Diagnostic Stethoscope Project Report

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VISVESVARAYA TECHNOLOGICAL UNIVERSITYBelgaum-590014

Project ReportOn

DIAGNOSTIC STETHOSCOPE

Bachelor of Engineering

IN

ELECTRONICS AND COMMUNICATION ENGINEERING

For the Academic Year 2013-2014

BY

BHANU PRATAP REDDY (1PE10EC018)

BHARATH KUMAR V (1PE10EC019)

CHETAN D (1PE10EC023)

SHABANA BANU S (1PE11EC420)

UNDER THE GUIDANCE OF

Mr. KIRAN KUMAR K V

Assistant Professor

Dept. of ECE, PESIT (BSC).

Department of Electronics and Communication Engineering

PESIT (Bangalore South Campus)

HOSUR ROAD

BANGALORE-560100


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PESIT (Bangalore South Campus)Hosur Road, Bangalore-560100

Department of Electronics and Communication Engineering

CERTIFICATE

This is to certify that the project work entitled DIAGNOSTIC STETHOSCOPE

carried out by Bhanu Pratap Reddy, Bharath Kumar V,Chetan D, Shabana Banu S,

bearing USNs 1PE10EC018,1PE10EC019, 1PE10EC023, 1PE11EC420,respectively in

partial fulfillment for the award of Degree of Bachelors (Bachelors of Engineering) in

Electronics and communication Engineering ofVisvesvaraya Technological University,

Belgaumduring the year 2013-2014.

It is certified that all corrections/suggestions indicated for internal assessment

have been incorporated in the Report. The project report has been approved as it satisfies

the academic requirements in respect of project work prescribed for said degree.

______________ ______________ ______________Signature of guide Signature of HOD Signature of the Principal

Mr. Kiran Kumar K V Dr. Subhash Kulkarni Dr. J Surya PrasadAssistant Professor HOD Principal/Director

Dept. of ECE Dept. of ECE PESIT(BSC)

External Viva

Name of the Examiners Signature with date

1 __________________

2. __________________


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ACKNOWLEDGEMENT

On the very outset of this report, we would like to extend our sincere and heartfelt thanks to our

guide Prof. Kiran Kumar K V and we are ineffably indebted to him for his conscientious

guidance and encouragement to the betterment of our final year project.

We are also grateful to our college PES Institute of Technology BSC for providing us the

opportunity and would also like to express a sense of gratitude to Prof. J Surya Prasadfor the

continued effort in creating a competitive environment in our college.

We would also like to convey our heartfelt thanks to our H.O.D. Prof. Subhash S Kulkarni, for

giving us the opportunity to work on a project such as this and his encouragement throughout its

Course.

We also wish to thank all the staff members of the department of Electronics & Communication

for helping directly or indirectly in completing this work successfully.

Any omission in this brief acknowledgement does not mean lack of gratitude.


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DIAGNOSTICSTETHOSCOPE

ABSTRACT

In medicine, diagnosis of a patient is in itself solves more than half of the problem of a

patient since knowing the problem gives us a reason to begin appropriate treatment. But it

is impossible for a diagnostician to be available conveniently. As we know that

sometimes if a problem goes undiagnosed then it may end up proving to be fatal.

Most of the abnormalities in a human being can be linked directly or indirectly to the

working of the heart. Hence medical diagnosis of heart problems should become

increasingly efficient and accurate. But sometimes or most of the times because of

inexperience or inability doctors prefer to consult Phonocardiogram, ECG and EKG

Physician thereby increasing the cost of medical care to the patient. So we look at means

of eliminating the human element by analysing various findings and applying diagnosis

algorithms for heart related problems.

Murmurs are a result of the presence of S3 and S4 symbols present along with S1 and S2

symbols in a heartbeat. Hence a partially portable device allowing us to diagnose heart

problems and consult a physician accordingly.

Hence the purpose of this project is to prototype a digital stethoscope to serve as a

platform for potential computer aided diagnosis applications i.e. heart rate calculation and

for the detection of cardiac murmurs.


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TABLE OF CONTENTS

Abstract

Acknowledgement

CHAPTER DESCRIPTION PAGE No.

1 PREAMBLE

1.1 Introduction 11.2 Overview 31.3 Basic Schematic Diagram 41.4 Circuit Diagram 6

2 LITERATURE SURVEY2.1 Introduction 92.2 Literature review 9

3 PROJECT PLANNING

3.1 Activities and Gantt chart 14

3.2 Milestones and Targets 16

4 HEART SOUND RECORDING SYSTEM

4.1 Piezo Pulse Sensor 184.2 Microphone 204.3 LM386 Low Voltage Audio Amplifier 21

4.4 TL072 Low Noise Dual Operational Amplifier 22

5 COMPONENTS OF HEART SOUND ANALYTICAL SYSTEM

5.1 Heart Rate 24

5.2 Heart Abnormalities and murmur 27

5.3 Wavelet transform 30

5.4 Noise Suppression 315.5 Hilbert Transform 34

5.6 Envelope Detection Using Hilbert Transform 35

6 ANALYTICAL ALGORITHM IMPLEMENTATION

6.1 MATLAB 39

6.2 Flow Chart for Heart Rate Calculation 406.3 Flow Chart for Heart murmur detection 41


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CHAPTER DESCRIPTION PAGE No.

7 PROTOTYPE & TESTING

7.1 Hardware and Software Integration 437.2 Prototype 1 447.3 Prototype 2 45

7.4 Prototype 3 47

7.5 Testing 48

8 CONCLUSION

8.1 Conclusion 52

8.2 Project Outcomes 53

8.3 Future Enhancements 54

References 55

Appendix 56List Figures

List of Flow Charts


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LIST OF FIGURES

FIGURE

NO.

FIGURE DESCRIPTION PAGE

NO.

1.1 Figure depicting different types of heart sounds 1

1.2 Circuit of prototype 1 6



2.1 Plots Showing Heart Sound before and after removal of ambient noise. 10

3.1 A list of tasks planned and carried out during the course of the project 14

3.2 Chart of the mentioned tasks 15

3.3 Resources Chart 15

3.4 Milestones Chart 16

4.1 Block Diagram of Heart Sound Measurement and analysis system 18

4.2 Equivalent circuit of piezo sensor 19

4.3 A typical piezo sensor 19

4.4 Frequency Response of Piezo sensor 20

4.5 Microphone sensor 20

4.6 LM386 Pin Diagram 21

4.7 TL072 Pin Diagram 22

5.1 Figure depicting Envelope of a given signal 36

6.1 MATLAB GUI of the diagnostic stethoscope program designed 39

7.1 3.5mm Audio Jack 43

7.2 3.5mm Audio Port 43


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FIGURE

NO.

FIGURE DESCRIPTION PAGE

NO.

7.3 Data from Sensor Circuit being recorded 43

7.4 Prototype 1 44

7.5 Prototype 2 45

7.6 Designed PCB of 2ndPrototype using fritzing app 45

7.7 Prototype 3 47

7.8 MATLAB GUI of the diagnostic stethoscope program designed 48

7.9 Data from Sensor Circuit being recorded 49

7.10 Envelope detected data and its peaks of teammates recorded heart

sound

49

7.11 Envelope detected data of a recorded normal heart sound. 50

7.12 Envelope detected data of a recorded abnormal heart sound. 50


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LIST OF FLOW CHARTS

SL NO. FLOW CHART DESCRIPTI ON PAGE NO.

1 Basic Schematic Diagram 4

2 A Flow chart describing how a series of counters are used to detect a

heart related conditions

9

3 Basic Flow diagram of a program for heart sound analysis 11

4 Flow Chart For Heart Rate Calculation 40

5 Flow Chart For Murmur Detection 41


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CHAPTER 1

PREAMBLE


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DIAGNOSTICSTETHOSCOPE PREAMBLE

DEPT. OF ECE 2013-14|PESITBSC 1 | P a g e

INTRODUCTION

The average human life span as of today stands at around 80 years compared to that of an

average of 30 years when humans first appeared around 10,000 years ago. We owe this

significant improvement to the advances in medicine and healthcare. As of today medicine

and healthcare is major aspect in human lifestyle. Heart related ailments and treatments are

a major part of it, where PCG or Phonocardiogram is one of many methods utilised.

A Phonocardiogramor PCGis a plot of high fidelity recording of the sounds and

murmurs made by theheart with the help of the machine calledphonocardiograph, or

"Recording of the sounds made by the heart during acardiac cycle." The sounds are thought

to result from vibrations created by closure of theheart valves.There are at least two: the

first when the atrio ventricular valves close at the beginning ofsystole and the second when

theaortic valve andpulmonary valve close at the end of systole. It allows the detection of

sub audible sounds andmurmurs, and makes a permanent record of these events. In

contrast, the ordinarystethoscope cannot detect such sounds or murmurs, and provides no

record of their occurrence. The ability to quantitate the sounds made by the heart provides

information not readily available from more sophisticated tests, and provides vital

information about the effects of certain cardiac drugs upon the heart. It is also an effective

method for tracking the progress of the patient's disease.

Fig1.1:Figure depicting different types of heart sounds
http://en.wikipedia.org/wiki/Hearthttp://en.wikipedia.org/wiki/Phonocardiographhttp://en.wikipedia.org/wiki/Cardiac_cyclehttp://en.wikipedia.org/wiki/Heart_valvehttp://en.wikipedia.org/wiki/Systole_(medicine)http://en.wikipedia.org/wiki/Aortic_valvehttp://en.wikipedia.org/wiki/Pulmonary_valvehttp://en.wikipedia.org/wiki/Heart_murmurhttp://en.wikipedia.org/wiki/Stethoscopehttp://en.wikipedia.org/wiki/Stethoscopehttp://en.wikipedia.org/wiki/Heart_murmurhttp://en.wikipedia.org/wiki/Pulmonary_valvehttp://en.wikipedia.org/wiki/Aortic_valvehttp://en.wikipedia.org/wiki/Systole_(medicine)http://en.wikipedia.org/wiki/Heart_valvehttp://en.wikipedia.org/wiki/Cardiac_cyclehttp://en.wikipedia.org/wiki/Phonocardiographhttp://en.wikipedia.org/wiki/Heart


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In the case of our project, PCG is the basis of the algorithms we designed for heart rate

calculation and detection of murmurs, we intend to classify the input heart sounds as

normal or abnormal based on the features we extract. Although various features and

classification approaches have been successfully developed and tested, the performance of

the algorithms depends highly on the specific training and evaluation data sets. Pattern

recognition in medical diagnostics often suffers from a lack of data, particularly in

comparison to the problems being solved in non-medical fields such as voice recognition

in audio recordings or face detection in images. The lack of data is partly due to patient

confidentiality but is also caused by the limited number of available ground truth datasets.

Trained physicians and technicians are often needed to generate accurate ground truth

annotations. In comparison, almost any individual is capable of labelling faces in image orthe words that are being spoken in a recording.


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OVERVIEW

Cardiac murmurs are pathologic sounds that are produced by turbulent blood flow in the

heart. Detailed diagnoses of pathologic murmurs often require echocardiogram procedures.

Although the procedure is effective, it requires special equipment and trained technicians

to capture the necessary images and measurements. On the other hand, heart murmurs can

sometimes be detected by a physician using a standard stethoscope during auscultation.

This procedure is commonly performed during routine check-ups. However, depending on

the grade or severity of the murmur, the quality of the stethoscope, and the training and

skill of the physician, it can be difficult for a physician to distinguish a murmur from a

normal heartbeat. This design project aims to assist physicians in detecting heart murmursby analysing cardiac signals in real time during auscultation and reporting any detected

abnormalities. The task of designing a cardiac murmur detection algorithm has been

previously explored by several researchers in various academic groups. In general, the task

can be described as a pattern recognition problem using 1-dimensional medical data.

Pattern analysis typically involves two key steps:

Feature extraction

Classification

In feature extraction, one or more discriminative metrics are calculated using the input

data.

These metrics are then used in classification to assign a specific class label to the input data.

For the problem of detecting cardiac murmurs, the classification is binary assigning either

a normal or murmur label to the analysed data.


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The Diagnostic stethoscope has mainly two functioning aspects namely:

The Sensor Circuit

MATLAB

The main job of the sensor circuit is to pick up heart beat sounds and effectively convert

them into electrical signals which can be further signal processed. The performance of the

sensor circuit plays a major role in the reliability of the stethoscope. The circuits designed

in the case of our project were based on piezo electric sensors or microphones each having

it pros and cons. The signals are fed into MATLAB through the microphone port.

The MATLAB aspects can be further divided into three parts namely:

Denoising

Heart rate Calculation Algorithm

Envelope Detection

Murmur Detection Algorithm

The Input signal obtained from the sensor circuit is usually induced with a significant

amount of noise enough to hamper the effectiveness of the algorithms utilised hence, we

denoise the input signal through the utilisation of wavelet transform. The process will be

discussed in more detail in the later chapters.

After S1 and S2 peaks are detected from the input signal, using the heart rate detection

algorithm realised as a MATLAB program, we calculate the effective heart rate of the input

signal.

In order to detect S3 and S4 symbols we first need the envelope detected output of the input

signal. We realise this in the frequency domain after taking the Hilbert transform of the

signal and later taking inverse of the magnitude of the signal in frequency domain.

Having Detected S3 and S4 if any through envelope detection, we can classify the heart

beat either as normal or abnormal.


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CIRCUIT DIAGRAM

The Circuit Diagram of the three prototype circuit is given as follows:

Piezo Sensor Circuit 1

Fig1.2: Circuit of prototype 1

This circuit mainly acquired and amplified the Heart beat signal it did nothing to

filter the noise. We discuss in detail about this prototype in the later chapters.

Piezo Sensor Circuit 2



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The Only difference between Prototype 1 & 2 is the addition of TL072 in 2nd

prototype which allows some noise cancelling capability. We discuss in detail

about this prototype in the later chapters.

Microphone Sensor Circuit


This Sensor circuit depends heavily on the performance of the microphone. It is

also easily prone to noise induction. We discuss in detail about this prototype in

the later chapters.


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CHAPTER 2

LITERATURE SURVEY


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DIAGNOSTICSTETHOSCOPE LITERATURESURVEY


INTRODUCTION

With the advent of the 21st century medical diagnosis of heart problems has become

increasingly efficient and accurate. But sometimes or most of the times because of

inexperience or inability doctors prefer to consult ECG and EKG specialists thereby

increasing the cost of medical care to the patient. So we look at means of eliminating the

human element by analysing various findings and diagnosis algorithms for heart related

problems as would be diagnosed utilizing an electronic stethoscope.

Thus with the new methods being developed, which give us a different perspective

on the cardiac system using heart sounds as a potential parameter for diagnosing

Heart problems.

LITERATURE REVEW


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In the first IEEE paper An Electronic Stethoscope with Diagnosis

Capability(2001), Wah W. Myint discussed aboutthe need for diagnosis algorithms and

the four main heart problems he would be focusing on namely sinus arrhythmia,

tachycardia,bradycardia, aortic stenosis and mitralregurgitation. After a brief introduction

on the said heart diseases Wah W. Myintdiscussed techniques to eliminate noisewhile pre

processing, differentiating betweenheart sounds S1 and S2 and finding

their time periods n1 and n3 which would be instrumental in the algorithm he describes.

To conclude Wah W. Myints work provides us the foundation on which we start

upon an unique algorithm.

Fig2.1: Plots Showing Heart Sound before and after removal of ambient noise.

Whereas in the second paper Samuel E. Schmidt in Noise and the detection

of coronary artery disease with an electronic stethoscope(2010), focuses mainly on

the importanceof a large data set and the types of noise encountered when acquiring heart

beat sounds through and electronicstethoscope and classified them as:

Ambient noise.

Recording noise.


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Respiration noise.

Abdominal sounds.

He talks about how noise contamination of heart sound recordings is a

widespread problem when recordings are collected with an electronic stethoscope in a

clinical environment and goes about with the design of a High pass filter based on AR

model. Samuel E. Schmidts techniques for eliminating noises present in the envelope will

form an integral part of pre-processing so as to suppress noise.

In the third paper Haibin Wang is the one who really gets down to the

implementation in his paper Heart Sound Measurement and Analysis System with

Digital Stethoscope(2009) where utilizing a traditionally built electronic stethoscope he

describes the extraction of heart sound variables S1, S2 from normal patients and S1,S2,S3


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and S4 from heart sounds of around 40 abnormal patients. He then summarizes his entire

diagnostic system in the following figure.

To conclude, the above are the substantial works of the respective authors which

adds weight to our project.


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CHAPTER 3

PROJECT PLANNING


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DIAGNOSTICSTETHOSCOPE PROJECTPLANNING


ACTIVITIES AND GANTT CHARTWith the help of an appropriate tool, the phases of the project was planned out along with

resource management. It is represented as:

Fig3.1:A list of tasks planned and carried out during the course of the project


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The above planned data was interpreted with the help of a gantt chart as follows:

Fig3.2: Gantt chart of the mentioned tasks

And the management of resources were planned as follows:

Fig3.3: Resources Chart


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MILESTONES

With the project being planned into particular phases and the plan being carried out, it was

necessary for the success of each phase that we placed milestones. Ones that would mark

the end of a phase of the project and the beginning of another, every milestone is significant

to a particular block in the block diagram.

The projected milestones are:

Fig3.4: Milestone Chart

REALIZATION OF SENSORCIRCUIT

ALGORITHM FOR HEARTRATE CALCULATION

ALGORITHM FOR MURMURDETECTION

HARDWARE ANDALGORITHM INTEGRATION

WORKING PROTOTYPE


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DIAGNOSTICSTETHOSCOPE HEARTSOUNDRECORDINGSYSTEM


The heart sounds acquiring system, as shown in Fig.4.1, is composed of a traditional

chest piece i.e either a microphone or a piezo sensor and amplifier IC Circuit. While

auscultation heart sounds, you can also hear in the same time.

PIEZO PULSE SENSORA piezoelectric sensor is a device that uses thepiezoelectric effect, to measure changes

inpressure,acceleration,strain orforceby converting them to anelectrical charge.

Piezoelectric sensors have proven to be versatile tools for the measurement of variousprocesses. They are used for quality assurance,process control and for research and

development in many industries. A piezoelectric transducer has very high DCoutput

impedance and can be modelled as a proportionalvoltage source andfilter network.The

voltage Vat the source is directly proportional to the applied force, pressure, or strain. The

output signal is then related to this mechanical force as if it had passed through the

equivalent circuit.
http://en.wikipedia.org/wiki/Piezoelectric_effecthttp://en.wikipedia.org/wiki/Pressurehttp://en.wikipedia.org/wiki/Accelerationhttp://en.wikipedia.org/wiki/Strain_(materials_science)http://en.wikipedia.org/wiki/Forcehttp://en.wikipedia.org/wiki/Electricityhttp://en.wikipedia.org/wiki/Quality_assurancehttp://en.wikipedia.org/wiki/Process_controlhttp://en.wikipedia.org/wiki/Output_impedancehttp://en.wikipedia.org/wiki/Output_impedancehttp://en.wikipedia.org/wiki/Voltage_sourcehttp://en.wikipedia.org/wiki/Electronic_filterhttp://en.wikipedia.org/wiki/Electronic_filterhttp://en.wikipedia.org/wiki/Voltage_sourcehttp://en.wikipedia.org/wiki/Output_impedancehttp://en.wikipedia.org/wiki/Output_impedancehttp://en.wikipedia.org/wiki/Process_controlhttp://en.wikipedia.org/wiki/Quality_assurancehttp://en.wikipedia.org/wiki/Electricityhttp://en.wikipedia.org/wiki/Forcehttp://en.wikipedia.org/wiki/Strain_(materials_science)http://en.wikipedia.org/wiki/Accelerationhttp://en.wikipedia.org/wiki/Pressurehttp://en.wikipedia.org/wiki/Piezoelectric_effect


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Fig4.2:Equivalent circuit of piezo sensor

For use as a sensor, the flat region of the frequency response plot is typically used, between

the high-pass cut-off and the resonant peak. The load and leakage resistance need to be

large enough that low frequencies of interest are not lost.

Fig4.3:A typical piezo sensor


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Fig4.4: Frequency response of piezo sensor

A simplified equivalent circuit model can be used in this region, in which Csrepresents the

capacitance of the sensor surface itself, determined by the standardformula for capacitance

of parallel plates. It can also be modelled as a charge source in parallel with the source

capacitance, with the charge directly proportional to the applied force.

MICROPHONE

A microphoneis an acoustic-to-electrictransducer orsensor that convertssound in air

into anelectrical signal. Microphones are used in many applications such

astelephones,tape recorders,live and recordedaudio engineering. Most microphones

today useelectromagnetic induction (dynamic microphone), capacitance change

(condenser microphone) orpiezoelectric generation to produce an electrical signal from air

pressure variations. Microphones typically need to be connected to apreamplifierbefore

the signal can be amplified with anaudio power amplifier or recorded.

Fig4.5: General microphone schematic
http://en.wikipedia.org/wiki/Capacitance#Capacitorshttp://en.wikipedia.org/wiki/Capacitance#Capacitorshttp://en.wikipedia.org/wiki/Transducerhttp://en.wikipedia.org/wiki/Sensorhttp://en.wikipedia.org/wiki/Soundhttp://en.wikipedia.org/wiki/Electrical_signalhttp://en.wikipedia.org/wiki/Telephonehttp://en.wikipedia.org/wiki/Tape_recorderhttp://en.wikipedia.org/wiki/Audio_engineeringhttp://en.wikipedia.org/wiki/Electromagnetic_inductionhttp://en.wikipedia.org/wiki/Piezoelectricityhttp://en.wikipedia.org/wiki/Preamplifierhttp://en.wikipedia.org/wiki/Audio_power_amplifierhttp://en.wikipedia.org/wiki/Audio_power_amplifierhttp://en.wikipedia.org/wiki/Preamplifierhttp://en.wikipedia.org/wiki/Piezoelectricityhttp://en.wikipedia.org/wiki/Electromagnetic_inductionhttp://en.wikipedia.org/wiki/Audio_engineeringhttp://en.wikipedia.org/wiki/Tape_recorderhttp://en.wikipedia.org/wiki/Telephonehttp://en.wikipedia.org/wiki/Electrical_signalhttp://en.wikipedia.org/wiki/Soundhttp://en.wikipedia.org/wiki/Sensorhttp://en.wikipedia.org/wiki/Transducerhttp://en.wikipedia.org/wiki/Capacitance#Capacitorshttp://en.wikipedia.org/wiki/Capacitance#Capacitors


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LM386 LOW VOLTAGE AUDIO AMP

The LM386 is a power amplifier designed for use in low voltage consumer

applications. The gain is internally set to 20 to keep external part count low, but the addition

of an external resistor and capacitor between pins 1 and 8 will increase the gain to any

value from 20 to 200. The inputs are ground referenced while the output automatically

biases to one-half the supply voltage. The quiescent power drain is only 24 milliwatts when

operating from a 6 volt supply, making the LM386 ideal for battery operation.

Fig4.6: LM386 Pin Diagram


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TL072 LOW NOISE DUAL OP AMP

The JFET-input operational amplifiers in the TL07x series are similar to the TL08x series,

with low input bias and offset currents and fast slew rate. The low Ranges harmonic

distortion and low noise make the TL07x series ideally suited for high-fidelity and audio

preamplifier applications. Each amplifier features JFET inputs (for high input impedance)

coupled with bipolar output stages integrated on a single monolithic chip.

NOTABLE FEATURES:

Low Noise

Low Power Consumption

High Slew Rate

Fig4.7: TL072 Pin Diagram


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CHAPTER 5

HEART SOUND

ANALYTICAL SYSTEM


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DIAGNOSTICSTETHOSCOPE HEARTSOUNDANALYTICALSYSTEM


HEART RATEHeart rate, also commonly known as pulse rate, is the number of times your heart beats

per minute. A normal heart rate depends on the individual, with age, body size, fitness level,

heart conditions, whether youre sitting or standing, medication and even air temperature.

Emotions can also have an impact on heart rate, as heart rate goes up when danger is detected or

other stress factors are experienced.

Some of the common factors that affect heart rate are:

Air temperature:When temperatures (and the humidity) soar, the heart pumps a little

more blood, so your pulse rate may increase, but usually no more than five to 10 beats a

minute.

Body position:Resting, sitting or standing, your pulse is usually the same. Sometimes as

you stand for the first 15 to 20 seconds, your pulse may go up a little bit, but after a

couple of minutes it should settle down. Emotions: If youre stressed, anxious or

extraordinarily happy or sad your emotions can raise your pulse.

Body size:Body size usually doesnt usually change pulse. If youre very obese, you

might see a higher resting pulse than normal, but usually not more than 100.

Medication use: Meds that block your adrenaline (beta blockers) tend to slow your

pulse, while too much thyroid medication or too high of a dosage will raise it.

Experts suggest that you should sit quietly for at least 10 minutes before taking your resting heart

rate.

Resting heart rate

For adults 18 and older, a normal resting heart rate is between 60 and 100 beats per minute

(bpm), depending on the persons physical condition. For children ages 6 to 15, the normal

resting heart rate is between 70 and 100 bpm.

Athletes and those in excellent physical condition can have resting heat rate of 40 bpm.


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Maximum heart rate

While there is no definitive medical advice on when a resting heart rate is too high, most medical

experts agree that a consistent heart rate in the upper levels can put too much stress on the heart

and other organs.

The two most common maximum heart rate calculations are:

220 - Age. For a 50-year-old person, for example: 220 - 50 = 170.

206.9 - (0.67 x Age). For a 50-year-old: 0.67 x 50 = 33.5, and 206.9 - 33.5 = 173.4.

The second is slightly more precise that the first, but the first is easier and more convenient for

most people to remember.

Target heart rate

You gain the most benefits and lessen the risks of cardiac disease when you exercise in your

target heart rate zone. According to the Centre for Disease Control and Prevention, for

moderate-intensity physical activity, a person's target heart rate should be 50 percent to 70

percent of his or her maximum heart rate. For example, using the results calculated above for a

50-year-old person, 50 percent and 70 percent levels would be:

50 percent level: 170 x 0.50 = 85 bpm


For intense exercise, a 50-year-old person's target heart rate should be 70 percent to 85 percent of

his or her maximum heart rate:



It is not recommended to exercise above 85 percent of your maximum heart rate, as this doesnt

typically provide any further benefits and increases cardiovascular and orthopaedic risks.


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Lowering a rapid heart rate

Regular exercise is the tried-and-true method to lowering your resting pulse rate, as people in

good physical condition generally have lower pulse rates. Even people who are fit can

experience spikes in their pulse, which can cause a feeling of faintness.

Pulse rates can spike do to nervousness, stress, dehydration and over exertion. Sitting down and

taking slow, deep breaths can generally lower your heart rate.

Arrhythmia, tachycardia and other conditions

A number of conditions can impact your heart rate. An arrhythmia causes theheart to beat too

fast, too slow or with an irregular rhythm.

Tachycardia is generally considered to be a resting heart rate of over 100 beats per minute and

generally caused when electrical signals in the heart's upper chambers fire abnormally. If the

heart rate is closer to 150 bpm or higher, it is a condition known as supraventricular

tachycardia (SVT).In SVT, your hearts electrical system, which controls the heart rate, is out

of whack. This generally requires medical attention.

Bradycardia is a condition where the heart rate is too low, typically less than 60 bpm. This can be

the result of problems with the sinoatrial node, which acts as the pacemaker, or damage to the

heart as a result of a heart attack or cardiovascular disease.

High blood pressure vs. high heart rate

Some people confuse high blood pressure with a high heart rate. Blood pressure is the

measurement of the force of the blood against the walls of arteries, while pulse rate is the number

of times your heart beats per minute.

There is no direct correlation between the two, and high blood pressure does not necessarily

result in a high pulse rate, and vice versa. Heart rate goes up during strenuous activity, but a

vigorous workout may only modestly increase blood pressure.
http://www.livescience.com/34655-human-heart.htmlhttp://www.livescience.com/34655-human-heart.html


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HEART MURMERS

Heart soundsare thenoises generated by the beatingheart and the resultant flow of blood

through it. Specifically, the sounds reflect the turbulence created when theheart valves snap

shut. In cardiacauscultation,an examiner may use astethoscope to listen for these unique anddistinct sounds that provide importantauditory data regarding the condition of the heart.

In healthy adults, there are two normal heart sounds often described as a luband a dub(or dup),

that occur in sequence with each heartbeat. These are the first heart sound(S1) and second

heart sound(S2), produced by the closing of theAV valves andsemilunar valves,respectively.

In addition to these normal sounds, a variety of other sounds may be present includingheart

murmurs,adventitious sounds,andgallop rhythmsS3andS4

Disease of the cardiac valves and other cardiac structures frequently result in abnormal turbulent

blood flow within the heart causing murmurs. Careful auscultation of heart murmurs is an

extremely valuable tool in the diagnosis of many cardiac conditions. Heart murmurs will be

discussed below. Heart sounds are discussed elsewhere.

When normal laminar blood flow within the heart is disrupted, an audible sound is created by

turbulent blood flow. Outside of the heart audible turbulence is referred to as a bruit, while inside

the heart it is called a murmur. A pictorial representation of systolic and diastolic murmurs are

below:

There are four major causes of cardiac murmurs.

First, if blood is forced through a tight area, turbulent blood flow ensues. This is the case in

valvular stenosis. As a general rule, the worse the stenosis, the louder the murmur, however if

heart failure develops, adequate pressures to create turbulent blood flow may not be able to be
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achieved and the murmur may lessen or even disappear. Thus, the intensity of a murmur is not

used to indicate severity of disease.

A second cause of a murmur is valvular insufficiency in which blood abnormally travels

backward through an incompetent valve causing turbulence when it meets normal, forward bloodflow.

If blood is forced through a congenital anomaly from one chamber to another, as in an atrial

septal defect (ASD) or ventricular septal defect (VSD), a murmur is produced again due to

turbulence.

Yet another cause of cardiac murmurs is increased flow of blood through a normal valve. In high

output states such as anaemia, thyrotoxicosis, or sepsis, a large amount of volume is passing

through the cardiac valves and the normal laminar blood flow may be disturbed. Still's murmur is

a normal aortic flow murmur frequently heard in childhood. This frequently disappears over

time.

Murmurs are described by their timing in the cardiac cycle, intensity, shape, pitch, location,

radiation, and response to dynamic manoeuvres. Using the above, a clinician can accurately

characterize the nature of a murmur and communicate their findings in a precise manner.

Describing Heart Murmurs

Timing

The timing of a murmur is crucial to accurate diagnosis. A murmur is either systolic, diastolic, or

continuous throughout systole and diastole. Remember that systole occurs between the S1 and S2

heart sounds while diastole occurs between S2 and S1.


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Once it is determined if the murmur is systolic or diastolic, the timing of the murmur within

systole or diastole also becomes important when characterizing murmur. Systolic murmurs can

be classified as either midsystolic (a.k.a. systolic ejection murmurs or SEM), holosystolic

(pansystolic), or late systolic. A midsystolic murmur begins just after the S1 heart sound and

terminates just before the P2 heart sound, so S1 and S2 will be distinctly audible. Conversely, a

holosystolic murmur begins with or immediately after the S1 heart sound and extends up to the

S2 making them difficult, if not impossible to hear. A mid-late systolic murmur begins

significantly after S1 and may or may not extend up to the S2.


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WAVELET TRANSFORMFourier transform based spectral analysis is the dominant analytical tool for frequency domain

analysis. However, Fourier transform cannot provide any information of the spectrum changes

with respect to time. Fourier transform assumes the signal is stationary, but PD signal is always

non-stationary. To overcome this deficiency, a modified method-short time Fourier transform

allows to represent the signal in both time and frequency domain through time windowing

function. The window length determines a constant time and frequency resolution. Thus, a shorter

time windowing is used in order to capture the transient behavior of a signal; we sacrifice the

frequency resolution. The nature of the real Partial discharge signals is nonperiodic and transient;

such signals cannot easily be analyzed by conventional transforms. So, an alternative mathematical

tool- wavelet transform must be selected to extract the relevant time-amplitude information from

a signal. In the meantime, we can improve the signal to noise ratio based on prior knowledge of

the signal characteristics.

In this work, we state only some keys equations and concepts of wavelet transform. A continuous-

time wavelet transform of (t)is defined as:

1

t bCWTf (a,b)=Wf(b,a)=a (t)

*( ) dt (1)2

a

Here a, bR,a 0 and they are dilating and translating coefficients, respectively. The asterisk

denotes a complex conjugate. This multiplication of a2is for energy normalization purposes so

that the transformed signal will have the same energy at every scale. The analysis function (t) ,

the so-called mother wavelet, is scaled by a, so a wavelet analysis is often called a time-scale

analysis rather than a time-frequency analysis. The wavelet transform decomposes the signal into

different scales with different levels of resolution by dilating a single prototype function, the

mother wavelet. Furthermore, a mother wavelet has to satisfy that it has a zero net area, which

suggest that the transformation kernel of the wavelet transform is a compactly support function

(localized in time), thereby offering the potential to capture the PD spikes which normally occur

in a short period of time.


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NOISE SUPRESSION

The general wavelet denosing procedure is as follows:

Apply wavelet transform to the noisy signal to produce the noisy wavelet

coefficients to the level which we can properly distinguish the PD occurrence.

Select appropriate threshold limit at each level and threshold method (hard or soft

thresholding) to best remove the noises.

Inverse wavelet transform of the thresholded wavelet coefficients to obtain a

denoised signal.

Wavelet selection

To best characterize the PD spikes in a noisy signal, such as Fig 18, and Fig 20, we

should select our mother wavelet carefully to better approximate and capture the

transient spikes of the original signal. Mother wavelet will not only determine how well

we estimate the original signal in terms of the shape of the PD spikes, but also, it will

affect the frequency spectrum of the denoised signal. The choice of mother wavelet can

be based on eyeball inspection of the PD spikes, or it can be selected based on correlation

betweenthe signal of interest and the wavelet-denoised signal, or based on the

cumulative energy over some interval where PD spikes occur.

Where X and Y are the mean value of set X and Y , respectively.

Where E is the energy and X is the signal vector.


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We choose to select the mother wavelet based on the last two methods: correlation

Between two signals and cumulative energy over some interval of PD spike

occurrence.

We found that the two methods give us a very similar outcome.

Threshold limits

Many methods for setting the threshold have been proposed. The most time-

consuming way is to set the threshold limit on a case-by-case basis. The limit is

selected such that satisfactory noise removal is achieved. For a Gaussian noise; if

we apply orthogonal wavelet transform to the noise signal, the transformed signalwill preserve the Gaussian nature of the noise, which the histogram of the noise will

be a symmetrical bell-shaped curve about its mean value. From theory, four times

the standard deviation would cover

99.99% of the noise. Therefore, we could set the threshold be 4.5 times of the

standard deviation of the wavelet-transformed signal to remove the Gaussian noise

in the signal.

We have found that for the fibre optic signals, we could simply apply the standard

Deviation methods, since the signal is mostly white noises however for the PZT

signals, we should set the threshold case-by-case to best denoise the signals.

Two rules are generally used for thresholding the wavelet coefficients (soft/hard

thresholding). Hard thresholding sets zeroes for all wavelet coefficients whose

absolute value is less than the specified threshold limit. It has shown that hard

thresholding provides an improved signal to noise ratio. In this study, we adopt the

hard thresholding method.


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Level of Decomposition

From the previous section, we have known that the wavelet transform is constituted

by different levels. The maximum level to apply the wavelet transform depends on

how many data points contain in a data set, since there is a down-sampling by 2

operation from one level to the next one. In our experience, one factor that affects

the number of 42 Level we can reach to achieve the satisfactory noise removal

results is the signal-to-noise ratio (SNR) in the original signal. Generally, the

measured signals from the PZT sensors have higher SNR than that of the measured

signals from fibre optic sensors. So to process the PZT data, we need more level of

wavelet transform to remove most of its noise. For the fibre optic sensor data, wecould only go up to 4 or 5 level otherwise we would remove much of the PD signal,

therefore the PD spikes wouldnt be captured.


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HILBERT TRANSFORMInmathematics and insignal processing,the Hilbert transformis alinear operator which

takes a function, u(t), and produces a function,H(u)(t), with the samedomain.The Hilbert

transform is named afterDavid Hilbert,who first introduced the operator in order to solve

a special case of theRiemannHilbert problem forholomorphic functions.It is a basic tool

inFourier analysis,and provides a concrete means for realizing theharmonic conjugate of

a given function orFourier series.Furthermore, inharmonic analysis,it is an example of

a singular integral operator, and of aFourier multiplier. The Hilbert transform is also

important in the field of signal processing where it is used to derive theanalytic

representation of a signal u(t).

The Hilbert transform was originally defined forperiodic functions,or equivalently for

functions on thecircle, in which case it is given byconvolution with theHilbert kernel.

More commonly, however, the Hilbert transform refers to a convolution with the Cauchy

kernel,for functions defined on thereal line R(theboundary of theupper half-plane). The

Hilbert transform is closely related to thePaleyWiener theorem,another result relating

holomorphic functions in the upper half-plane andFourier transforms of functions on the

real line.

The Hilbert transform of ucan be thought of as theconvolution of u(t) with the

function h(t) = 1/(t). Because h(t) is notintegrable the integrals defining the convolution

do not converge. Instead, the Hilbert transform is defined using the Cauchy principal

value (denoted here by p.v.) Explicitly, the Hilbert transform of a function (or signal) u(t)

is given by

provided this integral exists as a principal value. This is precisely the convolution

of uwith thetempered distributionp.v. 1/t; Alternatively, by changing variables, the

principal value integral can be written explicitly ) as
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When the Hilbert transform is applied twice in succession to a function u, the result is

negative u:

provided the integrals defining both iterations converge in a suitable sense. In particular,

the inverse transform is H. This fact can most easily be seen by considering the effect of

the Hilbert transform on the Fourier transform of u(t).

ENVELOPE DETECTION USING HILBERT TRANSFORM

Inmathematics andsignal processing, the analytic representationof a real-valued

function or signal facilitates many mathematical manipulations of the signal. The basic

idea is that thenegative frequency components of theFourier transform (orspectrum)of

areal-valued function are superfluous, due to theHermitian symmetry of such a spectrum.

These negative frequency components can be discarded with no loss of information,

provided that one is willing to deal with a complex-valued function instead. That makes

certain attributes of the signal more accessible and facilitates the derivation of modulation

and demodulation techniques, such as single-sideband. As long as the manipulated function

has no negative frequency components (that is, it is still analytic), the conversion from

complex back to real is just a matter of discarding the imaginary part. The analytic

representation is a generalization of thephasor concept while the phasor is restricted to

time-invariant amplitude, phase, and frequency, the analytic signalallows for time-

variable parameters.

The analytic signal can also be expressed in terms ofcomplex polar

coordinates, where:
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Fig5.1: Figure depicting Envelope of a given signal

These functions are respectively called theamplitude envelope andinstantaneous

phase of the signal In the accompanying diagram, the blue curve depicts and

the red curve depicts the corresponding

The time derivative of theunwrapped instantaneous phase is called theinstantaneous

frequency:[2]

The amplitude function, and the instantaneous phase and frequency are in some

applications used to measure and detect local features of the signal. Another application

of the analytic representation of a signal relates to demodulation ofmodulated signals.

The polar coordinates conveniently separate the effects ofamplitude modulation and

phase (or frequency) modulation, and effectively demodulates certain kinds of signals.

The analytic signal can also be represented as:
http://en.wikipedia.org/wiki/Envelope_detectorhttp://en.wikipedia.org/wiki/Instantaneous_phasehttp://en.wikipedia.org/wiki/Instantaneous_phasehttp://en.wikipedia.org/wiki/Phase_wrappinghttp://en.wikipedia.org/wiki/Instantaneous_phase#Instantaneous_frequencyhttp://en.wikipedia.org/wiki/Instantaneous_phase#Instantaneous_frequencyhttp://en.wikipedia.org/wiki/Analytic_signal#cite_note-2http://en.wikipedia.org/wiki/Analytic_signal#cite_note-2http://en.wikipedia.org/wiki/Analytic_signal#cite_note-2http://en.wikipedia.org/wiki/Modulationhttp://en.wikipedia.org/wiki/Amplitude_modulationhttp://en.wikipedia.org/wiki/File:Analytic.svghttp://en.wikipedia.org/wiki/Amplitude_modulationhttp://en.wikipedia.org/wiki/Modulationhttp://en.wikipedia.org/wiki/Analytic_signal#cite_note-2http://en.wikipedia.org/wiki/Instantaneous_phase#Instantaneous_frequencyhttp://en.wikipedia.org/wiki/Instantaneous_phase#Instantaneous_frequencyhttp://en.wikipedia.org/wiki/Phase_wrappinghttp://en.wikipedia.org/wiki/Instantaneous_phasehttp://en.wikipedia.org/wiki/Instantaneous_phasehttp://en.wikipedia.org/wiki/Envelope_detector


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where

is the signal's complex envelope. The complex envelope is not unique; on the contrary, it

is determined by an arbitrary assignment. This concept is often used when dealing

withpassband signals.If is a modulated signal, is usually assigned to be a

carrier frequency. In other cases it is selected to be somewhere in the middle of the

frequency band.
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CHAPTER 6

ANALYTICAL ALGORITHM

IMPLEMENTATION


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DIAGNOSTICSTETHOSCOPE ANALYTICALALGORITHMIMPLEMENTATION


MATLAB

The major aspect of this project as was discussed was the implementation of algorithms for

heart rate detection and murmur detection. There are innumerable number of ways to go

about achieving it namely with the help of microcontrollers, microprocessors, DSP

processors. In such a case it would increase the portability of the device but we regard

this as a future aspect of our prototype, as of know our main aim was to realise the

algorithm effectively if no efficiently hence we utilised MATLAB for the purposes of

signal processing wherein the input from the sensor circuit was given directly to the

microphone port of the PC through which MATLAB derived the input.

As per Wikipedia MATLAB as a tool is defined as amulti-paradigmnumerical

computing environment andfourth-generation programming language.

Fig6.1: MATLAB GUI of the diagnostic stethoscope program designed
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FLOW CHART FOR HEART RATE CALCULATION

READ INPUT SIGNAL AND ITS

SAMPLING RATE Fs

WITH GIVEN Fs CALCULATE TIME

SCALE AND TOTAL TIME

NORMALISE THE OBTAINED

WAVEFORM BY DIVIDING EACH VALUE

SET WINDOW AND UTILSE

FINDPEAKS FUNCTION TO FIND

IF ABOVE

THRESHOLD

LABEL PEAK APPROPRIATELY AS S1/S2

CALCULATE TOTAL PEAKS AND DIVIDE BY TOTAL TIME & * 60


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FLOW CHART FOR MURMUR DETECTION

READ INPUT SIGNAL AND ITS

SAMPLING RATE Fs

WITH GIVEN Fs CALCULATE TIME

SCALE AND TOTAL TIME

NORMALISE THE OBTAINED

WAVEFORM BY DIVIDING EACH VALUE

PERFORM HELBERT TRASNFORM AND FIND

ENVELOPE DETECTED OUTPUT IN FREQ DOMAIN

CONVERT BACK ONLY MAGNITUDE AND

PLOT AND DEFINE TWO THRESHOLDS

IF SATISFIES BOTH

THRESHOLDS

LABEL PEAK APPROPRIATELY AS S3/S4


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CHAPTER 7

PROTOTYPE

&

TESTING


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DIAGNOSTICSTETHOSCOPE PROTOTYPE&TESTING


HARDWARE AND SOFTWARE INTEGRATION

The Sensor circuit is designed to give its output through a 3.5mm audio port/jack so that

it would be convenient enough to provide the inputs through the microphone port of the

pc (or USB PnP Card)

Fig7.1: 3.5mm Audio Jack Fig7.2: 3.5mm Audio Port

Then MATLAB retrieves the signal by the process of recording it for a given duration

and later signal process it.

Fig7.3: Data from Sensor Circuit being recorded


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PROTOTYPE 1

The first prototype sensor was just basically a low voltage amplifier circuit which

employed LM386 heavily and had a constant gain. It also had the option for volume

control (refer chapter 1 for circuit diagram).

Fig7.4: Prototype 1

The piezoelectric sensor in the acoustic sensor needs to be biased in order for proper

operation. In addition, the output of the piezoelectric sensor is on the order of mill volts,

which is relatively small in magnitude. This makes it challenging for the Arduino to detect

changes in sensor output. In order to address both these issues, a bias and amplifier circuit

was designed and implemented to interface the raw sensor output with USB PnP Sound

Card.


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PROTOTYPE 2

The second prototype was the extension of the first prototype where gain control was

introduce by adding a potentiometer between pins 1 and 8. A major aspect added is noise

reduction due to the addition of TL072 amplifier, because of its high CMRR it can rejectnoise easily. (Refer chapter 1 for circuit diagram)

Fig7.5: Prototype 2

Fig7.6: Designed PCB of 2ndPrototype using fritzing app


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Circuit Description:

As per the circuit diagram mentioned in chapter 1 for prototype 2:-

U1a operates as a low-noise microphone preamp. Its gain is only about 3.9 because

the high output impedance of the drain of the FET inside the electrets microphone

causes U1as effective input resistor to be about 12.2K. C2 has a fairly high value

in order to pass very low frequency (about 20 to 30Hz) heartbeat sounds.

U1b operates as a low-noise Sallen and Key, Butterworth low pass- filter with a

cutoff frequency of about 103Hz. R7 and R8 provide a gain of about 1.6 and allow

the use of equal values for C3 and C4 but still producing a sharp Butterworth

response. The roll off rate is 12dB/octave. C3 and C4 can be reduced to 4.7nF to

increase the cutoff frequency to 1 KHz to hear respiratory or mechanical

(automobile engine) sounds.

U5 is a 1/4W Audio power amplifier IC with built-in biasing and inputs that are

referred to ground. It has a gain of 20. It can drive any type of headphones including

low impedance (8 ohms) ones.


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PROTOTYPE 3

The third prototype was designed with durability in mind rather than quality of the signal.

It utilises a microphone rather than piezo sensor to acquire input signals. (Refer chapter 1

for circuit diagram)

Fig7.7: Prototype 3


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TESTING

The entire system being designed is for naught if untested, hence we tested for the

feasibility of the stethoscope on both the levels i.e the hardware level as the software

level. The results below are of test cases of both recorded normal heart sound (namely of

a certain tem member) and that of an abnormal heart sound (from a database set of a

medical research university website).

Fig7.8: MATLAB GUI of the diagnostic stethoscope program designed

Fig 7.8 depicts the opening screen of the matlab GUI program designed and its features

(Refer User Manual in Appendix for more Details.).


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Fig7.9: Data from Sensor Circuit being recorded

Fig7.9 and Fig7.10 are graphical representations of data or heart sound recorded and

analysis of a patient (namely a project teammate) as depicted by the MATLAB GUI

program.

Fig7.10: Envelope detected data and its peaks of teammates recorded heart sound


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Fig7.11: Envelope detected data of a recorded normal heart sound.

Fig7.12: Envelope detected data of a recorded abnormal heart sound.


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CHAPTER 8

CONCLUSION


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DIAGNOSTICSTETHOSCOPE CONCLUSION


CONCLUSION

A MATLAB based platform was developed for computer-aided diagnosis of cardiac

murmurs. Acoustic heart signals are captured and analysed in real-time with visualization

on a PC monitor for reporting. A new algorithm for murmur detection was tested andevaluated and implemented in MATLAB on a Macintosh PC (can be implemented on any

other PCs) the resulting system is capable of aiding diagnosis by detecting murmurs with

reasonable accuracy.

The murmur detection algorithm that performed the best during preliminary testing was a

relatively simple feature. Calculations of the envelope require complex operations like the

Hilbert FT operator hence, would require a lot of effort when and if implemented on a DSP

Processor. However, the results of this project indicate that software part aside the

hardware decides the reliability factor of the circuit.

In the end, the diagnostic stethoscope although a relatively simple tool to build has great

potential as an everyday healthcare product.


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PROJECT OUTCOMES

On the path to realise a device that has the capability to make a lifesaving diagnosis we had

to first map whatever we had assimilated from the curriculum over the entire course and

learn what we could and could not apply. We learned more than anything that it was our

basics that counted most than that of all the complex theories we were aware of, hence we

stuck to the basics and avoided any complex or roundabout methods. In terms of technical

knowledge we had to go beyond what the curriculum gave us, we had to become familiar

with aspects of bio-medical engineering such as PCG, and all theories relevant to the

algorithm for the device.

At first everything appears to be anarchy, until a clear project goal is defined. But this

wasnt the case. It took some time well before we could have a concrete vision for the

project. Once we had that there were little or no deviations, time was managed better

even though we lacked it in the end.

Overall the project outcome was more than favourable, its greatest achievement

according to us would be exposure to an aspect of engineering which we would have

never experienced if we remained in the shackles of the curriculum defined to us.


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FUTURE ENCHANCEMENTS

The diagnostic stethoscope has few major aspects that can be improved upon in the

future, namely:

More reliable and effective sensor circuit, although this would mean increase in its

overall cost.

Increasing the complexity of the algorithm enough so that it can differentiate

between different types of heart related ailments.

Increasing the portability of the device by shifting the algorithm altogether to a

portable battery powered device namely microcontroller, microprocessor, DSP

processor.


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REFRENCES

[1] "Heart Murmur." Wikipedia. Wikimedia Foundation, 12 Sept. 2012. Web.

[2] Delgado-Trejos E et. Al. Digital auscultation analysis for heart murmurdetection.

Annals of Biomedical Engineering 2009;37:337-53.

[3] Thinklabs ds32a+ Electronic Stethoscope. .

[4] Bentley, P. and Nordehn, G. and Coimbra, M. and Mannor, S. The PASCAL

Classifying Heart Sounds Challenge 2011.

.

[5] G. Tzanetakis and P. Cook "Musical Genre Classification of Audio Signals", IEEE

Transactions on Speech and Audio Processing, 10(5), July 2002.

[6] Heart sounds and heart murmurs sepataionby Amina Atbi, Sidi Mouhamed Debbal

and Fadia Meziani

[7] An Electronic Stethoscope with Diagnosis Capability(2001),by Wah W. Myint

[8]Noise and the detection of coronary artery disease with an electronic stetho-

scope(2010),by Samuel E. Schmidt


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APPENDIX


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DIAGNOSTIC STETHOSCOPEUSER MANUAL


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WHAT IS IT?

The Diagnostic Stethoscope has been designed so as to provide the user

information relating to the input heart sound and provide the diagnosis without

the consult of a physician.

FEATURES

Heart Rate Calculator

Murmur Detector


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SYSTEM COMPONENTS

INPUT

9V/0.5mA Battery Dry Cell

Acoustic Amplifier

Sensor

OUTPUT

Speaker (Audio Output)

Audio Jack/Port (Electrical Signal for Analysis)

DIRECTIONS FOR USE

Self Explanatory GUI

CLICK FOR REAL TIME ANALYSIS RECORDED DATA ANALYSIS


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Documents

Diagnostic Stethoscope Project Report