However,

the important factor to recognize is that the limitations of this method do not preclude its use

entirely. Conversely, its use must be understood and the limitations taken into account. For

example, though it was found that the Fourier transform could not be resolved closely enough to

provide the time delay between the components of S2, their fiequency components could be

distinguished4'. Also, even those who detract from the use of the Fourier transform

acknowledge its usefulness in giving a gocd first approximation for fiequency a n a ~ ~ s i s ~ ~ " .

The most important feature of the PCG with respect to the mumur has been cited as the

frequency content4. Therefore, it was considered important to include this processing feature

into the Murmur Analyzer program. Fnquency domain analysis is especially important in the

diagnosis of some pathological situations, as the source of sounds are often distinguished by their

spectral content. For example, frequency analysis has been used in studies to distinguish aonic

insufficiency from mitral valve s t e n ~ s i s ~ ~ (see Section 1 J.2, Diastolic Mumun) and changes in

the spectrurn were observed after myocardial infar~tion~~. The source of mitral regurgitant

murmurs may also be distinguished by frequency spectrum, with the rupnired chordae tendinae

and mitral valve prolapse producing higher frequency mumun than those caused by papillary

muscle dysfunction3'. Spectral analysis has also been used to evaluate the status of a

bioprosthetic valve non-invasivelyM. A relation between the peak transaonic pressure gradient

and the fieqwncy content of the heari munnur has k e n estab~ished~~. In more general terms,

signifiant energy between SI and S2 or between S2 and the subsequent SI has been shown to

indicate a rn~rmur*~.

The spectral analysis plot allows the user to detemine the dominant fiequencies of the signal. In

tum, this gives dues as to the nature of the sound heard. Thetefore, this fùnction provides

insight into the nature of the PCG signal through the approximation of the fkquency content.

5.53 Program DesignlOperation

This program accepts the PCG and ECG data fiom the Mumur Analyzer. The ECG data is used

for segmentation purposes, while the PCG is analyzed using spectral analysis. Figure 20 shows

the front panel of the VI.

The actual signal portion for which the power spectrum is calculated is chosen by the user. The

segment as divided by the ECG is chosen first; the full PCG signal is shown at the top for

reference purposes. The length and offset of this segment may also be specified.

Further isolation of portions of the selected segment are often desired. For example, an artifact

in the selected cycle or a munnur in the cycle could be of interest for detemination of the signal

frequency content. For this purpose, the analysis is perfonned only on the chosen portion which

is isolated using the cursor display controlled by the mouse selector. For comparison between

cycles, the same lenph, offset and rnouse-selected portions remain during cycle selection.

The power spectrum is the most important display of the sub-program. The theory behind the

operations perfonned are discussed above in Section 4.2, Frequency Analysis, starting on page

38. The calculation made for the power spectrum is the square of the magnitude of the foufier

transforrn; in this case the Discrete Fourier Transforrn was used.

In order to tighten the resultant power spectrum, the use of a filtering window is available. There

are a number of windows, al1 having the similar effect of smoothing the profile of the power

spectrum by weighting the middle portion of the cuwe. The available windows displayed are as

follows: a Triangular, Flat Top, Hamming, Hanning, Blackman and Blackman-Harris window.

Figure 20. Spectral Analysis VI panel. This program takes the PCG and displays the power specmm and various specmim parameten for the selected portion of the signal.

The fiequency domain signal is then sent on for further calculations. The results are show on

the right side of the panel. Included in the display are the power spectrum and related

parameters obtained from it including the peak fiequency estimate and the signal power estimate.

Assessing the peak kquency is done by two means; the first is by using the fiequency

conesponding to the energy level determined by peak detection2' and the second is by using the

frequency conesponding to the centroid of the spectnim region3'. The centroid is calculated by

the f o ~ l o w i n ~ ~ ~ :

Another calculated parameter is the 95% spectral edge, which is a calculation of the frequency

below which 95% of the signal power occurs. This helps to give an impartial indication of the

maximum frequency content in the signal3". A signal power estimate calculation is also made

for the power spectrum. It is the total of al1 of the energy components around the peak as

determined above by the peak detector, and reptesents the power estimate''.

This program module is based on established signal processing techniques, so its functions are

well known and accepted. This particular application has been explored but has not found much

clinical acceptance. However, the evidence of research which demonstrates the usefulness of

4.1 8.28.36.42.44.55 spectral analysis as a murmur analysis tool supports the implementation of this

technique in the clinical setting.

6. Results Discussion .. -

One of the objectives of this research was to produce a usefil tool for the analysis of heart sound

and rnunnur data. Also, it was proposed that a flexible and easy to use method be utilized for

this tool. This is accomplished in this project, with the assistance of appropriate facilities such as

a powerfùl PC and the LabVIEW programming software. The use of a PC provided the

flexibility through the digital manipulation of data and continuously modifiable software.

LabVIEW greatly enhanced this power through the speed and modularity of prognunming which

it allows. The platform and software also combined to give accessibility, both in ternis of the

user operation of the system and outside accessibility to the programmed results. LabVIEW is

becoming a common package in research and development environments, so the code generated

and avaiiable for use may be a practical feature.

The factors described above deal with the platfonn as well as the programming competence to

the extent that the modules described nquire a certain level of performance. However, the next

objective which is to apply actual data to the constnicted system and gain useful results from

them has yet to be addressed. The following sections enter this second area in more depth to

arrive at a more objective conclusion for the results of the project.

6.1 Sources of Raw Dafa

Since this project entailed the development of a new cornputer tool, a full clinical trial was not

the goal in data testing. The goal of this section of work was to provide some data which

enabled each p ~ t i o n of the instrument to demonstrate its capabilities. Initial testing is nomally

done using simulated data, while final testing should include actual clinical data. This entire

spectrum was touched on in the collection of data with which to test this device.

Heart sounds were gathered h m a number of sources, each at different levels in ternis of

resemblance immediate or "Iive" clinic sources. The fust source was the cornputer-based

simulator which has k e n described at length above in Section 3, The Heart Sound Simulator on

page 28. A large number of different pathologies may be programmed using this source,

enabling the clinical appiication of the device to be visualized. Therefore, this was a successful

attempt to provide easy access to heart sound information.

Another source of data was actual patient data which was previously recorded for another

projec$3*34. Data acquisition methods to obtain this data were very similar to those used in this

project. However, subjects in this study were chosen for their lack of cardiac pathologies, and

analogue filtering was used to rernove murmurs and other high fiequency soundsJ3, so the

subsequent data contained no natural murmurs which could be used in teaing the murmur

analysis functions of the program. This murmur data may be added using the Add Synthetic

Munur VI, which may be accessed from the Murmur Analysis main panel.

The third source of data was pathological heart sounds recorded by other researchers and made

available over the Internet in the form of Microsoft wa~efiles~*~. This was useful in the wide

range of good quality recordings which were made available. However, only the sound file was

provided, so processing which required the use of the ECG could not be done. An ECG signal

was manually added to this data, but this was only used for reference purposes, and those

functions which require a physiologically accurate ECG synchronization was used with caution.

Finally, actual clinical data of patients who maqifest their pathology through their hem sounds

were recorded from the cardiac chic . The PCG and ECG waveforms of a number of patients

was recorded and used for the last test of the program functions.

6.2 Processing of Raw Data

6.2.1 Heart Sound Simulator Data

The Heart Sound Simulator made an excellent source for the teaching of cardiac auscultation. It

provided a visual representation of sounds in the form of the PCG and displayed the ECG which

assisted in undentanding the relationship between the features of the two signals. It is important

to also provide the relationship between these sipals and the audible manifestations of the

signals; this was also available through sound playback.

Since the data produced using the Heart Sound Simulator was physiologically accurate, it proved

to be very useful in testing the other aspects of the program. The Selective Auscultation VI took

this data quickly and performed the segmentation correctly. Further segmentation into portions

was also done successfully, as was the audio playback of the current selection. Only the display

segment was played, either one time or continuously, according to the selection.

The playback speed change was applied to the selected segment; the horizontal axis was used to

check accuracy, as with a slower speed, the time for the chosen segment rnust also be longer.

The length in time did double when the speed factor of 4 was chosen, and quadrupled when the

speed factor of 4 was chosen for either modality of speed change. Sound quality for the speed

factor change with subsequent change in frequency was good, however there was a drop in

frequency of one octave for each time the speed was halved. If the second speed change

modality was used, that which did not incur a fiequency drop, the sound quality was diminished.

The sound frequency is maintained with a loss of smoothness as the wavefom acquires sharp

directional changes due to the algorithm used. See Figure 21 for a graphical example of the

results of the speed change.

Figure 21. Speed change effects on waveform. The fvst fiorne shows the original sample sound source. The speed factor altered waveforms are the other huo frames. Sound quality is maintained if the drop in fiequency is allowed, as extra sharp transitions are not added.

The Simulator data was also used in the Signal Subtraction W. The results of this were excellent

since SI and S2 were programmed to be deterministic and the murmurs were programmed to be

random. When synchronized using the ECG or cross-correlation, the resultant waveform had no

visible SI or S2, but the murmur remained in full amplitude as cornpared with the original

samples. Clearly this was a factor of the ideal data which was generated, but it did provide

testing for the concept and related algorithm. See Figure 22 and Figure 23 for graphical display

of these results.

The last program tested with the simulated data was the Spectral Analysis VI. Since the

frequency distribution of the data were not known prior to testing, such a cornparison may not be

used to analyze the results. However, general features such as the cycle selector, segment

selection, offset and length change al1 functioned appropriately. Calculated spectrum data

including peak detection levels were confirmed manually.

F r -

25000 Cycle A - Cycle B

15000

Figure 22. Signai Subtraction results for simulated data. This routine worked well for simulated data, eiiminating the heart sounds and leaving the heart murmurs. Here S, and S, are seen in Cycle A and Cycle B which contain murmurs of mitral insufficiency, but disappear in the subtracted results.

Figure 23. Signal Subtraction results for simulated data. Similar to the above figure, the heart sounds are eliminated fiom signals including the mumur of aortic stenosis.

6.2.2 Non-Pathologicai Patient Data

The next set of data used in testing was the normal patient data with the addition of simulated

mumurs. Much of the existing data used was not appropriate due to smpling rate differences.

The purpose for data collection in the original studies were different fiom this one, thus sampling

rates of 500 Hz were used. Not only does this eliminate many of the murmurs and other sounds

of possible interest, but the sound reproduction at such sampling levels is very poor. Therefore,

this data was not as useful in testing the sound playback functions of the program, which is the

focus of the Selective Auscultation VI.

Signal Subtniction using this data gave mixed results. In some cases the subtraction worked very

well in eliminating the heart sounds. However, other samples proved to be less effective,

possibly due to the non-deterministic characteristics which exist in the heart sound signal. The

low sampling rate is another source of the error, as points which correspond to one another are

less likely to match up due to the loss in detail which occurs when the analogue signal does not

have enough digital points to represent it. Figure 24 and Figure 25 show examples of effective

and poor elimination of hem sounds.

The Spectral Analysis VI diainguished the hem sounds from the murmurs based on their

spectral content. The illustrations for this distinction is given below in Figure 27 for cardiac

pathology data, though the manifestation for this normal case showed a similar result between

the heart sounds and sirnulated murmurs.

0.15 Cycle A Cycle B O .1 0.1 5

Q)

2 0.05 a 0.1 0 u m - O 0,o.os C,

G H - O €-o.os

-0.1 -0 -1 5

Po,.: -0.1 5

-0.2 -0.2 Samples Samples

Cycle A - Cycle B

Figure 24. Signal Subtraction used with normal patients. The results ushg his data varied fram excellent hem sound elhination showed hem, to less reduction of heart sounds, show in the next figure.

Cycle A = Cycle B

Figure 25. Signal Subtraction used with normal patients. This example shows results which were not as favorable as those above.

0.15 T Cycle A 0 . 1 5 ~ , Cycle A

T Cycle A = Cycle B

Figure 26. Signal Subtraction used with normal patientr. The results for some patients proved to show almost no reduction of the har t sounds.

6.2.3 Pathological Patient Data

The next set of data used was pathological data obtained fiom other researchen. Recording

quality was very good, making it the best data in terms of hem sound and murmur samples. The

only drawback came in that the ECG data could not be used for important signal synchronization

tasks, as it was added in for nference purposes and was not actual physiological data.

The Selective Auscultation VI operated very well with this data. The sounds were clear, and the

ability to pick out specific portions of sounds was useful in hearing specific features of the

waveform. Speed changes also worked well, as with data fiom the Simulator described above.

This data could not be used reliably in the Signal Subtraction VI due to the excess trigger

temporal location variability. This was a product of the manual placement of the ECG in a

simulated fonn after the recordings.

This data provided appropriate testing for the Spectral Analysis VI, as portions of the PCG were

the main concem, and the ECG was used only for general navigation purposes. A comparison is

shown in Figure 27 and Figure 28 which demonstrate the usefblness of this analysis tool. Based

on choosing different segments of the sound file, the power spectmm showed different frequency

distributions. Figure 27 results in a higher frequency distribution than Figure 28. This

demonstrates that different murmurs may be distinguished based on their unique frequency

content17. The rnurmur of aortic insufficiency displays more energy higher fiequencies than that

of mitral stenosis; this is consistent with findings in the ~iterature'~. Although these m u n u n

occur at the same time in the cardiac cycle, this is an effective method of distinguishing them.

The fim graph in Figure 29 contains a pathology which does not manifest a munnur. This is

used to dernonstrate the distinction which may be found between heart sounds. Once again

consistent with findings in liteniture", this figure demonstrates that S2 has more energy at higher

frequencies than S ].

Normalited Power Spectrum 7 A

Figure 27. Spectral Analysis results for pathological data. This plot demonstrates the higher fkquency mumur of aortic insufficiency.

20000, Full PCG Cycle 1 sooo 4 1 I l

l2 T Nomalized Power Spectrum

Figure 28. Spectral Analysis results for pathological data. The distinction benveen the sounds of diflerent origins is made apparent through the variation in their corrcsponding fkquency specûums, as seen in the lower fiequency content of the diastolic mumur of mitral stenosis as compared with the above figure.

lSOOO T PCG Cycle t o m a b

SOOO q p

O

Smples *- .. ,. .k

S I Normalized Power Specbvm 82 Nomaîlzed Power Spectrum

Figure 29. Spectral Analysis resulü for pathological data. This VI also showed good results in identifjing the distinction in hequency components between the hem rounds. S2 normally contains higher fmluency components than S,; this is dernonstrated here.

6.2.4 Recent Pathological Patient Data

The final category of data used for testing was that gathered recently in the Cardiac Disorders

Clinic. This allowed the testing of the final module of the program, the Collect Sipals VI,

which performs the data acquisition. This hinction worked suitably, and gathered useful data for

testing the other functions of the device. The storage of recorded data, with other patient

information, is potentially useful, either as a contribution to patient records or for teaching

purposes when a patient with the particular pathology is not present. The higher cutoff

fiequency configured in the filter and the higher sarnpling rate from the data described above in

Section 6.2.2 provided more compete information as to the presence of rnurmurs and other high

frequency noise. Unfortunately, there was also more noise from the environment which was not

filtered due to the higher effective cutoff. Also, certain patients such as those with more hair of

those who had heavier body types, had lower quality recordings.

The Selective Auscultation VI perfoned well with this data, in a comparable sense to that of the

above section, Pathological Patient Data. Additional description is not necessary.

Signal Subtraction provided a number of good examples again, however not universally. The

trigger jitter problem or the non-deterministic characteristics likely had their effect in interfering

with the successful operation of this VI. An example result of a i s data is show in Figure 30.

Finally, the Spectral Anaiysis function was used with the recent pathological data. It perfonned

in a similar manner as described above in Section 6.2.3, Pathological Patient Data.

Figure 30. Signal Subtraction with recent pathological data. The "click" fiom the aortic valve of this patient shows up well after subtraction, with the hem sounds canceling each other out after subtraction.

7. Conclusions and Future Directions

7.1 Conclusions

In this project, a set of virtual instruments was programmed to perform tasks related to the

sounds of the heari. Modules were made to simulate heart sounds, to collect patient heart sound

and ECG data, to manipulate the heart sound signal, to perfonn spectral calculations on heart

sounds, and to perfom mumur isolation.

Use of the device with simulated and actual data revealed that the many huictions programmed

into the instrument worked well. Exposure to the clinical environment allowed further analysis

of the details of program operation.

The results of these tests revealed satisfactory performance, especially in the area of

manipulation and display of transduced physiological data. Spectral analysis functions also

proved to have value in the clinic, especially in objectively and quantitatively assisting with the

assessment of the source and nature of heart murmurs.

The routine which presently requires the most work is the mumur isolation instrument. Results

were inconsistent, with the instrument perfonning well in some cases but pooriy in others. This

was only preliminary work in a novel processing method, and further work is expected to

improve upon these results.

Finally, the simulation of heart sounds on a PC provided assistance to the leaming and

demonstration of heart sounds and the underlying pathologies fiom which they anke.

Enthusiasm for the use of this program was already expressed. The distribution of this module

as well as the othen described will be facilitated through distribution by way of the Intemet.

Work with this system is continuing at the time of publication. The addition of heart sounds to

teaching packages at the Toronto Hospital will be made using the hardware and cornputer

program modules set up as a part of this research. This enables both the hirther testing of

program modules, the refinement of programs used and the continuation of research efforts.

7.2 Future Reseamh

There are a nurnber of future directions which may be proposed for this research. They may be

grouped into three areas, which are program expansion, new analysis and the application of these

techniques to related areas.

7.2. l Program

When assembling a prototype or mearch device, the methods used are often chosen and applied

for flexibility and economical factors rather than eficiency of function. In application to

software, this ofien means that programs run more slowly than afier intense efforts are made to

streamiine programming. Being a hierarchical graphical windows prograrnming environment,

LabVIEW exposed itself to this kind of abuse, as layer upon layer of potentially inefficient code

was created. This layering effectively multiplies error and ineficiency. Such things as careful

choice of data types, creative use of pmgrm structure and elirnination of excess functions goes a

long way to honing the program into a more streamlined tool. External code may also be used to

reduce run time by replacing poorly perfonning LabVIEW functions.

Another area directly related to the program is the more extensive use of standard PC hardware.

The SoundBlaster card is a very common device, with which most other brands are fully

compatible with. As such, it would be an excellent source of input data and the reliance on

expensive National Instruments hardware would be eliminated. For the purposes of the work

outlined in this thesis, the dual channel analogue input is suficient. This program addition

would greatiy increase the potential distribution of the program.

Finally, the advanced methods of moving data fiom the time domain to the frequency domain

which were touched upon in Section 5.5, Spectral Analysis, may be explored fùrther. If further

processing of the resultant spectral plot is made, this will become more important for accuracy

and resolution purposes.

7.2.2 Analysis

Automation in terms of mumur detection and characterization could be added to the program.

There is currently a number of articles which attempt to classify munnun based on a number of

different parameters such as timing, length, fiequency content, relative amplitude and recording

location variation. A larger study and classification of murmurs would be required for this type

of addition to take place.

Envelope averaging is another processing feature which is used for feature detection2'. It is a

potentially valuable tool for assisting in the automation described above.

Recording from multiple sources and providing a corn parison would add information. This

would provide a basis for evaluation of the location of the pathology. Multiple simultaneous

recordings would also form another important step in the detection automation process, since a

knowledge-based computer decision process could be added based on physiological studies.

7,2,3 New Areas

The results of heari sound recording and analysis are ûansferable to other areas of study which

utilize biological vibrations for obtaining physiological data. One such area is

phonoangiography46r47, which is the use of recording of ~ u n d s of vibration relating to blood

vessels. This would be a natural area to adapt the constnicted program.

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Wanak J (1972) 'Phonocardiology: Integrated Study of Heart Sounds and Murmurs: 4" Edition' Year Book Medical Publishers Inc., Chicago.

Luisada AA, McCanon DM (1965) 'Functional bais of heart sounds' The American Journal of Cardio/ogy l6(5): 63 1 -63 3.

Sabbah HN, Stein PD (1976) 'Turbulent blood flow in humans: Its primary role in the production of ejection mumurs' Circulation Research 38(6): 5 13-525,

Rushmer RF, Morgan C (1968) 'Meaning of mumiun' The American Journal of Cardiology 21: 722- 730,

Chin JGJ, Vermariën H, Van Hollenhoven E, Wang .IN, Koops J (1990) 'The origins of innocent murmurs detected b y esophageal phonocardiography ' Acta Cardiologia 45(3): 245-248.

Stein PD (198 1) 'A Physical and Physiological Basis for the Interpretation of Cardiac Auscultation: Evaluations Based Primarily on the Second Sound and Ejection Munnurs' Futura PublrShing Company, Mount Kisco, N m York.

Johnson GR, Adolph RI, Campbell DJ (1983) 'Estimation of the severity of aortic valve stcnosis by fiequency analysis of the mumur' Journal of the American College of Cardiology l(5): 13 1 5- 1323.

Tavel ME (1985) 'Clinical Phonoçardiognphy & Extemal Pulse Recording, 4" Edition' Yem Book Medical Publishers, Chicago.

Fowler NO (199 1) 'Diagnosis of Heart Disease' Springer-Veriug, New York.

Johnson G (1994) 'LabVIEW Graphical Rogramming: Graphical Rogrammhg and Application Development' McGruw-Hill, New York.

National Instruments (1995) 'National Instruments LabVIEW Graphical Programming for Windows: User Manual, Data Acquisition Manual, Virtual Instruments Library' National Imtruments, Atrsrin, T,Y.

Moore JH (1995) 'Artificial intelligence programming with LabVIEW: genetic algorithms for instrument control and optim ization' Cornputer Metho& and Progrums in Biomedicine 47: 73-79,

O'Grady KF. Doyle DJ (1995) 'Characterization & evaluation of diastolic heart function using the LabVlE W signal processing environment' Journal of Clinical Engineering 20(4): 3 1 1-3 3 1 .

Gulotta M (1995) 'Teaching cornputer interfacing with virtual instmments in an object-orientrd language' Biophysical Journal 69: 2 168-2 173.

Kalkman CJ (1995) 'LabVIEW: A software system for data acquisition, data analysis and instrument control' Journal of Clinical Monitoring 1 1 : 51-58.

Baranek HL, Lee HC, CIoutier Ci, Durand LG (1989) 'Automatic detection of sounds and murmurs in patients with Ionescu-Shiley aortic bioprostheses' Medkaf di Biologicaf Engineering & Cornpuring 27: 449-455.

Lehner RI, Rangayyan RM (1987) 'A three-channel microcornputer system for segmentation and characterization of the phonocardiogram' IEEE Transucrions on Biomedical Engineering BM E- 34(6): 485-489.

Karpman L, Cage J, Hill C, Forbes AD, Karpman V, Cohn K (1975) 'Sound envefope averaging and the differential diagnosis of systolic murmun' American &art Jownal90(5): 600-606.

Wood JC, Barry DT (1994) 'Quantification of first heart sound fiequency dynamics across the human chest walI' Medical & Biological Engineering & Computation 32: s7 1 -s78.

Yoganathan AP, Gupta R, Corcoran WH (1976) 'Fast Fourier transfonn in the analysis of biomedical data' Medical and Biological Engineering 14(4): 239-244.

Cohen L ( 1 989) ' Ti me- fiequency distributions - a review ' Proceedings of the IEEE 77(7): 94 1-98 1.

Tohyama M, Suniki H, Ando Y (1995) 'The Nature and Technology of Acoustic Space* Academic Press, London.

Bonner AJ, Sacks HN, Tavel ME (1973) 'Assessing the severity of aonic stenosis by phonocardiography and extemal carotid pulse recordings' Circulafion 158: 247-252.

Rice ML (1 994) ' Cornparison of phonocardiographic ncording techniques ' M.H.Sc- Thesis, University of Toronto.

Lang P (1995) 'Signal processing of the fun heart sound for cardiac performance monitoringT M. A. Sc. Thesis, UnÏversiîy of Toronto.

Trahanias PE (1993) 'An approach to QRS cornplex detection using mathematical morphology' IEEE Transactions on Biomedical Engineering 40(2): 20 l-2OS.

Baracca E, Aggio S, Ansani L, Mele D, Aubert AE, Longhini C (1990) 'Indirect estimation of rnyocardial sti f i e s by a non invasive rnethod' Acta Cmdiologia 45(3): 1 95- 198.

Mori T, Ohnishi N, Sekioka KT Nakano T, Takezawa H (1986) 'Power spectrum of heart murmurs: special reference to mitral regurgitant munnurs' Journal of Cardiogruphy 16(4): 977-986.

Cohen A (1986) 'Biomedicai Signal Ptocessing Vol. 1 Time and Frequency Analysis' CRC Press, Boca Raton, FL.

Padmanabhan V , Sernmlow IL (1994) 'Dynamical analysis of diastolic heart sounds associated with coronary artery disease' Annds of Biomedical Engineering 22: 264-27 1 .

Abelson D, Bernbaum D (1971) 'A simple method of recording hem sounds and rnumurs' The American Journaf of Cardiology 28: 19 1 - 196.

Obaidat MS (1993) 'Phonocardiogram signal analysis: techniques and performance cornparison' Journal of Medical Engineering & Techno fogy 17(6): 22 1 -227.

Van Vollenhoven E, Van Rotterdam A, Dorenbos T (1969) 'Frequency analysis of heart murmurs' Medical and Biological Engineering 7 : 237-23 2.

Adolph RJ, Stephens JF, Tanaka K (1970) 'The clinical value of fiequency analysis of the first heart sound in myocardial infarction' Circulation 41 : lOO3- 10 16.

Durand LG, De Guise I, Cloutier G, Guardo R, Brais M (1986) 'Evaluation of FFT-based and modem parametric methods for the spectral analysis of bioprosthetic vaive sounds' IEEE Tramuctions on Biomedical Engineering BME-33(6): 572-578.

Johnson GR, Myes GS, Lees RS (1985) 'Evaluation of aortic stenosis by spectral analysis of the munnur' Journal of the American Colfege of Cardiofqy 6(1): 55-63.

Miller A, Lees RS, Kistler P, Abbott WM (1980) 'Spectral analysis of acterial bruits (phonoangiography): experimental validation' Cimdation 61(3): 5 15-520.

Akay YM, Akay M, Welkowitz W, Semmlow JL, Kostis JB (1993) 'Noninvasive acoustical detection of coronary anery disease: a comparative shidy of signal processing methods' IEEE 7'rusaction.s on Biomedical Engineering 40(6): 57 1 -578.

10. Additional Reading Akay M (1994) 'Biomedical Signal Rocessing' A c d m i c Press, Sm Diego.

Amon PI, Tavel ME (1984) 'Spectral analysis of heart sounds: relationships b e ~ e e n some physical characteristics and fkquency spectra of fim and second hem souch in nomaîs and hypmensives* Journal of Biomedical Engineering 6: 12 1 - 128.

Bauer S (1992) 'Online Cardiac Performance Monitoring uing Digital Signal Rocessing Techniques to Analyze the Inn-Operative Phonwardiograrn' Mmer of HeaIth Skiences Thesis, Universiéy of Toronto.

Collier IR (1993) 'Application of a The-Frequency Analysis Tool to the First Hem Sound' Master of Fieaith Science Thesis, University of Toronto.

Groom D ( 1 970) 'Standardization in phonocardiography: the microphone pic kup' CardiologV 55: 129- 135.

Harris TN, Friedman S. Tuncali MT (1964) 'Cornparison of innocent cardiac murmur of childhood with cardiac murmurs in high output state' Pedianics 33: 341-355.

Hinze JO (1975) 'Turbulence, P Edition' McGr~~,Hi l l~ NY.

Hotta S ( 1967) 'The mechanism of transmission of the cardiovascular sound: An experimental study of the conduction velocity of sound on the chest walI' Japanese Heart Journal 8: 3 54-368,

Humphries JO, McKusick VA (1962) 'The differentiation of organic and "innocent" systolic murmurs ' Prog. Cardiovascular Disease5: 1 52- 1 7 1 .

Kim ED (1 992) 'Phonocardiogram Signai Estimation ushg Wiener F i i t e ~ g ' 4" Year Electrical Engineering Thesis, Universis, of Toronto.

Lang P (1992) 'A General Tool for Computsr Based Phonocardiographic Analysis' #' Year Electricai Engineering Thesis, University of Toronto.

Luisada AA (1973) 'The Sounds of the Normal Heart' Warren H. Green, St. Louis, MS.

Mix DF (1 995) ' Random Signal Processing' Prentice Hall, Englewuod Cl@, NJ

Padmanabharn V, Sernmlow JL, Welkowitz W (1993) 'Accelerometer-type cardiac transducer for detection of low-level hem sounds' IEEE Transactions on Biomedical Engineering 4û( 1): 2 1-28.

Reynolds AJ (1974) 'Turbulent Flow in Engineering' John Wiley & Som, NY.

Sakai A, Feigen LP. Luisada AA (1971) 'Frequency distribution of the heart sounds in normal man' Cardiovascular Research 5: 558-363.

Sava HP. McDonnell(1996) 'Spectral composition of hart sounds before and afier mechanical heart valve implantation using a modified Foward-Backward Rony's method' IEEE Transactions on Biomedicol Engineering 43(7): 734.742.

Stein PD, Sabbah HN, Blick EF (1975) 'Contribution of crythrocytes to turbulent blood flow' Biorheology 12: 293-299.

Tavel ME, Brown DD. Shander D (1994) 'Enhanced auscultation with a new graphic display system' Archives of Internai Medicine 154: 893-898.

20. Tortora GJ, Grabowski SR (1993) 'Principles of anatomy and Physiology, 7h Edition' Hmper C o l h College Publishers, W.

2 1. Yellin EL (1966) 'Hydraulic noise in submerged and bounded liquid jets' Biomedicul Fluid Mechanics Symposium, ASME: 209-22 1 .

11. Appendices A. Utility Programs

Obtain Samples VI

Create Murmur File VI

Modify Existing Data VI

B. Hardware Specifications

C. Cornputer Program Details (these and other Vls on floppy disk in pocket):

Heart Sound Simulator VI

Mumur Analyzer VI

SeIective Auscultation VI

Speed Factor VI

Signal Subtraction VI

Spectral Analysis VI

Add Mumur to Data VI

Change Speed and Frequency VI

Change Speed without Frequency VI

Hear Sound VI

Play Sound (new sound) VI

Zero Crossings VI

Cross Correlate VI

Murmur Generator VI

Get Murmur Data VI

Change Heart Rate VI

Appendix A: Utility Programs In the creation of the program which was described in the main sections of this work, a number of utiliiy

prograrns were made. In order to provide a more complete representation of the Murmur AnaIyzer program,

these will be outlined briefly here.

1. Data Acquisition A module was constructed which collected physiological or simulated signals through the National

fnstruments Lab-PC+ data acquisition card. This card provides a number of digital input channels, a digital

to analogue converter for analogue output, and analogue to digital converter for analogue input. Only the

last feature was used in this project, though in the event of a lack of a sound card, the analogue output could

be configured for use. This module was adapted corn previous work done in the Advanced Clinical

Instrumentation Laboratory which runs under the guidance of Dr. D. John Doyle at the Toronto Hospital and

lnstitute of Biomedical Engineering, University of Toronto.

Figure A. Obtain Samples VI. Data acquisition fat subsequent off-line analysis was done using this program.

2. Create Murmur File One of the requirements for the Heart Sound Simulator is to have a file which contains information for the

synthetic murmur. This data is stored as a single file in order to save the processing t h e which multiple file

locations would entail. As such, a software routine was constructed to put dl of the selected component

murmurs together. This is the Create Murmur File VI, and is a simple routine which may be used in the

future for the addition of new sounds or the implementation of new data. For example, one cardiologist's

comment was that most sirnulators do not sound Jike real patients. One way to ensure that the match

between simulator and patient is to extract real patient segments for the construction of simulator segments.

The panel for this VI is pictured below in Figure B.

Figure B. Create Murmur File VI. This program may be used to piece togethet recorded file segments for the assembly of new simulator murrnur data.

3. Modlfy Existing Data Actual patient data is stored in LabVIEW dam files, and are stored in a manner prescribed by the

nroeramrner- While it is mssible to use standard formats. such as s~readsheet files. this is often not the most

efficient method in tems of file sin, retrieval speed or simply of programming convenience. Each of these

factors were reasons for the decision to store data in the LabVIEW format, One result of this is that standard

programs may not be used to modi@ data. For exarnple, it may be desired to modiS, sound files extemally

using a commercial software package. This necessitated the export into the appropriate format fkom

LabVIEW files, then the subsequent importation of those files so that they may be used in the program again.

This transfer between formats is facilitated by the ModiQ Existing Data VI, which is another utility routine

which may be used in conjunction with the Mwmur Analyzer,

Figure C. ModiQ Existing Data VI. This program allows the modification of standard LabVIEW tiles used in the Murmur Analyzer prognm.

Appendix B: Hardware Specifications

ogo

4 Channel Amdifier

~ h e unit is a four chuuiel mpUw which wu deriqaed bv the Mediail aweenng kpartment ofthe Toronto Hospital to Dr. D. John Doyle's rpeaSCIItions. It ;*(; certGied to the LSA ~ 3 2 . 2 standard !Or dcctromedicaî devices to Clus 2. The particul~n.. . . AU four chuuids ut M t aucily the same. The cbip which is urcd for ~npiiiication is Analog DMce's m d d 2B30 which ut hi& ptdo~llbllll~~, low cost. compact signil conditionhg modules de9gned rpffitiully for bigh acamcy i n t d h e to stmin gage-type transducen. Tbtre chips c0nSst of thrœ miia d o n s : r high qwüty instnunentation unpliner, i thrcx-pole low pur filter, and rn distable traducct excitation.

Setti y the Gaia

where: Rg - input nriffor (one cbooses see F i v ) = Variable 10 HZ &or (see Filpin)

For a gain of 1ûûû the rquittd Rg is 88.763 Q, for wliich an 88.7 n (Grey, Gny, Ruple, B u Brown, Red.

The varirble adjustment Rp cm be rdjustd to h e nuit the gain to the deand value.

Output OîMt Adjurtwot

The output of the 2B3 1 chip cm k immtiody o f f ! h m oao by rdj-8 the 50 M potcntiomaer.

ïhe three pole low pas f i l t r provides low-pas B d - t y p s churcteristiu ofmiaunurn overshoot response to stq inputs and r hrt rire time ud has r 60 d B / d d e roll o E b m 2 lk. Thc aitoKCtqtuq (-3 dB) is fictoiy set aZk& ait miy kincrased up to 5 kHz by the addition of zhrec uaad rrristoir. Fut conmm cutoff (-3dB) fiequencier the 1% b1 vaiues are givcn in the followuig uble.

For other desind cutoff fiequencies (Q, the mistor values un be caidud firom the foUowing equations:

whete hi is in ohms and 4 is in Hz.

Appendix C: Cornputer Program Details

Refer to floppy disk in thesis cover pocket.