Early Myocardial Infarction Detection

Early Myocardial Infarction Detection

By

Kasturi Joshi

Edward Labrador

(Team # 17)

A Project Report

Presented to

The Faculty of Department of General Engineering

San Jose State University

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science in Engineering

May 2009

Early Myocardial Infarction Detection ii

© 2009

Kasturi Joshi

Edward Labrador

ALL RIGHTS RESERVED

Early Myocardial Infarction Detection iii

APPROVED FOR THE DEPARTMENT OF GENERAL ENGINEERING

Dr. Leonard Wesley

Dr. Mallika Keralapura

Dr. Sudhi Gautam

APPROVED FOR THE UNIVERSITY

Early Myocardial Infarction Detection iv

Abstract

Cardiovascular heart disease, such as myocardial infarction, is the number one leading cause of death in United States. Having the ability to detect the symptoms and the ability to detect the onset of myocardial infarction can greatly decrease the mortality and morbidity of patients. This project report presents the ability of detecting the onset of symptoms of myocardial infarction using electrocardiogram (ECG). The proposed technique for identifying the isoelectric ST-segment of an ECG is by using Biorthogonal Wavelet Transform. The ST-segment is then compared to an isoelectric baseline, using the PQ segment, which determines if there’s a presence of myocardial infarction. An ST-segment that deviates from the baseline by ±1mV is a probable myocardial infarction. Having an ST elevation for more than 5 minutes determines that a myocardial infarction is present and a patient needs to be alerted. A program based on Matlab software was written to perform the identification of myocardial infarction. ECG datasets were gathered from Physiobank’s Automated Teller Machine database. The accuracy of the written code and its ability to detect true positive myocardial infarction was determined using the ROC analysis. The performance of the code showed that it can accurately determine true positives and true negatives in an ECG dataset. The accuracy of this project was proven to approximately 73% from the 54 ECG datasets tested from 5 different physiobank databases.

Early Myocardial Infarction Detection v

ACKNOWLEDGEMENT

We would like to express our gratitude to Prof. Dr. Mallika Keralapura, Dept of Electrical

Engineering, San Jose State University and Dr. Sudhi Gautam for their generous guidance,

encouragement, direction and support in completing this project.

We would like extend our sincere gratitude to Prof. Leonard P. Wesley, Dept. of Computer

Engineering for an opportunity to pursue ENGR 298 course under his guidance, precious

suggestions, and advice.

We would also like to extend our special thanks to the members of our family. Without their

support and encouragement this project would not be complete.

- Kasturi Joshi

- Edward Labrador

Early Myocardial Infarction Detection vi

Table of Contents List of Figures .............................................................................................................................. viii

List of Tables .................................................................................................................................. ix

List of Equations ............................................................................................................................. x

I.Objective ....................................................................................................................................... 1

II.Introduction ................................................................................................................................. 1

III.Anatomy of Heart ....................................................................................................................... 2

IV.Physiology of Heart ................................................................................................................... 5

V.Myocardial Infarction .................................................................................................................. 7

a.Diagnostic Studies to detect Myocardial Infarction ............................................................... 11

i.Blood Analysis ..................................................................................................................... 11

ii.Imaging ............................................................................................................................... 14

iii.Electrical Activity Monitoring ........................................................................................... 16

VI.Electrocardiography ................................................................................................................. 16

a.Measuring ECG ...................................................................................................................... 18

VII.Wavelet Transforms ............................................................................................................... 23

VIII.Introduction to Early Myocardial Infarction Detection System ............................................ 33

a.Background............................................................................................................................. 33

b.Materials and Method ............................................................................................................ 36

i.Database Description .......................................................................................................... 36

ii.Method for ST-elevation detection ..................................................................................... 37

IX.Testing and Verification .......................................................................................................... 47

a.Results..................................................................................................................................... 47

b.Discussion............................................................................................................................... 48

X.Economic Justification .............................................................................................................. 50

a.Executive Summary ................................................................................................................ 50

b.Problem Statement.................................................................................................................. 52

c.Solution and Value Proposition .............................................................................................. 52

d.Market Size ............................................................................................................................. 53

e.Competitors............................................................................................................................. 55

f.Customers ................................................................................................................................ 56

Early Myocardial Infarction Detection vii

g.Cost ......................................................................................................................................... 57

i.Fixed costs ........................................................................................................................... 57

ii.Variable Costs .................................................................................................................... 58

h.Price Point .............................................................................................................................. 59

i.SWOT Assessment ................................................................................................................... 59

j.Investment Capital Requirement ............................................................................................. 60

k.Personnel ................................................................................................................................ 62

l.Business and Revenue Model .................................................................................................. 63

m.Strategic Alliances/Partners .................................................................................................. 64

n.Profit and Loss ....................................................................................................................... 64

i.Demand Assumptions ........................................................................................................... 65

ii.Product Assumptions .......................................................................................................... 65

o.Exit Strategy ........................................................................................................................... 66

XI.Future Directions ..................................................................................................................... 67

XII.Conclusion .............................................................................................................................. 68

XIII.References ............................................................................................................................. 70

Appendix A ................................................................................................................................... 74

Appendix B ................................................................................................................................... 87

Appendix C ................................................................................................................................. 110

Appendix D ................................................................................................................................. 112

Early Myocardial Infarction Detection viii

List of Figures Figure 1: Anatomy of Heart [Source: Heart Information Center (2006)] ...................................... 3 Figure 2: Electrical Conduction System of the Heart ‘Bundle Branch Block’ of Heart [Source: Heart Information Center (2006)] ................................................................................................... 6 Figure 3: Myocardial Infarction [Source: Coronary Artery Disease, 2008] ................................... 8 Figure 4: Resulting zones from Myocardial Infarction [Source: Myocardial Infarction (2009)] .. 9 Figure 5: An ECG with the major peaks and intervals. (Vibes Electrocardiogram, n.d.) ........... 19 Figure 6: Illustrates the cause of deflection of an ECG (O’ Grady, M.R., n.d.) .......................... 19 Figure 7: An Einthoven's triangle with Lead I, II, and III. .......................................................... 20 Figure 8: Axial representation of Lead I, II, III, aVR, aVL, and aVF. (O'Grady, M.R., n.d.) .... 21 Figure 9: Typical ECG waveform [Source: Jouck. P.P.H. (2004)] .............................................. 22 Figure 10: 2.4 Biorthogonal Wavelet and ECG Signal ............................................................... 31 Figure 11: Types of Biorthogonal Wavelets available in Wavelet Toolbox 3.0 of Matlab 7.1 ... 32 Figure 12: Normal ECG waveform on Strip Chart [Source: Barron Jon, 2007] .......................... 34 Figure 13: Dyadic Wavelet Transform of ECG signal [Source: Jouck. P.P.H. (2004)] ............... 38 Figure 14: Biorthogonal Wavelet Transform of ECG Signal from 21 to24 level ......................... 39 Figure 15: Method for ECG Parameters Detection [Source: Tompkins, 2000] ........................... 40 Figure 16: Filter expressed in Direct Form II transposed structure [Source: Matlab7.1R14 Help] ....................................................................................................................................................... 41 Figure 17: Baseline Wander elimination ...................................................................................... 42 Figure 18: R-peak Detection and PQSTJK extraction of ECG wave at level 24 .......................... 43 Figure 19: Shows an intuitive GUI result for an ECG data with no MI detected ......................... 45 Figure 20: Shows an intuitive GUI result for an ECG data with an MI detected. ........................ 45 Figure 21: Flowchart for ST-elevation detection ......................................................................... 46 Figure 22: Receiver Operating Characteristic Curve ................................................................... 49 Figure 23: An estimate of myocardial infarction prevalence in the United States ....................... 53 Figure 24: An estimate of new and recurrent incidence of myocardial infarction in the United States ............................................................................................................................................. 54 Figure 25: The direct and indirect cost of myocardial infarction per year ................................... 54 Figure 26: Initial Investment Requirement of MI Detector device .............................................. 61 Figure 27: Yearly Model of MI Detector device .......................................................................... 61 Figure 28: The quarterly model of SMART Medical Devices ..................................................... 66

Early Myocardial Infarction Detection ix

List of Tables Table 1: Macroscopic & Microscopic Findings of MI [Source: Klatt.E.C, 2008] ....................... 12 Table 2: Typical Amplitudes and Durations for ECG signal [Source: Saritha. et.al. (2008)] ...... 35 Table 3: Testing Results for ST-change detection program ......................................................... 48 Table 4: Data Points for ROC Curve ............................................................................................ 49 Table 5: Fixed cost of MI Detector device ................................................................................... 58 Table 6: Variable cost of MI detector device ................................................................................ 59 Table 7: SWOT assessment of MI Detector medical device ........................................................ 60 Table 8: The break-even table of MI Detector device .................................................................. 62 Table 9: The quarterly model of SMART Medical Devices ......................................................... 66

Early Myocardial Infarction Detection x

List of Equations Equation (1) Fourier Transform .................................................................................................... 24 Equation (2) Short Time Fourier Transform ................................................................................. 25 Equation (3) Mother Wavelet ....................................................................................................... 26 Equation (4) Continuous Wavelet Transform ............................................................................... 26 Equation (5) Complete Equation for Continuous Wavelet Transform ......................................... 26 Equation (6)Scaling and Mother Wavelet Function of Biorthogonal Wavelet ............................. 29 Equation (7)Dual Scaling and Mother Wavelet Function of Biorthogonal Wavelet .................... 30 Equation (8)Frequency Dilation for Biorthogonal Wavelet ......................................................... 30 Equation (9)Frequency Wavelet for Biorthogonal Wavelet ......................................................... 30 Equation (10)Biorthogonal Wavelet Decomposition .................................................................... 30 Equation (11)Signal Filtering Equation ........................................................................................ 41

Early Myocardial Infarction Detection 1

I. Objective

The objective of this project is to create a smart algorithm that can detect the elements of

an electrocardiogram (ECG) and determine if symptoms of myocardial infarction are present.

The algorithm is written in MATLAB, but when coupled with a portable ECG machine, can

provide greater protection against mortality and morbidity associated to myocardial infarction.

II. Introduction

Myocardial Infarction (MI) is commonly referred to as “Heart Attack”. A “heart attack”

is defined by World Health Federation as a condition which “occurs when the heart’s supply of

blood is stopped” (World Health Federation, 2009). It is highly important to understand the

meaning of the words myocardial infarction in order to diagnose the disease. The word

‘myocardial’ means related to the heart muscle and the word ‘infarction’ means tissue death due

to lack of oxygen and ‘myocardial infarction’ means heart muscle or tissue death due to lack of

oxygen. When the heart’s blood supply is restricted, “a sequence of injurious events occur

beginning with subendocardial or transmural ischemia, followed by necrosis, and eventual

fibrosis (scarring) if the supply isn’t restored in an appropriate period of time” (Yanowitz, 2006).

The American heart association defines myocardial infarction as “the damaging or death of an

area of the heart muscle (myocardium) resulting from blocked blood supply to the area; medical

term for a heart attack” (American, 2008).

The need for early diagnosis of myocardial infarction is apparent from the statistics

estimated by American heart association. The United States American Heart Association

estimates 80,700,000 people suffered with some form of cardiovascular symptom, out of which

8,100,000 suffered from myocardial infarction alone. The heart disease and stroke statistics for


the year 2008 by the American Heart Association publishes that there are 600,000 new

incidences of myocardial infarction reported annually and 320,000 recurrent attacks annually.

Hospital stays in 2005 were recorded as 1.8 million in-patients, which amounted to $256 billion

dollars in direct and indirect cost of myocardial infarction. American Heart Association’s

statistics also show that Heart Attacks are still the leading cause of death in the United States of

America.

Myocardial Infarction is not a fatal condition if proper medical help is received at the

right time. MI can be diagnosed by various diagnostic tools like Angiogram, Echocardiogram,

Blood Analysis, Chest X-ray and the oldest and most trusted tool by the doctors ECG or

electrocardiography. Not only is ECG the oldest tool available to monitor the electrical activity

of the heart, it is also the most efficient diagnostic tool, giving speedy diagnosis compared to the

other available tool for monitoring heart activity. Early recognition of symptoms of myocardial

infarction can reduce the morbidity and mortality of patients. The literature shows that

continuous monitoring of heart electrical activity decreases the changes of fatal myocardial

infarction and the project aims at developing an algorithm that can easily be incorporated in the

current available portable and wireless ECG machines to create a ‘standalone’ state of the art

smart medical device for giving an early warning against the imminent myocardial infarction.

III. Anatomy of Heart

It is important to understand the anatomy of the heart in order to understand what

myocardial infarction is and why does it occur. Heart is a muscular organ that supplies blood

through the body. It is located between the lungs in the left side of the sternum. The heart has


four chambers as can be seen in Figure 1 below. The four chambers are Right Atrium, Right

Ventricle, Left Atrium and Left Ventricle.

Figure 1: Anatomy of Heart [Source: Heart Information Center (2006)]

• Right Atrium: This chamber consists of de-oxygenated blood that returns from the body,

this de-oxygenated blood is then passed on to the Right Ventricle through the tricuspid

valve.

• Tricuspid Valve: It is a one-way valve that controls the flow of blood from the Right

Atrium to the Right Ventricle.

• Right Ventricle: It is a chamber that consists of de-oxygenated blood which is passed into

the lungs for oxygenation via the pulmonary valve.

• Pulmonary Valve: It is a one-way valve that controls the flow of blood from Right

Ventricle to the pulmonary arteries.


• Pulmonary Arteries: These arteries supply de-oxygenated blood to the lungs where the

blood gets oxygenated.

• Pulmonary Veins: After the blood passes to the lungs from the pulmonary arteries, the

blood gets oxygenated and flows from the lungs to the pulmonary veins, the pulmonary

veins supply the oxygenated blood from the lungs to the Left Atrium.

• Left Atrium: This is the chamber where the oxygenated blood enters from the pulmonary

vein. The blood from the left atrium is then forced into the left ventricle via the mitral

valve.

• Mitral Valve: It is a one-way valve that controls the flow of blood from the left atrium to

the left ventricle.

• Left Ventricle: The oxygenated blood enters the left ventricle through the mitral valve

and is then forced from the left ventricle into the aorta through the aortic valve.

• Aortic Valve: It is a one-way valve that controls the flow of blood from the left ventricle

to the aorta.

• Aorta: It is largest artery in the body and the aorta branches into smaller arteries. The

aorta carries the oxygenated blood from the heart to the other parts of the body.

The Texas Heart Institute gives some interesting and fun facts about heart and they are

listed below:

• Heart Weighs between 7 and 15 ounces (200 to 425 grams) and is little larger than the

size of the fist

• In a lifetime, the heart expands and contracts 3.5 billion times


• In a day, heart pumps 2,000 gallons (7,571 liters) of blood and the heart beats 100,000

times

IV. Physiology of Heart

With understanding the anatomy of heart, this section would discuss the physiology of

the heart, the electrical conductivity that drives the heart and pumps the blood throughout the

body. The heart is made of cardiac muscle tissue that contracts and relaxes throughout the

lifetime of a person and this contraction and relaxation of the muscles drives the blood from the

heart. The contraction and relaxation of the cardiac muscle is in a rhythm, when the cardiac

muscles of the heart’s ventricles contract, it is called as systole and when the cardiac muscles of

heart’s ventricles relax, it is called as diastole. “A network of nerve fibers coordinates the

contraction and relaxation of the cardiac muscles tissue to obtain an efficient, wave-like pumping

action of the heart” (Cardiovascular Consultants, 2006).

Figure 2 shows the diagram of heart with some of the key elements of labeled that are

necessary in understanding the physiology of heart. Sinoatrial node commonly known as the SA

node is the natural pacemaker of the heart. It triggers an electrical impulse that produces a

heartbeat. The impulse trigger passes through the atria and causes the muscles to contract. The

impulse that travels from the SA node reaches the Atrioventricular node commonly known as the

AV node after the contraction of the atrium muscles. The AV node triggers another pulse which

now causes the ventricles to contract.


Figure 2: Electrical Conduction System of the Heart ‘Bundle Branch Block’ of Heart [Source: Heart Information Center (2006)]

The ventricular contraction is brought about by the bundle of His which receives the

triggering impulse from the AV node. The bundle of His then divides the impulse into the left

bundle branch and the right bundle branch, which in turn contracts the left and right ventricles.

The contraction and the relaxation of the heart muscles thus brought about by the SA and the AV

nodes is wavelike and in rhythm. The rhythmic wavelike activity can be heard by the doctors

using the stethoscope. It can also be imaged using echocardiography that uses the principle of

ultrasound or heart imaging. Electrocardiography is another diagnostic tool for monitoring the

rhythmic electrical activity of the heart. Subsequent sections will introduce the principle behind

electrocardiography along with its advantages and disadvantages.

This rhythmic electrical activity of the heart sometimes is lost and “the electrical impulse

cannot travel throughout the heart because part of the heart’s conduction system is ‘blocked’”

(Heart Information Center, 2006) due build of plaque, cholesterol deposits in the arteries that

supply blood to the heart. This is one of the reasons that lead to the arrhythmic electrical activity

of the heart. There are several ways to diagnose the cause of loss of rhythm in the conduction of


heart. Chest X-ray, Angiogram, echocardiogram and electrocardiogram are some of the

diagnostic tools that aid in defining the cause of blockage of the arteries supplying blood to the

heart. Myocardial Infarction is one such condition that results due to the blockage of the artery

supplying blood to the heart. What is myocardial infarction and how it is caused is discussed in

the next section.

V. Myocardial Infarction

Myocardial Infarction is a type of ischemic heart disease. “Myocardial infarction (MI) is

the irreversible necrosis of heart muscle secondary to prolonged ischemia” (Samer Garas et al.

2008). It is caused due to relative insufficiency of oxygen to the heart muscles called cardiac

muscles. Myocardial Infarction is associated with acute coronary syndrome and “approximately

90% of MIs result from an acute thrombus that obstructs an atherosclerotic coronary artery”

(Samer Garas et al. 2008).

Myocardial Infarction can result due to the following causes:

• “Occlusive intracoronary thrombus - a thrombus overlying an ulcerated or fissured

stenotic plaque causes 90% of transmural acute myocardial infarctions” (Klatt E.C,

2008).

• “Vasospasm - with or without coronary atherosclerosis and possible association with

platelet aggregation” (Klatt E.C, 2008).

• “Emboli - from left sided mural thrombosis, vegetative endocarditis, or paradoxic emboli

from the right side of heart through a patent foramen ovale” (Klatt E.C, 2008).

Narrowing and hardening of heart muscles is process that is known in the medical terms

as ‘Atherosclerosis’. Atherosclerosis when happens in the arteries that supply blood to the heart,


it results in coronary heart diseases. There are various coronary heart diseases and myocardial

infarction is one of the types of coronary heart disease. When the blood supply of the heart

muscles is hampered, it can lead to chest pain called angina and if angina is not treated, it may

result in heart attack. The blood vessels that supply blood to heart are called the coronary

arteries. There are three coronary arteries that supply blood to the heart. These three coronary

arteries supply blood to three different areas of heart muscles cells. Since the area of these heart

muscles cells is known, myocardial infarction “can occur in the anterior, lower, and the lateral

heart territory” (The Myocardial Infarction-Heart Attack, 2006). Over the years cholesterol and

other fatty substances in the blood get deposited on the arterial wall and builds to form a ‘Plaque’

or ‘Atheroma’. The plaque build up obstructs the flow of blood to the heart muscle. This is

depicted in the figure 3 below.

Figure 3: Myocardial Infarction [Source: Coronary Artery Disease, 2008]

The obstruction of blood flow across the coronary artery develops a pain in the chest

called angina. It also develops pain in the arms, around the neck and back of the chest. Necrosis


of heart tissue begins when the plaque bursts and causes a blood clot that blocks the artery

completely. This cuts off the blood supply to an area of the heart. As the supply of oxygen to the

blocked artery stops, the heart muscle cells start to die. The dying heart muscle cells constitute

the zone of infarction as can be seen in figure 4. The area of heart muscle cells surrounding the

zone of infarction is called the zone of injury (as seen in figure 4), the heart muscle cells in this

area do not die but the working of those heart cells is hampered to an extent that the cells are

rendered non-functional. This is called a heart attack or myocardial infarction; it causes

permanent damage to that area of the heart. The zone surrounding the zone of injury is the zone

of ischemia; the heart muscle cells in this region are partially functional. If enough heart muscle

is damaged the heart may beat irregular or may even stop beating altogether.

Figure 4: Resulting zones from Myocardial Infarction [Source: Myocardial Infarction (2009)]

There are patterns that are observed by the cardiologists in prognosis of myocardial

infarction cases, they are as follows:

• “Transmural infarct - involving the entire thickness of the left ventricular wall from

endocardium to epicardium, usually the anterior free wall and posterior free wall and


septum with extension into the RV wall in 15-30%. Isolated infarcts of RV and right

atrium are extremely rare” (Klatt E.C, 2008).

• “Subendocardial infarct - multifocal areas of necrosis confined to the inner 1/3-1/2 of the

left ventricular wall. These do not show the same evolution of changes seen in a

transmural MI” (Klatt E.C, 2008).

When myocardial infarction occurs there is gradual necrosis of heart tissue. The necrosis

of the heart tissue happens over a period of time and may vary with depending on the size of the

infarct. Table 1 gives the pathologic findings in terms of timeline for the necrosis of heart tissue.

It is well known that first myocardial infarction may not be fatal but subsequent infarcts

may prove fatal depending on the damage done by the previous infarcts. This also would change

the timeline for the necrosis of heart tissue and may result in sudden death. “Sudden death is

defined as death occurring within an hour of onset of symptoms” (Klatt E.C, 2008). Some pre-

existing conditions that may prove dangerous for subsequent infarcts are as follows:

• “Arrhythmias and conduction defects, with possible ‘sudden death’” (Klatt E.C, 2008).

• “Extension of infarction, or re-infarction” (Klatt E.C, 2008).

• “Congestive heart failure (pulmonary edema)” (Klatt E.C, 2008).

• “Cardiogenic shock” (Klatt E.C, 2008).

• “Pericarditis” (Klatt E.C, 2008).

• “Mural thrombosis, with possible embolization” (Klatt E.C, 2008).

• “Myocardial wall rupture, with possible tamponade” (Klatt E.C, 2008).

• “Papillary muscle ruptures, with possible valvular insufficiency” (Klatt E.C, 2008).

• “Ventricular aneurysm formation” (Klatt E.C, 2008).


After understanding what and how myocardial infarction occurs, it is now essential to

know how to get myocardial infarction diagnosed. As mentioned already in the introduction

there are several ways for diagnosis of myocardial infarction. The next section will describe a

few different diagnostic tools that diagnose myocardial infarction.

a. Diagnostic Studies to detect Myocardial Infarction

The diagnostic tools for studying myocardial infarction have been divided into three

types and they are as follows:

i. Blood Analysis

During the period of myocardial infarction the dying heart muscle cells release different

types of enzymes called as ‘cardiac enzymes’. These different cardiac enzymes are present in the

bloodstream at different intervals of time. The cardiac enzymes can be seen in the bloodstream as

early as start of the infarct. Blood analysis for these different enzymes can reveal the crucial

information for diagnosis of myocardial infarction.

The different cardiac enzymes are as follows:

• “Troponin” (Samer Garas et al.,2008)

This enzyme plays a major role in diagnosis of myocardial infarction. The reason is

because “Troponin I and T are structural components of cardiac muscle” (Klatt E.C., 2008). The

level of troponins can be found in the bloodstream as early as 3 to 12 hours in myocardial

infarction. The levels of this enzyme will also remain elevated in the bloodstream for up to 2

weeks into myocardial infarction. The reason for troponin not being considered as the only blood

analysis enzyme is because another account of myocardial infarction happening in the time

period of already elevated levels of troponins will go unnoticed and may prove fatal.


Table 1: Macroscopic & Microscopic Findings of MI [Source: Klatt.E.C, 2008]

Time of Onset Gross Morphologic Findings

18-24 Hours Pallor of myocardium

24-72 Hours Pallor with some hyperemia

3-7 Days Hyperemic border with central yellowing

10-21 Days Maximally yellow and soft with vascular margins

7 Weeks White fibrosis

Time of Onset Microscopic Morphologic Finding

1-3 Hours Wavy myocardial fibers

2-3 Hours Staining defect with tetrazolium or basic fuchsin dye

4-12 Hours Coagulation necrosis with loss of cross striations, contraction bands,

edema, hemorrhage, and early neutrophlic infiltrate

18-24 Hours Continuing coagulation necrosis, pyknosis of nuclei, and marginal

contraction bands

24-72 Hours Total loss of nuclei and striations along with heavy nuetrophilic infiltrate

3-7 Days Macrophage and monoclear infiltration begin, fibrovascular response

begins

10-21 Days Fibrovascular response with prominent granulation

7 Weeks Fibrosis


Also, troponin elevations have been found in other forms of conditions like renal failure

and also conditions related to skeletal muscles.

• “Creatine Kinase” (Samer Garas et al.,2008)

This enzyme consists of 3 sub-enzymes. Each type of enzyme is related to a particular

part of the body. “creatine kinase with muscle subunits (CK-MM), which is found mainly in

skeletal muscle; creatine kinase with brain subunits (CK-BB), predominantly found in the brain;

and myocardial muscle creatine kinase (CK-MB), which is found mainly in the heart” (Samer

Garas et al.,2008). Creatine kinase (CK-MB) along with troponin is usually obtained together to

provide better diagnosis of myocardial infarction. The sensitivity of creatine kinase is

approximately 95%.

• “Myglobin” (Samer Garas et al.,2008)

This is a type of protein that is found in both skeletal as well as cardiac muscles. The

function of myoglobin is to bind oxygen. This makes the identifying level of myoglobin in the

bloodstream important as it would help in determining the amount of injury made to the heart

muscle in myocardial infarction. Rise in the level of myoglobin is present even before creatine

kinase-MB, but the rise may or may not be related to myocardial infarction alone.

• “Lactate Dehydrogenase” (Klatt E.C., 2008)

This enzyme like creatine kinase consists of 5 different enzymes and like all the other

enzymes does not help in diagnosis of myocardial infarction alone. When used in conjunction

with the other types of enzymes, the blood analysis for these enzymes gives excellent myocardial


infarction diagnosis. “It begins to rise in 12 to 24 hours following MI, and peaks in 2 to 3 days,

gradually dissipating in 5 to 14 days” (Klatt E.C., 2008).

There are other blood analysis tests that are done as part of diagnosis for myocardial

infarction and they are as follows:

• Complete blood cell count

• Chemistry Profile

• Lipid level Profile

• C-reactive Protein (CRP)

These above mentioned blood analysis types are not specifically related in diagnosis of

myocardial infarction alone and are utilized as diagnostic methods for other types of conditions

and diseases as well.

ii. Imaging

There are several Imaging diagnostic tools available for diagnosis of myocardial

infarction. The imaging diagnostic tools are as follows:

• “Chest Radiography” (Samer Garas et al.,2008)

Chest Radiography is another word for Chest X-ray. This imaging technique does not

provide results that are specific to myocardial infarction detection, but is usually done as one of

the first step towards assessing any patient admitted in an emergency room with a heart

condition. A chest Radiograph is used for assessing the size of the heart and also conditions such

as pneumonia that might be one of the reasons for certain types of heart conditions. The details


revealed in a chest radiogram are mostly anatomic and macroscopic, for microscopic details

other imaging tools like angiogram proves helpful.

• “Echocardiography” (Samer Garas et al.,2008)

Echocardiogram is an excellent imaging tool for cardiologists. It provides “the extent of

the infarction and assesses overall left ventricle (LV) and right ventricle (RV) function” (Samer

Garas et al., 2008). It also helps in diagnosis of different complications of the heart valves.

Echocardiography is an imaging tool is generally required by the physicians when all the other

tests are questionable or inconclusive.

• “Myocardial Perfusion Imaging” (Samer Garas et al.,2008)

This imaging tool is generally used for patients with after heart attack to assess the

damage done to the heart muscle tissue by previous infarctions. This tool is also used for

prognosis of patients entering the emergency room with serious heart conditions.

• “Cardiac Angiography” (Samer Garas et al.,2008)

Cardiac Angiography more commonly known as angiogram is a technique where a radio

opaque dye is inserted in the blood stream via the femoral artery and then a chest X-ray of the

patient is obtained to diagnose blockages in the coronary arteries. This type of imaging technique

has become very common in the recent years. The process through which is radio opaque dye is

inserted is called cardiac catherization. It is an imaging technique which is minimally invasive

and requires local anesthesia.


iii. Electrical Activity Monitoring

Probably the most reliable and oldest available tool for measuring electrical activity of

the heart is Electrocardiography or more commonly known as ECG. ECG is considered as the

first diagnostic tool when evaluating patients with suspected Myocardial Infarction. It is

confirmatory of the diagnosis in approximately 80% of cases.

Electrocardiography when used as a diagnostic tool in myocardial infarction can yield the

following prognosis:

• To rule out the Right Ventricular infarct, ECG recording on the right-sided setting for

patients with inferior Myocardial Infarction (Samer Garas et al., 2008).

• Obtain daily serial ECGs for the first 2-3 days and additionally as needed (Samer Garas

et al., 2008).

• Convex ST-segment elevation with upright or inverted T waves is generally indicative of

MI in the appropriate clinical setting (Samer Garas et al., 2008).

• ST depression and T-wave changes may also indicate evolution of NSTEMI (Samer

Garas et al., 2008).

A more detailed description of what electrocardiography is and how it is used as a

diagnostic tool for monitoring electrical activity of the heart is given in the following section.

VI. Electrocardiography

Patients suffering from S-T elevated myocardial infarction can be diagnosed with

several type of diagnostic methodology. One of which is by using electrocardiography. The

recorded trace of electrocardiography is called an electrocardiogram (ECG).


Electrocardiography is a non-invasive diagnostic procedure that records the electrical current

transmitted by the heart all over the body. The electrical current can be picked up by an

electrical sensing device, which is attached to an ECG machine. The electrical sensing devices

are strategically placed on the body surface to detect heart impulses. They can be placed in the

arms and legs. The recorded ECG is the representation of the depolarization and re-polarization

of the heart and can diagnose a patient by looking at the characteristics of the traced ECG

readings (Klabunde, R.E., 2007).

Every beating of the heart, an electrical impulse is generated and transmitted to the

myocardium which causes the pumping action of the heart and provides blood throughout the

body. There are 3 main deflections in an ECG: the P-wave, the QRS complex, and the T-wave.

The P-wave records the electrical activity of the atria. It starts from the SA node, which

generates an impulse. It causes an excitation to the atria and I then picked up by the AV node.

The P-wave usually lasts for about 0.8 to 0.1 seconds (80 to 100 ms). The impulse travels from

the AV node to the Bundle of His, which generates an isoelectric pattern in the ECG.

A trace between the onset of the P-wave to the onset of the QRS complex is called the P-R

interval. It is the representation of the onset atrial depolarization and the onset of ventricular

depolarization. The P-R interval has a period of 0.12 to 0.20 seconds (120 to 200 ms).

The next main deflection of an ECG is the QRS complex. It represents the ventricular

depolarization. The impulse travels from the Bundle of His to the ventricular walls through the

left and right bundle branches. The impulse causes contraction of the ventricular walls, which

causes the blood to be pumped out to the lungs and body. The QRS complex has a short


duration, usually lasts for 0.06 to 0.1 seconds (60 to 100 ms). The short duration indicates that

the ventricular depolarization happens in a relatively quick time.

After the QRS complex, an isoelectric line called an S-T segment occurs, at which, there

is a complete depolarization in the ventricles. The isoelectric line of the S-T segment is very

important in diagnosing heart conditions in a patient, since a depressed or elevated S-T segment

represents a cardiac ischemia.

The last major deflection in an ECG is the T-wave. The T-wave represents the re-

polarization of the ventricles. It has a longer trace in the ECG reading. It is sometime followed

by a U-wave, which represents the remainder of the ventricular re-polarization.

The trace between the ventricular depolarization and re-polarization is called the Q-T

interval. The range of the Q-T interval is between 0.2 and 0.4 seconds (200 and 400 ms). The

duration of the Q-T interval is important in detecting certain types of tachyarrhythmias.

a. Measuring ECG

Electrical sensing devices or electrodes are placed strategically on top of the body to

detect the electrical activity of the heart and diagnose patients with different heart anomalies. In

addition, the recorded trace of the ECG is not always recorded as shown in figure 5. The trace

depends on the position of the lead.

The leads are placed on the body can be described as a positive lead and a negative lead.

The electrical impulse that is generated in the heart travels in parallel to the direction of the lead.

If the direction moves toward the positive lead, then a positive deflection takes place, on the

other hand, if the direction of the impulse moves toward the negative lead, then a negative

deflection takes place. This action is illustrated in figure 6.

Figure 5: An ECG with the major peaks and intervals. (Vibes Electrocardiogram, n.d.)

Figure 6: Illustrates the cause of deflection of an ECG (O’ Grady, M.R., n.d.)


An ECG with the major peaks and intervals. (Vibes Electrocardiogram, n.d.)

Illustrates the cause of deflection of an ECG (O’ Grady, M.R., n.d.)


An ECG with the major peaks and intervals. (Vibes Electrocardiogram, n.d.)

Illustrates the cause of deflection of an ECG (O’ Grady, M.R., n.d.)


Electrodes are placed in the arms and legs, which are called the Einthoven's triangle. The

Einthoven’s triangle, shown in figure 7, is composed of Leads I, II, and III, which are the basic

electrodes of the 12 lead ECG system.

Figure 7: An Einthoven's triangle with Lead I, II, and III.

There are 3 additional leads that are developed from leads I, II, and III. They are called

the augmented limb leads, aVR, aVL, and aVF. The augmented limb leads views the heart in

different vectors compared to the original leads. It is the recording between one limb and two

other limbs. aVR, augmented vector right, is a recording between the positive lead in the right

arm and a combination of negative leads in the left arm and left leg. aVL, augmented vector left,

is between the positive lead of the left arm and combination of negative leads in the right arm

and left leg. aVF, augmented vector foot, is between the positive lead of the left foot and

combination of right arm and left arm.

Leads I, II, III, aVR, aVL, and aVF represents the hexial reference system which

determines the electrical axis of the heart. All leads are in the frontal plane axis as shown in

figure 8.

Lead I

Lead II Lead III

_

+

_

_

+ +

Figure 8: Axial representation of Lead I, II, III, aVR, aVL, and aVF. (O'Grady, M.R., n.d.)

Precordial leads, V1, V2, V3, V4, V5, and V6, are also called

are strategically placed on the chest overlying the heart or its vicinity. Unlike leads I, II, III, aVR,

aVL and aVF, the precordial leads records ECG in the horizontal plane. The precordial leads are

V1, V2, and V3 are called the right precordial leads; while V4, V5, and V6 are called left

precordial leads and all precordial leads are unipolar, which means it can be a positive lead or a

negative lead. The precordial leads provide different view of the electrical activity of the

and they are very useful in identifying the P

The ECG waveform follows a pattern that describes the physiological meaning of the

conduction of heart. Figure 9 shows such a typical waveform. The description for the typical

waveform with its analogous conduction activity is as follows:


Axial representation of Lead I, II, III, aVR, aVL, and aVF. (O'Grady, M.R., n.d.)

Precordial leads, V1, V2, V3, V4, V5, and V6, are also called chest leads because they



e right precordial leads; while V4, V5, and V6 are called left


negative lead. The precordial leads provide different view of the electrical activity of the

and they are very useful in identifying the P-wave of an ECG recording.



veform with its analogous conduction activity is as follows:


Axial representation of Lead I, II, III, aVR, aVL, and aVF. (O'Grady, M.R., n.d.)

chest leads because they



e right precordial leads; while V4, V5, and V6 are called left


negative lead. The precordial leads provide different view of the electrical activity of the heart




• “The first deflection, termed the P-wave is due to the depolarization of the atria”

(Jouck. P.P.H., 2004).

• “The large QRS-complex is due to the depolarization of the ventricles. This is the

complex with the highest amplitude” (Jouck. P.P.H., 2004).

• “The last and most significant part for this report is the T-wave. It corresponds to

the ventricular repolarization of the heart” (Jouck. P.P.H., 2004).

Figure 9: Typical ECG waveform [Source: Jouck. P.P.H. (2004)]


VII. Wavelet Transforms

Signal transformation aides in converting signal from Time domain to Frequency domain.

It is of very importance to be able convert the signal from time domain to frequency domain to

get the complete information carried by the signal. There are many different mathematical

transformations available for converting time domain signal to frequency domain and vice a

versa. Typically physiological signals are present in time domain i.e. the signal has a time value

and the amplitude value. What is also hidden in this type of signal is its frequency value. When

the time domain signal is converted in frequency domain, the hidden frequency values of the

signal can be found out. It is important to know the frequency component of a signal to

completely define and/or analyze the signal. Hence, mathematical signal transformation plays a

crucial role in signal analysis and signal representation that are the key elements in determining

the ECG signal and hence important for this project.

There are several mathematical transformation tools available, hence choosing the

transformation tool that best suites the signal that is under measurement is of high importance.

Some of the mathematical signal transformation tools are listed below:

• Fourier Transform

• Short Time Fourier Transform

• Wigner Distribution

• Laplace Transform

• Z-Transform

• Wavelet Transform

• Fast Fourier Transform

• Hilbert Transform


• Radon Transform

• Linear Canonical Transform

The list of transform above is no way a complete list; there are several other

mathematical transformations available. Choice of transform for a particular type of application

is critical. There are certain conditions based on which the choice of the transform to be used for

a particular application is based on. The conditions were crucial in deciding type of transform to

be used for ECG signal processing were as follows:

• Type of application

• Type of signal, stationary or non-stationary

The choice of transform made for this project was Biorthogonal Wavelet Transform. The

reason for choosing this type of mathematical signal transformation was done methodologically.

For most of the engineering applications, Fourier transform is popular (Polikar Robi, 2001).

Fourier transforms can provide signal representation either in time domain or the frequency

domain. The equation 1 for Fourier Transform is given as follows:

�� ………………………..(1)

What Fourier transforms fails to provide is the time instant at which the frequency value

is present and hence is not a transform of choice for non-stationary signal. Since ECG signal is

non-stationary signal Fourier transform was not a favored choice for doing ECG signal analysis

in this project.

The next suitable mathematical transformation is the Short Time Fourier Transformation.

As the name suggests, short time Fourier transform is similar to Fourier transform but there is a


small difference between then which is “In STFT, the signal is divided into small enough

segments, where these segments (portions) of the signal can be assumed to be stationary”

(Polikar Robi, 2001). The equation 2 for Short Time Fourier Transform is given as follows:

�� ′�� !"#$�#�# ……………… (2)

Short Time Fourier Transform does overcome the limitation of signal representation with

known time instant of frequency. But there is a limitation to using Short Time Fourier Transform

too, which is the segments of the signal called window have fixed width and it may not be useful

for a signal that has multiple frequencies varying rapidly throughout the signal. If the width of

the segment or the window is chosen such that it narrow, then the signal transformation obtained

has good time resolution but poor frequency resolution, on the other hand, if the width of the

window is chosen such that it is wide, then the signal transformation obtained has poor time

resolution but good frequency resolution. For getting both good time resolution as well as good

frequency resolution, the width of the window should be varied across the signal during the

signal transformation such that both time and frequency component of the signal does not get

compromised. Due to this limitation of Short Time Fourier Transform, Wavelet Transform was

originally developed. For this project, the signal under measurement ECG that is non-stationary

signal that has varying frequency levels throughout the signal, and with addition of noise, it

would be difficult to use short time Fourier transform for this project, hence Wavelet Transform

was chosen.

Like short time Fourier transform, wavelet transform are similar “in the sense that the

signal is multiplied with a function, similar to the window function in the STFT, and the

transform is computed separately for different segments of the time-domain signal” (Polikar


Robi, 2001). There are differences in Short Time Fourier Transform and Wavelet Transform

though and they are as follows:

• “The Fourier transforms of the windowed signals are not taken, and therefore single peak

will be seen corresponding to a sinusoid, i.e., negative frequencies are not computed”

(Polikar Robi, 2001).

• “The width of the window is changed as the transform is computed for every single

spectral component, which is probably the most significant characteristic of the wavelet

transform” (Polikar Robi, 2001).

The continuous wavelet transform has a time scaling and a time shifting function ψ (t)

called as the mother wavelet. The time scaling and the time shifting function given by equation 3

and the continuous wavelet transform are given by equation 4 and 5.

ψ%�τ�&� �'

()%) ψ *+�τ

% ,-……………………………………(3)

Where .- / -0-1-�

� 2�&�� $� � 3

45 � � �)5�6)�7)6)

�� -$6 8 -9

“The conditions above state that ψ (t) is bandpass and sufficiently smooth. Assuming that

|| ψ (t) || = 1, the definition above ensures that ||2%�:�&�||=1 for all .-and 0” ( Schniter Phil,2005).

�;<=�>� ?� � � ��@>�A�B�CCCCCCCCC�� -��……………………………..(4)

�� D;@� � �;<=�>�?�--------@>�A�B�

>� �?�>��

�� …………………(5)


Explaining the above equations in simple words mean that continuous wavelet transform

can decompose the signal into a collection of shifted and stretched versions different scales.

Wavelet transform is thus a ‘time-scale’ analysis and scale is inverse of frequency.

There are different types of wavelets available and few of the wavelet transforms are listed

below:

• Haar

• Morlet

• Mexican Hat

• Meyer

• Quadratic Spline

• Dyadic Spline Wavelet

• Debauchies

• Biorthogonal

• Gaussian

There are several other types of wavelets and each type of wavelet is different from one

to the other by properties such as symmetry, singularity etc. The selection of wavelets for ECG

signal processing is done based on the following parameters:

• “Orthogonal Vs. Non-Orthogonal: a non-orthogonal analysis involves high

redundancy at larger scales” (Bhatia et al, 2006).

• “Complex or real valued wavelet function: a complex wavelet providing

information about both amplitude and phase is better suitable for oscillatory

signal behaviour whereas real valued wavelet function only returns a single signal


modulus that can be used to isolate signal peaks and discontinuities” (Bhatia et al,

2006).

• “Width of the wavelet function: this directly acts on the analysis resolution that is

for wavelet the result of balance between the length of analysis of samples

window in time frequency axes” (Bhatia et al, 2006).

• “Shape of the mother wavelet: wavelet filtering can be viewed as an adapted filter

looking for the highest correlation between the ECG signal to analyze and the

considered wavelet” (Bhatia et al, 2006).

“For ECG parameter estimation, it is desirable that the basis function (wavelets) be

symmetric/antisymmetric” (Sivannarayana et al. 1999). “It is also desirable that the basis have a

minimum number of sign changes which will simplify the steps in the parameters estimation

algorithm” (Sivannarayana et al. 1999). Out of the wavelets listed above Biorthogonal Wavelet

was the choice of wavelet for this project because it satisfies the properties that are required for

ECG signal analysis. To understand Biorthogonal Wavelets, it is important to understand the

concepts of dual basis. “Consider a two-dimensional coordinate space. Any two vectors {e1, e2}

that are not parallel can form a basis for the space. If the angle between the two vectors happens

to be 90 degrees, we have an orthogonal basis” (Wolfram Research, 2009).

“Any vector EF in this space can be written uniquely as a superposition of the two basis

vectors: EF � G'�' H G � . If the basis is orthogonal,�I�� JI-� and the component GI along�I is

given by the inner product�I EF � G'�I�' H G �I� � GI . However, if the basis is not

orthogonal, GIis no longer given by the inner product of EFand �I . In order to calculate the

componentGI, we introduce another set of basis vectorsK�'L� � LM, called the dual of {e1, e2}. The


dual basis satisfies �ILN �� JI-� and the space spanned by the dual basis is called the dual space

of the original space. In terms of the dual basis, the components of a vector along the basis {e1,

e2} can be calculated as �ILN EF�-O G�� ILN �� GI”(Wolfram Research, 2009) . “Similarly, for a

nonorthogonal basis KPI��M of a function space, we can introduce dual basis KPIL��M

byQPIL� P�R � -� PSL��CCCCCCCC∞

�∞ -P��$� � -JI-� . A function f(t) can be decomposed as a superposition

of the nonorthogonal basisKPI��M using a set of dual basis functionsKPIL��M:

�� O �IPI�� O �PIL� ��PI��II . We will tacitly assume that the function space and its dual

are the same, a condition satisfied by L2. Therefore, the roles of dual basis and the original basis

can be interchanged and we obtain�� O �PI�I ��-PIL��. When KPI��M is orthogonal, we have

the obvious relation PL � P” (Wolfram Research, 2009).

“The dilations and translations of the scaling function KP�T��M constitute a basis for Vj and,

similarly, KU�T��M for Wj. To define a dual multiresolution analysis with dual subspacesKV�LM

and KW�LM generated from a dual scaling function PL and a dual mother waveletUL, respectively.

In terms of subspaces, the above biorthogonality conditions can be expressed as

VX Y -W�L� V�L Y WX-.Z$-WX Y W�

L�[\-X ] XL ” (Wolfram Research, 2009).

“By definition, a scaling function and a mother wavelet satisfy the dilation equation and the

wavelet equation, respectively. So we have

^�� -_�O à^�� a�>b�-c�� _�O daâ �� a�-a …………………………(6)

and


^L�� -_�O àL^L�� a�>b�-cL�� _�O daL^La �� a�-a …………………..(7)

The roles of the two functions-P andPL or U and-UL can be interchanged. Or if we "take the

dual" of the above equations (define .e � .), we obtain the following relations,

�fL � QP'�f� PLR � -_g� P�g� � T�CCCCCCCCCCCCCPL��$�-.Z$-hfL �-QP'�f� ULR � -_g� P�g� ��

��

T�-UL��$�” (Wolfram Research, 2009).

“In frequency space, the above dilation and wavelet equations 6 and 7 assume the form

î�j� � k- *j�,î *j�, ->b�-c

i�j� � l- *j�,î *j�,- ……………………………….(8)

and

^Li�j� � k- *j�,^Li *j�, ->b�-c

i�j� � l- *j�,^Li *j�,-- …………………………..(9)

As before,m�� is defined by-m�� -O �f��If�n_ f , and G��, mL��, andoL�� are

defined analogously” (Wolfram Research, 2009).

The “biorthogonality conditions can be translated into conditions on the filter coefficients using

the dilation and wavelet equations. These conditions are

mL��m��CCCCCCC H mL�� H p�m�� H p�CCCCCCCCCCCCC � q-.Z$-oL��o��CCCCCCC H oL�� H p�o�� H p�CCCCCCCCCCCC � q--

-oL��m��CCCCCCC H oL�� H p�m�� H p�CCCCCCCCCCCCC � 3-.Z$--mL��o��CCCCCCC H mL�� H p�o�� H p�CCCCCCCCCCCC � 3-

Using P� � r� T�� O �ss � gTPXr�� and U� � r� T�� O hss � gTPXr��, the relations for

wavelet decomposition becomes” (Wolfram Research, 2009):

�a �D � O `t � �a-�t

t >b�-�a

�D � O dt � �a-�t

t -…………………………………….(10)


Figure 11 below shows the typical shapes of biorthogonal and reverse biorthogonal wavelets

available in the Matlab 7.1 Release 14 Wavelet Toolbox 3.0 Biorthogonal Wavelets 2.4 were

chosen because of the near shape of the ECG signal and that of the 2.4 biorthogonal wavelet.

This can be seen in the figure 10.

Figure 10: 2.4 Biorthogonal Wavelet and ECG Signal


Figure 11: Types of Biorthogonal Wavelets available in Wavelet Toolbox 3.0 of Matlab 7.1 R 14[Source: Matlab7.1R14 Help]


VIII. Introduction to Early Myocardial Infarction Detection System

After having acquired the background for understanding how myocardial infarction

occurs and what the ways and means to diagnose it are, we move on to discuss about the

specifics that are crucial to understand the algorithm for early myocardial infarction detection.

The subsequent section will discuss the specifications that were considered, the features of the

algorithm, the detailed functions, and the steps to get to early myocardial infarction detection.

During the last few years telemetry monitoring has become the most widely used for of

monitoring system and telemetry monitoring of cardiovascular diseases is gaining popularity.

Hence, it is important to have a telemetry device monitoring the condition of heart to warn onset

of myocardial infarction. The method presented in the project is to detect ST-changes in the ECG

of the patient based on wavelet signal processing technique to warn onset of myocardial

infarction. The testing for the method is done using the following databases from Physiobank

organization:

• MIT-BIH ST change Database

• Long-term ST Change Database

• European ST-T Database

• MIT-BIH Normal Sinus Rhythm Database

• MIT-BIH Noise Stress Test Database

a. Background

The typical frequency range for ECG signal is between 0.5 to 100Hz. Table 2 below

gives the typical wave durations and amplitudes that are present in ECG signal which is the

physiological signal under measurement for this project. Figure 12 shows the normal ECG


waveform on a strip chart paper. It shows how to interpret data from a strip chart into the actual

amplitude and time values that are of interest. “Changes in the ST-segment of the ECG may

indicate that there is a deficiency in the blood supply to the heart muscle” (Tompkins Willis,

2000).

Figure 12: Normal ECG waveform on Strip Chart [Source: Barron Jon, 2007]

The detection of the ECG waveform is based on the on the duration and amplitude

measurements given in the table 2 and figure 10. Matlab 7.1 was used for the programming of

the code for ST-elevation detection. The signal processing for the ECG waveform was done by

using Biorthogonal Wavelets. For using Wavelets Wavelet Toolbox3.0 from the Matlab software

was used. The use of Biorthogonal Wavelet was based on the symmetry, and the fact that the

shape of the biorthogonal wavelets is closest to that of the ECG Waveform, giving precision

accuracy to the time-scale conversion. By using Biorthogonal wavelets it is possible to get

complete reconstruction of the signal.

“The ST-segment represents the period of the ECG just after depolarization, the QRS

complex, and just before the repolarization, the T- wave”(Tompkins, Willis, 2000). The ST-


segment is isoelectric in normal ECG and hence the elevation of ST-segment is tested for by

comparison between isoelectric line and the ST-segment. The next section describes the features

and functions of early myocardial infarction detection.

Table 2: Typical Amplitudes and Durations for ECG signal [Source: Saritha. et.al. (2008)]

Amplitude

P-Wave 0.25 mV

R-Wave 1.60 mV

Q-Wave 25% of R-Wave

T-Wave 0.1 to 0.5 mV

Duration

P-R Interval 0.12 to 0.20 s

Q-T Interval 0.35 to 044 s

S-T Interval 0.05 to 0.15 s

P-Wave Interval 0.11 s

QRS Interval 0.09 s

The most important function of early myocardial infarction detection is that it warns the

patient of the imminent attack as early as 20 minutes before the actual attack. In typical cases, a

patient would only go to the emergency room when the heart attack has already happened. This

reduces the time for the doctors to treat it. If the patient and the doctor are warned early of the

imminent attack, the doctor gets the much needed time to curb the intensity of the attack by

providing medication faster. By having the ST-elevation detection program installed on a


portable ECG machine having wireless transmission capability will make the best standalone

telemetry device available for treating myocardial infarction related conditions.

b. Materials and Method

i. Database Description

As described above there were five databases that were used for testing the method

proposed for detecting onset myocardial infarction. The databases are developed and managed

by Physiobank organization. Physiobank is a database of “well-characterized digital recordings

of physiologic signals and related data for use by the biomedical research community”

(Goldberger et al. 2000).

MIT-BIH ST change database has ECG recordings from long “exercise stress tests and

exhibit transient ST depression” (Albrecht P., 1983). There are some recordings in this database

that consists of ST-elevation too.

Long-term ECG database consists of ECG recordings from 80 subjects “chosen to exhibit

a variety of events of ST segment changes, including ischemic ST episodes, axis-related non-

ischemic ST episodes, episodes of slow ST level drift, and episodes containing mixtures of these

phenomena” (Franc Jager. et al, 2003).

European ST-T database consist of “ambulatory ECG recordings from 79 subjects”

(Taddei, A.et al. 1992).

MIT-BIH Normal Sinus Rhythm database consists of recordings from 18 subjects. The

database comes from the Arrhythmia Laboratory at the Boston’s Beth Israel Deaconess Medical

Center (Goldberger et al. 2000).


The fifth and the last database that was tested for the method described in this report for

ST-segment changes was that of MIT-BIH Noise Stress Test Database. This database consists of

ambulatory ECG recordings. “The noise recordings were made using physically active

volunteers and standard ECG recorders, leads, and electrodes; the electrodes were placed on the

limbs in positions in which the subjects' ECGs were not visible” (Moody GB, 1984).

Out of the available ECG recordings from the databases described above, 17 recordings

were tested from MIT-BIH ST Change Database, 5 from MIT-BIH Noise Stress Test Database, 6

from the Long-Term ST Database, 13 from the MIT-BIH Normal Sinus Rhythm Database and

14 recording were randomly selected and tested for ST-segment change using the method

described in the next section.

ii. Method for ST-elevation detection

The warning of the imminent myocardial infarction is done through the use of different

filtering techniques, followed by the signal processing to ensure accurate ECG signal extraction

and estimation. Electromagnetic Interference (EMI), muscle activity (EMG), bowel movements,

and electric line interference are often always embedded in ECG signal and constitute noise in

the ECG signal. It is important to be able to remove this noise in order to have a good ECG

parameter estimation. The presence of this noise also changes the baseline for the ECG signal, it

is also important for ECG parameter extraction and estimation to be able to remove the baseline

wander before the actual ECG signal parameters are extracted. This is done by having a baseline

wander cancellation along with efficient signal filtering to remove electric line, EMI, EMG and

other types of noise from the ECG signal. After eliminating the baseline wander the ECG signal

was converted into time-scale domain for further analysis. Converting the ECG signal into time-

scale domain was done because “Wavelet Transformation (WT) has shown to be substantially


noise proof in ECG segmentation and thus appropriate for ST-T segment extraction”

(Milosavljević Nebojša, et al.2006). The signal was decomposed into 4 scales ranging from 21 to

24. Figure 13 shows sample decomposed scales using Dyadic Wavelets for ECG signal.

Figure 13: Dyadic Wavelet Transform of ECG signal [Source: Jouck. P.P.H. (2004)]

It can be interpreted from Figure 13 that wavelet transform “at small scales reflects the

high frequency components of the signal and, at large scales, the low frequency components. The

energy contained at certain scales depends on the center frequency of the used wavelet” (Jouck.

P.P.H., 2004).


24 scale was used to detect the R-peak because “most energies of a typical QRS- complex are at

scales 23 and 24…. QRS complex with high frequency components, the energy at scale 22 is

larger than that at 23” (Jouck. P.P.H., 2004). According to Wenli Chen et al. 2007, the high

frequency noises like the electric line interference, muscle activity, bowel movement activity,

electromagnetic interference is concentrated in the lower scales of 21 and 22, while the levels 23

and 24 constitute for less noise compared to the lower scales. This can also be seen in figure 14.

Wenli Chen et al. 2007 summarize that the frequency of the QRS complex is mainly present in

the 23 and 24 scales. Since the scale 24 shows less noise compared to 23, in this project we chose

scale 24 for R-peak detection.

Figure 14: Biorthogonal Wavelet Transform of ECG Signal from 21 to24 level

The R-peak detection was followed by detecting point S and Q. (Pam, Tompkins, 1985)

method was utilized for detection of the R-peaks. After finding R-peaks (Tompkins, 2000)

method was used for detecting points S, Q, T-peak, T-point, J-point as seen in figure 15.


Figure 15: Method for ECG Parameters Detection [Source: Tompkins, 2000]

The first inflection point before Q point was chosen as point K and P point was found, the

distance between point P and point K is the isoelectric line. The isoelectric line was then

compared to the ST-segment for checking the elevation or depression of the ST-segment in all

the ECG waveforms. The algorithm for the ST-change detection program is divided in several

subsections and is as follows:

1. Signal Filtering and Baseline Wander Correction

After getting the ECG dataset, the first step was to remove the inherent noise from the

ECG signal. The typical noises that affect the ECG signal are electric or power line interference

of 60Hz, EMG or the muscle activity that gets captured along with the ECG measuring

electrodes, bowel movements also called EGG movement; EEG sometimes may interfere with

ECG signal and constitute noise. Electromagnetic interference is also seen in the ECG signal. It

thus becomes important to remove the noise from the signal for accurate and precise ECG


parameter detection and extraction. The filtering technique applied in this project is a simple FIR

filter.

Figure 16: Filter expressed in Direct Form II transposed structure [Source: Matlab7.1R14 Help]

Figure 16 can also be expressed as:

y(n)=(1)*x(n)+b(2)*x(n-1)+…+b(nb+1)*x(n-nb)-a(2)*y(n-1)-…-a(na+1)*y(n-na)….(11)

After filtering the signal using the FIR filter, the filtered signal was passed through

median filters to correct for the baseline wander correction. The process followed included

passing the filtered signal through a 200ms median filter that eliminates the QRS complex from

the signal. This median filtered signal was again passed through a 600ms median filter to

eliminate the T-wave from the signal. This final filtered signal is the signal that consists of the

noise that changes the baseline through the signal. The difference between the FIR filtered signal

and the final median filtered signal thus gives the baseline wander eliminated signal. The

baseline wander elimination process in shown in figure 17, where the first plot if of the FIR

filtered signal, the second plot is of the first median filtered signal, while the third plot is of the

second median filtered signal, the final plot shows the baseline wander eliminated signal.


Figure 17: Baseline Wander elimination

2. R-Peak Detection

The R-peak detection was carried out by using the Pan, Tompkins 1985 method of R-

peak detection. The method uses the threshold level to calculate the maximum amplitude in the

ECG waveform. The threshold level set for the R-peak detection in the ST-change program is

approximately 0.6. The R-peak detection was done in the time-scale domain at level 24. Same

level was utilized to estimate the other key points in the ECG waveform. Figure 18 shows the

final ECG waveform with all the detected points along with its legend.

3. Heart Rate Measurement

It is essential to calculate the heart rate of the patient in order to determine accurate

measurement and approximation of the PQSTJK points on the ECG waveform; hence heart rate

was calculated from the datasets after for every minute of data scanned. The heart is calculated

by taking the difference in time between consecutive R-peaks.


Figure 18: R-peak Detection and PQSTJK extraction of ECG wave at level 24

4. Detection of S, Q, T, T-peak, J, K, P point

Tompkins method for ST-segment analyzer was followed to compute J, T-peak, and T-

point (Tompkins, 2000). The algorithm for estimating the Q and S point was derived from

Tompkins method of ST-segment analyzer. After detecting R-peak, knowing the QRS complex

duration to be 60ms, points Q and S were estimated as the first inflection points to the left and

the right of R-peak respectively. After estimating the S-point, J-point was estimated to be first

inflection point after S-point to the right of R-peak. T-peak was estimated to between R-peak+

400ms to J-point+80ms. T-point was later on estimated from T-peak to T-point duration of 35ms

to the R-peak side. Similarly K-point was estimated to be the first inflection point after Q on the

left side of the R-peak, and P-point was estimated to be the first inflection point after K-point on

the P-peak side. The detection of point is depicted in figure 18 above.


5. Isoelectric line and ST-segment Computation

After the estimation of all the relevant points in the ECG waveform, the isoelectric line

and the ST-segment were computed. For the isoelectric line the mean value for point P and point

K was computed and for ST-segment the mean value for point J and point T was computed.

“Single ST-deviation as an absolute amplitude change between ST point and PR point values

greater than 0.1mV” (Milosavljević Nebojša, et al.2006). The computed values for both

isoelectric and the ST –segment were then compared within a range of ±0.1mV range.

The complete code for the ST-segment change program is available in Appendix A section of

this report. The generalized flow chart for the above described algorithm is given figure 21. The

next section describes the testing and verification done using the datasets and the results obtained

are also presented.

6. Graphical User Interface (GUI)

A graphical user interface was created using Matlab’s GUIDE to create an intuitive

interface to check for any abnormalities in an ECG data that is being tested. The ECG dataset

name was given as the File name. After typing the File name, the start button activates the GUI

program. The GUI consists of three axes figures. The first one from the top is original, unfiltered

noisy signal that is being fed to the program. The second is the filetered and base line wander

corrected signal. The third shows how the detection and estimation of parameters is being done

in the program. The figures are refreshed after the every minute of procesing of the data. The

GUI processes 5 minutes of the ECG dataset that is being fed to it. An ECG data that passes the

test will show a “No ST Detected” message (figure 19) and an ECG data that has an ST-segment

changes predicting abnormality, will show a “Warning” message (figure 20).

Figure 19: Shows an intuitive GUI result for an ECG data with no MI detected

Figure 20: Shows an intuitive GUI result for an ECG data with an MI detected.


Shows an intuitive GUI result for an ECG data with no MI detected

Shows an intuitive GUI result for an ECG data with an MI detected.


Shows an intuitive GUI result for an ECG data with no MI detected

Shows an intuitive GUI result for an ECG data with an MI detected.


Figure 21: Flowchart for ST-elevation detection

Load the.mat Data set File and the Header File

Read the Header File and get required variables (Gain, Frequency)

Filter the Data set and correct the baseline wanders

Is ST>ISO

Detect PQRST Wave

Compute Isoelectric line (ISO) and ST segment

Check the ECG Signal for 1minute

Show MI Warning

End

Start

NO

YES

Go to the next minute’s signal


IX. Testing and Verification

Extensive testing of the ST-change detection program was done to ensure the code

estimates the ST-segment changes effectively. Receiver Operating Characteristic curve better

known as ROC curve are “useful for organizing classifiers and visualizing their performance”

(Fawcett Tom, 2006). ROC graphs are used for showing the tradeoff between the true positive

rate and false positive rate. The next section discusses the results that were obtained during the

testing.

The total processing time for a minute of dataset was found to be ranging between 4

seconds to 10 seconds depending on the sampling frequency of the datasets. Calculation of Heart

Rate every minute of data processing was done to account for the change in the heart rate. The

testing and verification results are provided in the next section.

a. Results

A total of 54 datasets were tested. Random datasets from the five described databases

were selected for the testing. The datasets were tested with the ST-change program and cross

checked visually. The time interval for every dataset evaluated was one minute with a total of 5

minutes for each dataset. Out of every database close to 10 samples were analyzed. Out of the

54, 41 were True Positive whereas 13 were False Positive. 15 datasets from databases that have

ST-change gave 100% accuracy and 11 datasets from normal sinus rhythm and noise stress

showed no change in ST-segment i.e. again gave 100% accuracy rate. Other 15 datasets

comprising of both the ST-change and the normal ECG database were accurate for 80%, 70%,

50% and 40% respectively. The other two accuracy levels 50% and 40% were chosen only for

doing the ROC analysis. The overall accuracy of the algorithm was found to be approximately


73% from the ROC analysis. Table 3 shows the details for the testing of databases. The detailed

testing results are in Appendix B of this report.

Table 3: Testing Results for ST-change detection program

Observed Frequencies Cumulative Rates Diagnostic Levels

False Postive

True Positive

False Positive

True Positive

90% 4 26 0.3077 0.6341 80% 1 4 0.3846 0.7317 70% 2 4 0.5385 0.8293 50% 1 4 0.6154 0.9248 40% 5 3 1 1

Total 13 41

A more detailed version of the database testing is available in Appendix C part of this report. The

cumulative rates were calculated using the mathematical formulae developed by Lowry, Richard

2008. Table 4 shows the plotted point for ROC curve followed by the ROC curve in Figure 22.

b. Discussion

Based on the results it can be said with confidence that the ST- change detection program

produces overall better results that many available method for ST measurement. Unlike many

other ST –detection algorithms, this ST-change detection program also takes into consideration

the change in the Heart Rate, which if ignored might not give precise ECG parameters estimation

and result wrong ST-change predictions. Using Biorthogonal wavelets gives precise

transformation points due to the similarity of shape between the ECG signal and that of the

biorthogonal wavelets.

Table

False Positive Rate(1-Specificity)

Figure 22:


Table 4: Data Points for ROC Curve

False Positive Rate Specificity)

True Positive Rates (Sensitivity)

0.05 0.1612 0.1 0.3553

0.15 0.4688 0.2 0.5494

0.25 0.6118 0.3 0.6629

0.35 0.706 0.4 0.7434

0.45 0.7764 0.5 0.8059

0.55 0.8326 0.6 0.857

0.65 0.8794 0.7 0.9001

0.75 0.9194 0.8 0.9375

0.85 0.9545 0.9 0.9705

0.95 0.9856

: Receiver Operating Characteristic Curve



Use of Wavelet transforms speeds the signal processing of the ECG, which decreases the

overall processing time for ST-segment estimation. Use of single level ECG parameter

estimation reduces the time required for reconstruction of the signal. Also, faster dataset

processing gives faster ECG parameter estimation, which gives faster response on the change in

ST-segment, since the processing time for the ST-change detection program was found to be

approximately 8 seconds. This shows that the fast algorithm of the ST-change detection program

in real time ECG signal analysis will provide real time response, which is crucial in the case of

myocardial infarction detection.

X. Economic Justification

a. Executive Summary

SMART Medical Devices Company is an established medical device company that

design and sells medical diagnostic devices. It currently does not have a portable ECG device in

the market. SMART Medical Device Company wants to develop a portable ECG device that can

analyze physiological signals to detect the onset of myocardial infarction.

Myocardial infarction is defined by the American Heart Association as the damaging or

death of the heart muscles due to the blockage of blood supply. Diagnosing patients and

detecting the symptoms, before the onset of myocardial infarction, is important to increase the

mortality rate of patients. There are several ways to diagnose myocardial infarction and one of

which is by using electrocardiogram (ECG) devices. Most of the ECG devices in the market are

bulky and are not suitable for everyday use. In addition, most ECG devices are installed in

hospitals where doctors or nurse can readily give diagnosis to patients. But, time is of the

essence for doctors and nurses and they cannot be with the patients all the time. Furthermore,


hospitals are loaded with patients that only need short-term care. In 2008 alone, there were more

than 33 million short-term, acute patients that stayed in non-federal hospitals. (American

Hospital Directory, 2008)

One of the ways to reduce the numbers of short-term, acute patients staying in hospitals is

by using a portable diagnostic device such as a small ECG device. It can be worn 24/7 with ease

and comfort to patients. Healthcare providers can prescribe this device and unload hospitals with

extra cost related to myocardial infarction disease. But to be useful, the portable ECG device

must be smart and be able to process input data from the patients without the aid of a physician.

The smart analytical tool that SMART Medical Devices develops complements the portable

ECG device of the company since it can detect myocardial infarction without the aide of a

doctor.

Portable ECG devices are already in the market. Some are manufactured and marketed

by big company competitors such as Philips Healthcare, Welch Allyn, and GE Medical. They

gather ECG data from the patients and collect them to be sent by the patient to the doctor for

further analysis. Some of them also have simple analytical tool to diagnose the patient. On the

other hand, SMART Medical Devices’ ECG device has an algorithm that uses wavelet transform

methodology as a smart analytical tool to analyze the ECG data coming form the patient. It can

perform real-time analysis of the ECG data and can instantly notify the patient if there is an onset

of myocardial infarction. Then a doctor can confirm the diagnosis remotely or as soon as the

patient arrives in the hospital.

In 2009, myocardial infarction had a prevalence of 8 million Americans and an estimated

incidence of 900,000. Furthermore, coronary heart disease (CHD), which includes myocardial


infarction, has a death rate of 144.4 in the United States. (American Heart Association, 2009) A

smart portable ECG device with smart analytical tool can greatly reduce that numbers.

For this project, a funding of $ 1.5 million is needed to start for the coding of the smart

analytical software. The funding will be needed to acquire computer hardware and software

licenses. Majority of the funds will go to the salary of the team, which composes of five

software engineers, with different levels of expertise. It is estimated that the breakeven point

will be reached on the third quarter of the product release, assuming the company will sell 500

units in the first year and with a price point of $2,000.

b. Problem Statement

The ability to detect the symptoms of myocardial infarction before the disease becomes

severe is important to save the life of a patient. The best way to diagnose and identify the

symptoms of myocardial infarction is ECG. Current ECG diagnostic devices do not have a smart

analytical function to analyze ECG data. There’s a need to analyze ECG data from patients

prone to myocardial infarction without the immediate aid of doctors or nurses. This can greatly

increase the mortality rate of a patient and reduce the burden caused by myocardial infarction to

hospitals.

c. Solution and Value Proposition

This project’s main focus is to develop an analytical tool that can analyze the ECG data

of patients. The software can be installed in a portable ECG device developed by SMART

Medical Devices. This project can greatly leverage the company to be the best in the industry.

A well-made, and cost efficient portable ECG device that can accurately and efficiently diagnose

patients can facilitate the realization of this mission.

d. Market Size

There’s a huge market for portable medical devices that diagnose heart diseas

disease is the number one cause of death in the United States. Every year, the American Heart

Association estimates the prevalence of myocardial infarction. From 2003 to 2009, there was an

estimated increase of 3.95% in the prevalence of myoc

in figure 23).

Figure 23: An estimate of myocardial infarction prevalence in the United Statesand Stroke Statistics - 2003 Update, Heart Disease and Stroke Statistics

Disease and Stroke Statistics - 2005 Update, Heart Disease and Stroke Statistics Heart Disease and Stroke Statistics

Update, Heart Disease and Stroke Statistics

And from the same period, there was an increase of 8.09% of new and recurrent attacks

of myocardial infarction in the United St

The 2009 estimated direct and indirect cost of myocardial infarction was estimated to be $165

billion compared to $133 billion in 2004, a 24% increase


There’s a huge market for portable medical devices that diagnose heart diseas



estimated increase of 3.95% in the prevalence of myocardial infarction in the United States (

An estimate of myocardial infarction prevalence in the United States2003 Update, Heart Disease and Stroke Statistics - 204 Update, Heart

2005 Update, Heart Disease and Stroke Statistics Heart Disease and Stroke Statistics - 2007 Update, Heart Disease and Stroke Statistics

Update, Heart Disease and Stroke Statistics - 2009 Update)


of myocardial infarction in the United States (Seen in figure 24).


billion compared to $133 billion in 2004, a 24% increase (Seen in figure 25).


There’s a huge market for portable medical devices that diagnose heart diseases. Heart



United States (Seen

An estimate of myocardial infarction prevalence in the United States (Heart Disease

204 Update, Heart 2005 Update, Heart Disease and Stroke Statistics - 2006 Update,

2007 Update, Heart Disease and Stroke Statistics - 2008



Figure 24: An estimate of new and recurrent incidence ofStates(Heart Disease and Stroke Statistics 2005 Update, Heart Disease and Stroke Statistics

Statistics - 2007 Update, Heart Disease and Stroke Statistics Stroke Statistics

Figure 25: The direct and indirect cost of myocardial infarction per yearStroke Statistics - 2004 Update, Heart

Disease and Stroke Statistics - 2006 Update, Heart Disease and Stroke Statistics Heart Disease and Stroke Statistics


An estimate of new and recurrent incidence of myocardial infarction in the United Heart Disease and Stroke Statistics - 204 Update, Heart Disease and Stroke Statistics

2005 Update, Heart Disease and Stroke Statistics - 2006 Update, Heart Disease and Stroke Disease and Stroke Statistics - 2008 Update, Heart Disease and Stroke Statistics - 2009 Update)

The direct and indirect cost of myocardial infarction per year (Heart Disease and 4 Update, Heart Disease and Stroke Statistics - 2005 Update, Heart

2006 Update, Heart Disease and Stroke Statistics Heart Disease and Stroke Statistics - 2008 Update, Heart Disease and Stroke Statistics

Update)


myocardial infarction in the United

204 Update, Heart Disease and Stroke Statistics - 2006 Update, Heart Disease and Stroke

2008 Update, Heart Disease and

Heart Disease and 2005 Update, Heart

2006 Update, Heart Disease and Stroke Statistics - 2007 Update, 2008 Update, Heart Disease and Stroke Statistics - 2009


In 2009, there are a total of 7.9 million cases of myocardial infarction in the United

States. Every 34 seconds, an American will suffer a heart attack, and from that, 25% men and

38% women will die within the first year after suffering a heart attack. In 2005, the recorded

number of deaths due to myocardial infarction was more 150,000 Americans (American Heart

Association, 2009).

Giving patients, who are prone to heart attacks or who had suffered an initial heart attack,

an access to a portable ECG device can greatly decrease their mortality rate.

The US market for home monitoring is expected to increase over 5% annually to $1.8

Billion in 2012 (Demand for Home Medical, n.d.). SMART Medical Devices will market the MI

Detector, a portable ECG device with the smart algorithm, in the United States.

e. Competitors

Heart monitoring device is a big market, considering that these devices diagnose the

number one cause of death in the United States. With such, there are many competitors in this

industry. Philips Healthcare, a part of Koninklijke Philips Electronics N.V. (Royal Philips

Electronics), developed an ECG Holter device, DigiTrak XT. It can monitor and record the

patient’s ECG data for up to 7 days. The recorded data are then transferred to personal computer

using Philips Healthcare’s software and analyzed before sending the data securely sent to a data

center. The Philips Healthcare DigiTrak XT Holter device can cost almost $9,000.

Welch Allyn, another company that develops ECG Holter devices, has HR 100 and HR

300 ECG Holter devices that record, and store patient’s data. The recorded data are also

downloaded to a personal computer and analyzed using proprietary algorithm to detect any

abnormalities in the patient’s recorded ECG data. It is sold online and cost approximately

$3,000.


GE Healthcare, a part of General Electric Company, developed SEER Light compact

digital recorder, a compact ECG Holter device that can record ECG data for up to 48 hours. It is

a part of GE Healthcare’s ambulatory system to analyze the patient’s ECG data for any

anomalies. After monitoring the patient, the data is downloaded and analyzed using an advance

algorithm and can be stored to a central database, which is run by proprietary software. The

device can be purchased for almost $9,000.

Omron Healthcare has a portable ECG Monitor, HCG-801, which can be used by patients

whenever symptoms of heart disease occur. It has a large screen that can display the ECG data

from the patient, but the data also needs to be downloaded to a computer for analysis. The

device is cheap and is found online for almost $500.

All of the devices mentioned are ECG Holter devices that can monitor the patient’s heart

activity for 24 hours or more. They are portable and can be worn for a long period of time

without impeding day-to-day activities of patients. The portable and easy to use MI Detector

device by SMART Medical Devices will be a Holter device, which can provide constant and real

time analysis of ECG devices. The advantage of the MI Detector over the competitors’ is that it

can monitor the patient and process the data recorded in the portable ECG device without

downloading the data to a personal computer.

f. Customers

The target markets for SMART Medical Device’s MI Detector are physicians that will

prescribe the ECG monitoring device to patients. This ECG device is considered a

“prescription” device since it monitors physiological functions of the patient. Special training

needs to be administered to the patient before a patient can bring the device home. The device

will be particularly marketed to doctors specializing in heart conditions. And since this device


will be regulated under the Food and Drug Administration (FDA) law, this device will be

marketed initially in the United States.

The device can be purchased by individual patients in pharmacies or through online

channel, granted they have a prescription from their physician. The MI Detector device can be

covered by Medicare or Health Insurance or can be purchased, out-of-pocket, at a manufacturer

suggested retail price (MSRP).

g. Cost

There are several factors that influence the cost of MI Detector. The MI Detector device

is composed of a hardware and software components. The hardware component is outsourced to

an Original Equipment Manufacturer (OEM), which has a proven record for manufacturing

quality-made medical devices.

The software component is developed in-house and is designed by SMART Medical

Device’s software department.

The total cost in designing, developing, marketing, delivering and servicing the MI

detector is estimated from different cost drivers.

i. Fixed costs

Fixed costs are expenses that do not change and are not based on the activity to develop

and market the device. Majority of the fixed cost associated with the MI Detector consists of

salary of software engineers. The software development team is composed of two level 1 or 2

software engineers, three level 4 to 5 software engineers, and a department manager. Other costs

associated with the fixed costs of MI Detector device are: the purchase of software licenses and

hardware components as well as other development cost for improving the algorithm of the MI

Detector. The fixed cost of MI Detector is listed in table 5.


ii. Variable Costs

Variable costs are cost drivers that are associated with the activity from developing to

servicing the MI Detector device. It varies from time to time due to the number of volume of

producing and selling the device. Since an OEM vendor manufactures the MI Detector device,

there is no associated cost with manufacturing. Logistics and manufacturing contracts are major

cost drivers for this device, which consists of contracting the manufacturing to an OEM

manufacturer, receiving and delivery of the device from the OEM manufacturer to the sales

channel, and keeping the inventory in order. Other cost drivers are sales and marketing, the

support staff and the initial regulatory and legal requirements to manufacture and sell the device.

Table 6 illustrates the variable costs of MI Detector device.

Table 5: Fixed cost of MI Detector device

Fixed Cost 2009 2010 2011 2012 2013

Hardware and License $6,000.00 $1,000.00 $1,000.00 $1,000.00 $1,000.00

Software Engineer Salary

Level 1/2 Engineers (2) $100,000.00 $105,000.00 $110,250.00 $115,762.50 $121,550.63

Level 4/5 Engineers (3) $300,000.00 $315,000.00 $330,750.00 $347,287.50 $364,651.88

Department Manager $120,000.00 $126,000.00 $132,300.00 $138,915.00 $145,860.75

Total S. Engr Salary $520,000.00 $546,000.00 $573,300.00 $601,965.00 $632,063.26

Development Cost $50,000.00 $0.00 $0.00 $0.00 $0.00

Business Expense $100,000.00 $100,000.00 $100,000.00 $100,000.00 $100,000.00

Total Fixed Cost $676,000.00 $647,000.00 $674,300.00 $702,965.00 $733,063.26


h. Price Point

The price of the MI Detector device will be below the competitor’s prices. The MI Device will

be sold with an MSRP of $2,000, which is well below the competitor’s. The device, when purchased

initially, is a complete device since all the analytical processing is done in the device. Occasional

accessory replacement is necessary after every use to ensure accurate ECG data recording and analysis.

Table 6: Variable cost of MI detector device

Variable Cost 2009 2010 2011 2012 2013

Marketing Salary $150,000.00 $157,500.00 $165,375.00 $173,643.75 $182,325.94

Support Staff $150,000.00 $157,500.00 $165,375.00 $173,643.75 $182,325.94

Logistics and Contracts $500,000.00 $500,000.00 $500,000.00 $500,000.00 $500,000.00

Regulatory and Legal $50,000.00 $5,000.00 $5,000.00 $5,000.00 $5,000.00

Total Variable Cost $850,000.00 $820,000.00 $835,750.00 $852,287.50 $869,651.88

i. SWOT Assessment

The device itself has much strength and some weaknesses. The SWOT assessment of the

MI Detector device is shown in table 7.


Table 7: SWOT assessment of MI Detector medical device

Strengths Weaknesses

• Provides real-time and accurate analysis of ECG data

• Data does not need to be downloaded for processing

• Portable and easy to use • Can be worn several hours per

day

• Since the device is a product in the company portfolio, patients may be hesitant to try and use the product

Opportunities Threats

• There are currently no other major medical device company that offers the same product that we offer

• Other medical device companies might develop similar products with similar price point

j. Investment Capital Requirement

The MI Detector device is composed of two components: Hardware and Software. Since

the MI Detector device’s major component is the smart software installed to diagnose

myocardial infarction, majority of the funding will be focused on the software development

team. In addition, SMART Medical Devices will outsource the manufacturing of the portable

ECG device to an OEM company. Thus, the capital required to fund the project will primarily

be divided down for software engineers’ salaries, and the logistics of the portable device. The

initial year will require a budget of $1.5 million to fund the initial development of the product.

The company is expected to become profitable with this device on the second year of

developing and selling the device, as shown in figure 26. The company is expected to become

profitable with this device on the second year of developing and selling the device, as shown in

figure 27.

Figure 26: Initial Investment Requirement of MI Detector device

Figure 27

Having a price point of $2,000 and an estimate of 500 units sold on the first year, the

company will have a loss of $500,000 on the first year. With an estimated sales increase of 20%

every year or 5% every quarter, the company is estimated to gain profit

the fifth year. (Shown in table 8)

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

$1,200,000

$1,400,000

$1,600,000

$1,800,000

Cost

s

-$1,000,000

$0

$1,000,000

$2,000,000

$3,000,000

$4,000,000

$5,000,000

1

Sale

s

Revenue


Initial Investment Requirement of MI Detector device

27: Yearly Model of MI Detector device



every year or 5% every quarter, the company is estimated to gain profit of almost $2.5 million on

Fixed Cost Variable Cost Investment

Initial Investment

2 3 4

Year

Yearly ModelRevenue Total Cost Profit and Loss


Initial Investment Requirement of MI Detector device



of almost $2.5 million on

5


Table 8: The break-even table of MI Detector device

2009 2010 2011 2012 2013 Number of Units sold 520 1200 1440 1730 2100 Price $2,000 $2,000 $2,000 $2,000 $2,000 Revenue $1,040,000 $2,400,000 $2,880,000 $3,460,000 $4,200,000 Fixed Cost $676,000 $647,000 $674,300 $702,965 $733,063 Variable Cost $850,000 $820,000 $835,750 $852,288 $869,652 Total Cost $1,526,000 $1,467,000 $1,510,050 $1,555,253 $1,602,715 Profit and Loss -$486,000 $933,000.00 $1,369,950.00 $1,904,748 $2,597,285

k. Personnel

This project only justifies the requirements of the software development team of SMART

Medical Devices. This project is required to develop the smart analytical software of the MI

Detector device. The software development team of the company consists of five software

engineers with varying levels, and a department manager. The members of the development

team are currently employed at SMART Medical Devices.

A department manager will oversee the development of the project and needs to have

skills and knowledge to drive the project forward. The department manager should have an

experience in project management and can deal well with different levels of the company.

There are three level 4 to 5 software engineers with the following minimum

qualifications to develop the project:

• At least 5 years experience

• Have a BS or MS degree in Computer Engineering/Electrical

Engineering/Mechanical Engineering/Biomedical Engineering

• Experience in hardware and software development


Two level 1 or 2 software engineers are also employed for this project to help in

developing the algorithm of the MI Detector. Minimum qualifications for these positions are the

following:

• 1 or 2 years experience in software and/or hardware projects

• BS Degree in Computer Engineering/Electrical Engineering/Mechanical

Engineering/Biomedical Engineering

• Experience in software language

• Able to work without supervision

l. Business and Revenue Model

SMART Medical Devices have an established sales and marketing team. The device will

be sold to patients through direct and indirect sales. The device will be marketed initially in the

United States after obtaining the necessary permit from the FDA. SMART Medical Devices will

use direct marketing techniques through advertising and attending conventions and events.

SMART Medical Devices will also utilize the well-established sales force of the company and

market the device through contract sales.

The company website will also feature the MI Detector device and consumers will be

able to purchase the device online. Marketing materials will also be posted to educate consumers

on the features and benefits of MI Detector.

The company will approach insurance companies, for greater acceptance by the consumer

and having an insurance reimbursement codes will greatly increase the chances of selling the

device. Even though the device will be covered by the insurance, consumers will be willing to

pay, out-of-pocket, for the MI Detector due to the great benefits it can provide.


The company elected to manufacture the device through an OEM company that are

reputed for their quality-made medical devices. Having an OEM company manufacturer, the

device is expected to minimize the cost and increases profit to the company.

The MI Detector will have an MSRP of $2,000 which is comparable or well below the

price of the competitor. Other accessories of the device will also be sold with a less or

comparable prices.

m. Strategic Alliances/Partners

SMART Medical Device Company will partner with physicians to develop the MI

Detector. Having the inputs and comments of doctors during the design phase can greatly

leverage the device to be the best diagnostic device in diagnosing myocardial infarction.

Partnership with a well-known hospital can be a great promotional tool and can provide good

benefits in both ends. Consumers take medical advices from well known institutions and

medical institutions can give a technological achievement when a medical device proves to be

effective and efficient.

Also, a well-established partnership with the OEM manufacturer is in place to deliver the

best diagnostic device in the market.

n. Profit and Loss

The company will outsource the manufacturing of the MI Detector ECG device to

minimize its expenses. Majority of the fixed costs are attributed to the salary of the software

development team. It will also cover for the expenses to develop the smart algorithm of the ECG

device. Those expenses consist of the hardware equipments needed and other business expenses

such as utilities and rent. The rest of the expenses are expected to be variable, giving the

company to expand as needed.


i. Demand Assumptions

The SMART Medical Devices considers 100% of all patients with myocardial infarction

as a potential customer of MI Detector device.

• In 2009, there is an estimated 7.9 million Americans with myocardial infarction

condition

• An annual incidence of 610,000 and 325,000 new and recurrent attacks of myocardial

infarction

• Every 34 seconds, an American will suffer myocardial infarction

• 25% of men and 38% of women die within the first year of having the diagnosed with

myocardial infarction; 50% of men and women age under 65, will die within 8 years.

ii. Product Assumptions

In the initial quarter, no products will be sold in the market, since the MI Detector device

is still in development phase. It is expected to be in the market on the third quarter of 2009. An

initial sale of 500 units in the first year is expected and is conservatively anticipated to grow 5%

per quarter or 20% per year. Table 9 and figure 28, illustrates the quarterly model with 5%

quarterly growth.

With the assumption that there are no unforeseen circumstances, the company is expected

to break even on the third quarter of its release. On the second year, it is expected that the

demand will pick up and will see the continued growth and success of MI Detector ECG device.

Table 9: The quarterly model of SMART Medical Devices

Q2 2009 Q3 2009Number of Units sold

0

Price $2,000.00 $2,000.00Revenue $0.00 $500,000.00Fixed Cost $225,000.00 $225,000.00Variable Cost

$285,000.00 $285,000.00

Total Cost $510,000.00 $510,000.00Profit & Loss

-$510,000.00 -$10,000.00

Figure 28: The quarterly model of SMART Medical Devices

o. Exit Strategy

The company will lose revenue on the first year due to the development phase and initial

introduction of MI Detector to the market. The company is estimated to be profitable from the

first year to the fifth year, and beyond.

Even though the company expects to be profitable on the third quarter of selling the

device, unforeseen circumstances may arise and can alter the financial forecast of the company.

If the company does not make any profits

management may elect to discontinue marketing and selling the device.

-$600.00

-$400.00

-$200.00

$0.00

$200.00

$400.00

$600.00

$800.00

1$ Th

ousa

nds

Revenue


The quarterly model of SMART Medical Devices

Q3 2009 Q4 2009 Q1 2010 Q2 2010 250 265 275 290

$2,000.00 $2,000.00 $2,000.00 $2,000.00 $500,000.00 $530,000.00 $550,000.00 $580,000.00 $609,000.00$225,000.00 $225,000.00 $225,000.00 $225,000.00 $225,000.00$285,000.00 $285,000.00 $205,000.00 $205,000.00 $205,000.00

$510,000.00 $510,000.00 $430,000.00 $430,000.00 $430,000.00$10,000.00 $20,000.00 $120,000.00 $150,000.00 $179,000.00

The quarterly model of SMART Medical Devices

revenue on the first year due to the development phase and initial


to the fifth year, and beyond.



ny does not make any profits from this device, SMART Medical Devices’

management may elect to discontinue marketing and selling the device.

2 3 4 5 6

Quarterly

Break Even AnalysisRevenue Total Cost Profit & Loss


Q3 2010 Q4 2010 305 320

$2,000.00 $2,000.00 $609,000.00 $639,450.00 $225,000.00 $225,000.00 $205,000.00 $205,000.00

$430,000.00 $430,000.00 $179,000.00 $209,450.00

revenue on the first year due to the development phase and initial




m this device, SMART Medical Devices’

7


Since SMART Medical Devices developed a smart analytical tool to analyze ECG data, the

company will sell the copyrights of the software algorithm to established portable ECG

manufacturing company to get back the investment made for MI Detector. The company may

also elect to market only the smart analytical software to patients with existing portable ECG

devices. Having smart analytical software installed in their portable ECG devices can greatly

increase the efficacy of those machines to detect myocardial infarction. The other appropriate

exit stratergy will be market the smart algorithm as add-on for computer programs and mobiles

and market and sell the smart algorithm as a healthcare application.

XI. Future Directions

We have developed a ST-change detection program that can accurately analyze ECG data

and can determine if it contains any traits of ST-segment elevation or depression and based on

the analysis predicatively warn onset of myocardial infarction. This project, we believe, can

greatly decrease the morbidity and mortality of patients that are prone to have myocardial

infarction, as well as, those that are suffering from it.

Although the project is successful in diagnosing myocardial infarction, there are still many

issues that need to be addressed. The project was developed using datasets gathered from the

Physiobank database. Applying the project directly to patients and the ability to analyze data in

real-time can help us better understand the myocardial infarction condition which can lead to

improved analytical decisions. This can be done using the Real Time Toolbox that is present in

Matlab 7.1 software, the connection to the ECG machine can be made through a USB port and

the real-time data can be captured and analyzed. With an addition of few hardware components it

will be possible to have a standalone bedside ST-change monitoring device.


The project was designed using the Matlab software. The group wanted to port the program

to a portable ECG device that can be comfortably worn by patients for a prolonged period of

time. This can effectively and efficiently diagnose the patient and eliminate missed diagnosis.

Another future consideration is by having wireless communication capability to the portable

ECG device. Personal area network (PAN) such as Bluetooth was considered to create a

wireless probe that connects wirelessly to a base ECG device. The ECG device is then

connected wirelessly to the hospital or doctor through wide area network (WAN) such wireless

internet or WI-FI. Telecardiology is rising in popularity and incorporating wireless internet to

the ECG device can provide long distance diagnosis to the patients by their physician and give

patients the freedom to roam outside the reach of hospitals. With telecardiology in mind, an

ECG with a GPS feature can give patients an additional security.

XII. Conclusion

The algorithm created in MATLAB was successful in detecting the different segments of

ECG signal from the Physiobank database. The QRS complex was detected and was used to

identify the ST-segment. The code was able to detect the abnormalities in the ST segment with

high accuracy. It was also successful in eliminating noises and baseline drifts that can degrade

the accuracy of the algorithm.

With the use of biorthogonal wavelets the ECG signal processing was made faster so that

when real time ECG signal is fed to the algorithm, the processing of the ECG signal and the

resultant warning in the case of abnormality will be close to the actual signal. The algorithm was

successful in identifying ST-segment changes/abnormalities for single lead ECG signal.


Incorporating this algorithm into a 12-lead ECG monitoring system will make a standalone

myocardial infarction detection device.


XIII. References

Albrecht P. (1983). “S-T segment characterization for long-term automated ECG analysis”. M.S. Thesis, MIT Dept. of Electrical Engineering and Computer Science, (1983)

American Heart Association. (2002). Heart Disease and Stroke Statistics - 2003 Update. Dallas, TX: American Heart Association.






American Heart Association (2008). “What is a Heart Attack?” Life after Heart Attack, Diseases and Conditions, American Heart Association (2008). Retrieved on November 6th 2008 from http://www.americanheart.org/presenter.jhtml?identifier=3038238


American Hospital Directory - Hospital Statistics by State. (2008, August 6). Retrieved March 28, 2009, from http://ahd.com/state_statistics.html

Barron Jon (2007). “Secrets of the Heart”. Baseline of Health Foundation (2007). Retrieved on

6th April 2009 from http://www.jonbarron.org/heart-health-program/07-02-2007.php Bhatia Praval, Boudy Jerome, Varejao Rodrigo (2006). “Wavelet transformation and pre-

selection of mother wavelets for ECG signal processing” Proceedings of the 24th IASTED International Multi-conference. Biomedical Engineering February 2006.

Cardiovascular Consultants (2006). “ Physiology”. Retrieved on March 28th 2009 from

http://www.cardioconsult.com/Physiology/ Coronary Artery Disease (2008). Up to Date. Retrieved on 07th December 2008 from

http://www.uptodate.com/patients/content/images/card_pix/Coronary_artery_disease.jpg

http://www.americanheart.org/presenter.jhtml?identifier=3038238

http://ahd.com/state_statistics.html

http://www.jonbarron.org/heart-health-program/07-02-2007.php

http://www.cardioconsult.com/Physiology/

http://www.uptodate.com/patients/content/images/card_pix/Coronary_artery_disease.jpg


Demand for Home Medical Equipment to Exceed $10 b. (n.d.). Retrieved March 28, 2009, from www.expresshealthcaremgmt.com/200903/market16.shtml

Fawcett Tom (2005). “An introduction to ROC Analysis”. Pattern Recognition letters 2006, Issue # 27. Pages: 861-874. Retrieved on April 3rd 2009 from http://www.csee.usf.edu/~candamo/site/papers/ROCintro.pdf

Franc Jager, Alessandro Taddei, George B. Moody, Michele Emdin, Gorazd Antolic, Roman Dorn, Ales Smrdel, Carlo Marchesi, and Roger G. Mark. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Medical & Biological Engineering & Computing 41(2):172-183 (2003)

Goldberger, A. L.,Amaral, L. A. N.,Glass, L., Hausdorff, J. M. and Ivanov, P. Ch.,Mark, R. G.,

Mietus, J. E., Moody, G. B., Peng, C.-K., Stanley, H. E.(2000). “{PhysioBank, PhysioToolkit, and PhysioNet}: Components of a New Research Resource for Complex Physiologic Signals" June 13th 2000. Journal: Circulation, Volume: 101, Issue # 23. Pages: e215--e220. Retrieved on 22nd November 2008 from http://circ.ahajournals.org/cgi/content/full/101/23/e215

Heart Information Center (2006). “Anatomy of Heart”. Texas Heart Institute (July 2006).

Retrieved on 17th March 2009 from http://www.texasheart.org/HIC/ANATOMY/anatomy2.cfm

Heart Information Center (2006). “Bundle Branch Block”. Texas Heart Institute (February 2009). Retrieved on 25th March 2009 from http://www.texasheart.org/HIC/Topics/Cond/bbblock.cfm

Jouck. P.P.H. (2004). “Application of the Wavelet Transform Modulus Maxima to the T-wave

Detection in Cardiac Signals” December 2004. Retreived on 17th March 2009 from http://www.personeel.unimaas.nl/Westra/PhDMaBa-teaching/GraduationStudents/PJouck2004/PJouck2004verslag.pdf

Klabunde, R. E. (2007, April 6). ECG Introduction. Retrieved April 4, 2009, from

http://www.cvphysiology.com/Arrhythmias/A009.htm Klatt E.C. (2008). “Myocardial Infarction” The University of Utah Eccles Health Sciences

Library (2008). Retrieved on 29th March 2009 from http://library.med.utah.edu/WebPath/TUTORIAL/MYOCARD/MYOCARD.html

Lowry, Richard (2008). “Simple ROC Curve Analysis: Version 1”. VassarStat: Web Site for

Statistical Computation 2001-2009. Retrieved on April 3rd 2009 from http://faculty.vassar.edu/lowry/roc1.html#down

Milosavljević Nebojša., Petrović Aleksandar. (2006). “ST Segment Change Detection by Means

of Wavelets”. 8th Seminar on Neural Networks Applications in Electrical Engineering, NEUREL-2006. http://www.ewh.ieee.org/reg/8/conferences.html.

http://www.expresshealthcaremgmt.com/200903/market16.shtml

http://www.csee.usf.edu/~candamo/site/papers/ROCintro.pdf

http://circ.ahajournals.org/cgi/content/full/101/23/e215

http://www.texasheart.org/HIC/ANATOMY/anatomy2.cfm

http://www.texasheart.org/HIC/Topics/Cond/bbblock.cfm

http://www.personeel.unimaas.nl/Westra/PhDMaBa-teaching/GraduationStudents/PJouck2004/PJouck2004verslag.pdf

http://www.personeel.unimaas.nl/Westra/PhDMaBa-teaching/GraduationStudents/PJouck2004/PJouck2004verslag.pdf

http://www.cvphysiology.com/Arrhythmias/A009.htm

http://library.med.utah.edu/WebPath/TUTORIAL/MYOCARD/MYOCARD.html

http://faculty.vassar.edu/lowry/roc1.html#down

http://www.ewh.ieee.org/reg/8/conferences.html


Moody GB, Muldrow WE, Mark RG. A noise stress test for arrhythmia detectors. Computers in

Cardiology 1984; 11:381-384.

Myocardial Infarction (2009). “Myocardial Infarction”. The Free Dictionary by Farlex. Retrieved on 29th March 2009 from http://medical-

dictionary.thefreedictionary.com/Myocaridal+infarction">myocardial infarction</a> O'Grady, M. R., & O'Sullivan, M. L. (n.d.). VetGo Cardiology. Retrieved April 4, 2009, from

http://www.vetgo.com/cardio/concepts/concsect.php?conceptkey=165 Pan Jiapu., Tompkins Willis. J. (1985). “A Real-Time QRS Detection Algorithm”. IEEE

Transactions on Biomedical Engineering, Vol. BME-32, Issue #3, March 1985, Pages:230-236.

Polikar Robi (2001). “The Engineers ultimate guide to wavelet analysis”. The Wavelet Tutorial.

College of Engineering, Rowan University (2001). Retrieved on 5th April 2009 from http://users.rowan.edu/~polikar/WAVELETS/WTtutorial.html

Poul-Erik Paulev(2000). “Textbook in Medical Physiology and Pathophysiology Essentials and Clinical Problems- Chapter 10: Cardiac Performances and Disorders”. Copenhagen Medical Publishers 1999-2000. Retrieved on 29th March 2009 from http://www.mfi.ku.dk/ppaulev/chapter10/chap_10.htm

Samer Garas, Maziar Zafari (2008). “Myocardial Infarction”. EMedicine from WebMD (August 2008). Retrieved on 27th March 2009 from http://emedicine.medscape.com/article/155919-overview

Saritha.C., Sukanya. V., Murthy. Narasimha. Y. (2008) . “ECG Signal Analysis using Wavelet

Transforms”. Journal of Bulg. J. Physics(2008). Issue 35. Pages: 68-77. Schniter Phil (2005). “Continous Wavelet Transform”. Creative Commons Attribution License.

Connexious module: m10418. Version: 2:13. June 9th, 2005. Retrieved on April 5th 2009 from http://cnx.org/content/m10418/latest/

Sivannarayana N., Reddy D.C. (1999). “Biorthogonal Wavelet Transforms for ECG Parameters

Estimation”. Journal Medical Engineering and Physics (1999) Issue: 21Pages: 167-174. Taddei, A., Distante, G., Emdin, M., Pisani, P., Moody, G.B., Zeelenberg, C., and Marchesi, C.

The European ST-T Database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European Heart Journal 13: 1164-1172 (1992).

The Myocardial Infarction –Heart Attack (2006). The Causes: The Myocardial Infarction: The

Cardiovascular Diseases. Heart and Vessels (July 2006). Retrieved on 29th March 2009 from http://www.heart-vessels.com/cardiovascular-diseases/heart-attack-myocardial-infarction6.php

http://medical-/

http://medical-/

http://www.vetgo.com/cardio/concepts/concsect.php?conceptkey=165

http://users.rowan.edu/~polikar/WAVELETS/WTtutorial.html

http://www.mfi.ku.dk/ppaulev/chapter10/chap_10.htm

http://emedicine.medscape.com/article/155919-overview

http://emedicine.medscape.com/article/155919-overview

http://cnx.org/content/m10418/latest/

http://www.heart-vessels.com/cardiovascular-diseases/heart-attack-myocardial-infarction6.php

http://www.heart-vessels.com/cardiovascular-diseases/heart-attack-myocardial-infarction6.php


Tompkins Willis. J. (2000). “Biomedical Digital Signal Processing”. Chapter 13: ECG Analysis

System. Prentice Hall (2000). Pages: 271-272. VIBES Electrocardiogram Mosaic. (n.d.). Retrieved April 4, 2009, from

http://www.vanth.org/vibes/electro.html Wenli Chen., Zhiwen Mo., Wen Guo. (2007) “Detection of QRS Complexes Using Wavelet

Transforms and Golden Section Search” Advances in Intelligent Systems Research ISKE-2007 Proceedings. Retrieved on March 18th 2009 from http://www.atlantis-press.com/php/download_paper.php?id=1195.

Wolfram Research (2009) . “Biorthogonal Wavelets”. Documentation: Wavelet Explorer:

Fundamentals of Wavletes. Retrieved on April 8th 2009 from http://documents.wolfram.com/applications/wavelet/FundamentalsofWavelets/1.6.1.html

World Heart Federation (2009). Cardiovascular Diseases.

http://www.world-heart-federation.org/cardiovascular-health/heart-disease/different-heart-diseases/#c434

Yanowitz G.Frank (2006). “IX. Myocardial Infarction”. The Alan E. Lindsay ECG Learning Center in Cyberspace. Spencer S. Eccles Health Sciences Library University of Utah Health Services Center (2006). Retrieved on November 6th 2008 from http://library.med.utah.edu/kw/ecg/ecg_outline/Lesson9/index.html#Intro

http://www.vanth.org/vibes/electro.html

http://www.atlantis-press.com/php/download_paper.php?id=1195

http://www.atlantis-press.com/php/download_paper.php?id=1195

http://documents.wolfram.com/applications/wavelet/FundamentalsofWavelets/1.6.1.html



http://library.med.utah.edu/kw/ecg/ecg_outline/Lesson9/index.html#Intro


Appendix A

Code for Matlab Program

clear all;

clc;

close all;

%*************************************************************** % Get Data & User Inputs %*************************************************************** Fileloc = 'C:\MATLAB7\Work\';

Filename = input('Enter ECG File Name = ','s'); % Input Filename

Headerfile = strcat(Filename,'.hea'); % Header In TXT

Format

load (Filename); % .mat file for Data

%*************************************************************** % Load Header Data %*************************************************************** fprintf(1,'\nK> Loading Data from Header File %s ...\n', Headerfile);

signalh = fullfile(Fileloc, Headerfile);

fid1 = fopen(signalh,'r');

z = fgetl(fid1);

A = sscanf(z, '%*s %d %d',[1,2]);

nosig = A(1); % Number Of Signals

sfreq = A(2);

clear A;

z = fgetl(fid1);

A = sscanf(z, '%*s %*d %d %d %d %d',[1,4]);

gain = A(1); % Integers Per mV

clear A;


S = sfreq*60;

counter1=0;

counter2=0;

counter3=0;

for n = 0:5

tic

j = S*n+1:1:S*(n+1);

D = val(j);

dat = length (D);

k = 1:1:dat;

D = D(k)/gain;

%*************************************************************** %Signal filter and Base line wander correction %*************************************************************** D= transpose (D);

windowSize = 5;

filsig = filter (ones(1,windowSize)/windowSize,1,D);

y = medfilt1(filsig,200); % 1st median filter

s1 = y;

clear y;

y = medfilt1(s1,600); % 2nd median filter

D = filsig - y;

clear s1;

clear y;

pack;

%***************************************************************% Manipulate Data So We Only Look At What The User Wants %*************************************************************** D = transpose (D);


D = cwt (D, 1:4, 'bior2.4'); %Performing Continuous Wavelet

Transform using Biorthogonal

Wavelet to ECG_1

D = transpose (D);

x = D (:,4);

clear D;

%*************************************************************** % R-Peak Detection %*************************************************************** thresh = 0.6;

% create time axis

len = length (x);

tt = 1/sfreq:1/sfreq:ceil(len/sfreq);

t = tt(1:len);

max_h = max (x(round(len/4):round(3*len/4)));%segment search

area first find the

highest bumps in

the ECG_1

poss_reg = x>(thresh*max_h); %then build an array of segments to

look in

%find indices into boudaries of

each segment

left = find(diff([0 poss_reg'])==1); % remember to zero pad at

start

right = find(diff([poss_reg' 0])==-1); % remember to zero pad at

end

%loop through all possibilities

for(i=1:length(left))

[maxval(i) maxloc(i)] = max( x(left(i):right(i)) );


[minval(i) minloc(i)] = min( x(left(i):right(i)) );

maxloc(i) = maxloc(i)-1+left(i); % add offset of present

location

minloc(i) = minloc(i)-1+left(i); % add offset of present

location

end

R_index = maxloc;

R_t = t(maxloc);

R_amp = maxval;

%*************************************************************** % Heart Rate Calculation %*************************************************************** for j = 2:length (R_t)

HR(j)= R_t(j)-R_t(j-1);

end

H_R = 60/(mean (HR));

fprintf (1,'\nK> Heart Rate is %d \n',H_R);

%*************************************************************** % S-Point Detection %*************************************************************** R_len= length (R_index);

for j = 1:R_len

IR1 = R_index(j);

for i = IR1:IR1+ (round(sfreq*0.03)*(H_R/72))

if i == length (x)|i==0

S_index(j)= 1;

S_amp(j) = x(1,1);

S_t(j) = t(1,1);

break

end


if x(i,1)< x(i+1,1) && x(i,1)< x(i-1,1)

S_index(j)= i;

S_amp(j) = x(i,1);

S_t(j) = t(1,i);

break

end

end

end

%*************************************************************** % Q-Point Detection %*************************************************************** for j = 1:R_len

IR1 = R_index(j);

for i = IR1:-1:IR1- (round(sfreq*0.03 *(H_R/72)))

if i == 0|i==length (x)

Q_index(j)= 1;

Q_amp(j) = x(1,1);

Q_t(j) = t(1,1);

break

end

if x(i,1)< x(i+1,1) && x(i,1)< x(i-1,1)

Q_index(j)= i;

Q_amp(j) = x(i,1);

Q_t(j) = t(1,i);

break

end

end

end


%*************************************************************** % J-Point Detection %*************************************************************** S_len = length (S_index);

for j = 1:S_len

IS1= S_index(j);

for i=IS1:IS1+ (round(sfreq*0.03) *(H_R/72))

if i==0

J_index(j)=1;

J_amp(j)= x(1,1);

J_t(j)= t(1,1);

break

end

if i> length (x)

break

end

if x(i,1)>=0

J_index(j)=i;

J_amp(j)= x(i,1);

J_t(j)= t(1,i);

break

end

end

end

%*************************************************************** % T-Peak Detection %*************************************************************** J_len = length (J_index);

for j = 1: J_len

P1 = R_index(j)+ (round(sfreq*0.4) *(H_R/72));


P2 = J_index (j)+ (round(sfreq*0.08) *(H_R/72));

if P1> length (x)|P2> length (x)

break

end

if P1 > P2

[T_peak(j),T_peak_index(j)] = max(x(P2:P1));

T_peak_index(j) = T_peak_index(j)+ P2;

else



end

T_peak_t (j)= t(T_peak_index (j));

end

%*************************************************************** % T-Point (T onset) Detection via T-peak %*************************************************************** T_len = length (T_peak_index);

for j = 1:T_len

IT1 = T_peak_index(j);

for i = IT1:-1:IT1-(round(sfreq*0.035) *(H_R/72))

TP_index(j)=i;

TP_amp(j)=x(i,1);

TP_t(j)=t(1,i);

end

end


%*************************************************************** % K-Point %*************************************************************** Q_len = length (Q_index);

for j = 1:Q_len

IQ1 = Q_index(j);

for i=IQ1:-1:IQ1- (round(sfreq*0.03) *(H_R/72))

if i == 0

K_index(j) = 1;

K_amp(j)= x(1,1);

K_t(j)=t(1,1);

break

end

if x(i,1)>=0

K_index(j) = i;

K_amp(j)= x(i,1);

K_t(j)=t(1,i);

break

end

end

if i == 0

K_index(j) = 1;

K_amp(j)= x(1,1);

K_t(j)=t(1,1);

break

end

if x(i,1)< x(i+1,1) && x(i,1)< x(i-1,1)

K_index(j) = i;

if K_index(j)==0


K_index(j)=1;

end

K_amp(j)= x(i,1);

K_t(j)=t(1,i);

end

end

%*************************************************************** % P-Point Detection via K+80ms %***************************************************************K_len = length (K_index); for j = 1:K_len

IK1 = K_index(j);

for i=IK1:-1:IK1- (round(sfreq*0.08) *(H_R/72))

if i ==0

P_index(j) = 1;

P_amp(j)= x(1,1);

P_t(j)=t(1,1);

break

end

P_index(j) = i;

P_amp(j)= x(i,1);

P_t(j)=t(1,i);

end

end


%*************************************************************** % Calculation of Isoelectric Line %***************************************************************j = 1:1:K_len; ISO(j) = mean(x(P_index(j):K_index(j)));

%*************************************************************** % Calculation of ST-segment %*************************************************************** a = length (J_index);

b = length (TP_index);

if a==b;

j = 1:1:J_len;

ST(j) = mean(x(J_index(j):TP_index(j)));

end

if a>b

j = 1:1:b;


end

if a<b

j = 1:1:a;


End

%*************************************************************** % Comparison of ISO and ST %*************************************************************** a = length (ISO);

b = length (ST);

if a==b

for j = 1:a

counter1=counter1+1;

if (ISO(j))>= (ST(j)+0.0001) && ISO(j)>=ST(j)-0.0001|ISO(j)==ST(j)



else


end

end

end

if a<b

for j=1:a


if ISO(j)>=ST(j)+0.0001 && ISO(j)>=ST(j)-0.0001|ISO(j)==ST(j)


else


end

end

end

if a>b

for j=1:b


if ISO(j)>=ST(j)+0.0001 && ISO(j)>=ST(j)-0.0001|ISO(j)==ST(j)


else


end


end

end

clear ISO;

clear ST;

toc

fprintf(1,'\nK> %d loop completed %n \n',n);

end

fprintf (1,'\nK> total number of signals evaluated is %d \n',counter1)

fprintf (1,'\nK> total number of signals without MI is %d \n',counter2)

fprintf (1,'\nK> total number of signals with MI is %d \n',counter3)

if counter3/counter1>=0.95

fprintf(1,'\nK>WARNING: MI\n');

else

fprintf(1,'\nK>No MI\n');

end

%*************************************************************** %Plotting Function %*************************************************************** figure

subplot(2,1,1)

plot(t,x), grid on;

title('Level 4 2^4 Biorthogonal Wavelet Transformed ECG Signal')

ylabel('ECG')

subplot(2,1,2)

plot(t,x,'b');hold on;

plot(S_t,S_amp,'+r'), grid on; hold on;

plot(R_t,R_amp,'+k'); hold on;


plot (Q_t, Q_amp, '+g'); hold on;

plot (T_peak_t,T_peak, '+y');hold on;

plot (TP_t,TP_amp, '+m');hold on;

plot (J_t,J_amp,'+c');hold on;

plot (K_t,K_amp,'*r');hold on;

plot (P_t,P_amp,'*m');

title('Biorthogonal Wavelet Transformed ECG Signal with Q-Peaks (green), R-Peaks (black),S-Peaks (red)')

ylabel('ECG+S+R+Q+P+J')

hold off;

fprintf(1,'\nK> Analysis Complete \n');

%*************************************************************** % End of Code %***************************************************************


Appendix B he Graphical user inerface for the software code developed is as follows:

function varargout = GUISTchange(varargin)

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @GUISTchange_OpeningFcn,

...

'gui_OutputFcn', @GUISTchange_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin && ischar(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State,

varargin{:});

else

gui_mainfcn(gui_State, varargin{:});


end

%***************************************************************

% Get Data & User Inputs

%***************************************************************

function edit1_Callback(hObject, eventdata, handles)

Fname = get(hObject,'string');

handles.Filename = Fname;

guidata(hObject, handles);

function edit2_Callback(hObject, eventdata, handles)

handles.Filename = '';

handles.Result = '';

handles.output = hObject;


function varargout = GUISTchange_OutputFcn(hObject, eventdata,

handles)

varargout{1} = handles.output;

Fileloc = 'C:\MATLAB7\Work\';

Headerfile = strcat(handles.Filename,'.hea'); % Header

In TXT Format


load (handles.Filename); % .mat file for Data

%***************************************************************

% Load Header Data

%***************************************************************

signalh = fullfile(Fileloc, Headerfile);

fid1 = fopen(signalh,'r');

z = fgetl(fid1);

A = sscanf(z, '%*s %d %d',[1,2]);

nosig = A(1); % Number Of Signals

sfreq = A(2);

clear A;

z = fgetl(fid1);

A = sscanf(z, '%*s %*d %d %d %d %d',[1,4]);

gain = A(1); % Integers Per mV

clear A;

S = sfreq*60;

counter1=0;

counter2=0;


counter3=0;

for n = 0:4

j = S*n+1:1:S*(n+1);

D = val(j);

dat = length (D);

k = 1:1:dat;

D = D(k)/gain;

%***************************************************************

%Signal filter and Base line wander correction

%***************************************************************

D= transpose (D);

axes(handles.axes1)

plot (D,'g');

title ('\it{Original Signal for ECG}');

ylabel ('Amplitude in Volts');

xlabel ('# of Samples');

%set(handles.axes1,'XMinorTick','on');

set(handles.axes1,'Color','k');


drawnow;

windowSize = 5;

filsig = filter (ones(1,windowSize)/windowSize,1,D);

y = medfilt1(filsig,200); % 1st median filter

s1 = y;

clear y;

y = medfilt1(s1,600); % 2nd median filter

D = filsig - y;

axes(handles.axes2)

plot (D,'g');

title ('\it{Filtered Baseline Wander Corrected Signal for

ECG}');


xlabel ('# of Samples');


drawnow;

clear s1;

clear y;


pack;

%***************************************************************

% Manipulate Data So We Only Look At What The User Wants

%***************************************************************

D = transpose (D);

D = cwt (D, 1:4, 'bior2.4'); %Performing Continuous Wavelet

Transform using Biorthogonal Wavelet to ECG_1

D = transpose (D);

D_1 = D (:,1);

D_2 = D (:,2);

D_3 = D (:,3);

x = D (:,4);

clear D;

%***************************************************************

% R-Peak Detection

%***************************************************************

thresh = 0.6;

% create time axis

len = length (x);


tt = 1/sfreq:1/sfreq:ceil(len/sfreq);

t = tt(1:len);

max_h = max (x(round(len/4):round(3*len/4)));%segment search

area first find the highest bumps in the ECG_1

poss_reg = x>(thresh*max_h); %then build an array of segments to

look in

left = find(diff([0 poss_reg'])==1); % remember to zero pad at

start

right = find(diff([poss_reg' 0])==-1); % remember to zero pad at

end

for(i=1:length(left))

[maxval(i) maxloc(i)] = max( x(left(i):right(i)) );

[minval(i) minloc(i)] = min( x(left(i):right(i)) );

maxloc(i) = maxloc(i)-1+left(i); % add offset of present

location

minloc(i) = minloc(i)-1+left(i); % add offset of present

location

end

R_index = maxloc;


R_t = t(maxloc);

R_amp = maxval;

%***************************************************************

% Heart Rate Calculation

%***************************************************************

for j = 2:length (R_t)

HR(j)= R_t(j)-R_t(j-1);

end

H_R = 60/(mean (HR));

%***************************************************************

% S-Point Detection

%***************************************************************

R_len= length (R_index);

for j = 1:R_len

IR1 = R_index(j);

for i = IR1:IR1+ (round(sfreq*0.03*(H_R/72)))

if i == length (x)|i==0

S_index(j)= 1;

S_amp(j) = x(1,1);


S_t(j) = t(1,1);

break

end

if x(i,1)< x(i+1,1) && x(i,1)< x(i-1,1)

S_index(j)= i;

S_amp(j) = x(i,1);

S_t(j) = t(1,i);

break

end

end

end

%***************************************************************

% Q-Point Detection

%***************************************************************

for j = 1:R_len

IR1 = R_index(j);

for i = IR1:-1:IR1- (round(sfreq*0.03*(H_R/72)))

if i == 0|i==length (x)|i-1==0


Q_index(j)= 1;

Q_amp(j) = x(1,1);

Q_t(j) = t(1,1);

break

end

if x(i,1)< x(i+1,1) && x(i,1)< x(i-1,1)

Q_index(j)= i;

Q_amp(j) = x(i,1);

Q_t(j) = t(1,i);

break

end

end

end

%***************************************************************

% J-Point Detection

%***************************************************************

S_len = length (S_index);

for j = 1:S_len


IS1= S_index(j);

foundj = 0;

for i=IS1:IS1+ (round(sfreq*0.03*(H_R/72)))

if i==0

J_index(j)=1;

J_amp(j)= x(1,1);

J_t(j)= t(1,1);

foundj = 1;

break

end

if i > length (x)

break

end

if x(i,1)>=0

J_index(j)=i;

J_amp(j)= x(i,1);

J_t(j)= t(1,i);

foundj = 1;


break

end

end

if foundj == 0

J_index(j)=1;

J_amp(j)= x(1,1);

J_t(j)= t(1,1);

end

end

%***************************************************************

% T-Peak Detection

%***************************************************************

J_len = length (J_index);

for j = 1: J_len

P1 = R_index(j)+ (round(sfreq*0.4*(H_R/72)));

P2 = J_index (j)+ (round(sfreq*0.08*(H_R/72)));

if P1> length (x)|P2> length (x)

break


end

if P1 > P2



else



end

T_peak_t (j)= t(T_peak_index (j));

end

%***************************************************************

% T-Point (T onset) Detection via T-peak

%***************************************************************

T_len = length (T_peak_index);

for j = 1:T_len

IT1 = T_peak_index(j);

for i = IT1:-1:IT1-(round(sfreq*0.035*(H_R/72)))


TP_index(j)=i;

TP_amp(j)=x(i,1);

TP_t(j)=t(1,i);

end

end

%***************************************************************

% K-Point

%***************************************************************

Q_len = length (Q_index);

for j = 1:Q_len

IQ1 = Q_index(j);

foundk = 0;

for i=IQ1:-1:IQ1- (round(sfreq*0.03*(H_R/72)))

if i == 0

K_index(j) = 1;

K_amp(j)= x(1,1);

K_t(j)=t(1,1);

foundk = 1;


break

end

if x(i,1)>=0

K_index(j) = i;

K_amp(j)= x(i,1);

K_t(j)=t(1,i);

foundk = 1;

break

end

end

if foundk == 0

K_index(j)=1;

K_amp(j)= x(i,1);

K_t(j)=t(1,i);

end

end


%***************************************************************

% P-Point Detection via K+80ms

%***************************************************************

K_len = length (K_index);

for j = 1:K_len

IK1 = K_index(j);

for i=IK1:-1:IK1- (round(sfreq*0.08*(H_R/72)))

if i ==0

P_index(j) = 1;

P_amp(j)= x(1,1);

P_t(j)=t(1,1);

break

end

P_index(j) = i;

P_amp(j)= x(i,1);

P_t(j)=t(1,i);

end

end


%***************************************************************

% Calculation of Isoelectric Line

%***************************************************************

j = 1:1:K_len;

ISO(j) = mean(x(P_index(j):K_index(j)));

%***************************************************************

% Calculation of ST-segment

%***************************************************************

a = length (J_index);

b = length (TP_index);

if a==b;

j = 1:1:J_len;


end

if a>b

j = 1:1:b;


end

if a<b


j = 1:1:a;


end

%***************************************************************

% Comparison of ISO and ST

%***************************************************************

a = length (ISO);

b = length (ST);

if a==b

for j = 1:a


if (ISO(j)>= ST(j)+0.0001 && ISO(j)>=ST(j)-

0.0001)|ISO(j)==ST(j)

%fprintf(1,'\nK>No MI\n');


else


end


end

end

if a<b

for j=1:a


if (ISO(j)>=ST(j)+0.0001 && ISO(j)>=ST(j)-

0.0001)|ISO(j)==ST(j)


else


end

end

end

if a>b

for j=1:b



if (ISO(j)>=ST(j)+0.0001 && ISO(j)>=ST(j)-

0.0001)|ISO(j)==ST(j)


else


end

end

end

clear ISO;

clear ST;


fprintf(1,'\nK>WARNING: Possible MI\n');

else

fprintf(1,'\nK>No MI\n');

end

axes(handles.axes3)

plot(t,x,'g');hold on;

plot(S_t,S_amp,'+r'), hold on;


plot(R_t,R_amp,'+w'); hold on;

plot (Q_t, Q_amp, '+b'); hold on;

plot (T_peak_t,T_peak, '+y');hold on;

plot (TP_t,TP_amp, '+m');hold on;

plot (J_t,J_amp,'+c');hold on;

plot (K_t,K_amp,'Marker','+','color',[1,0.4,0.6]);hold on;

plot (P_t,P_amp,'Marker','+','color',[0.4,0,0.6]);

title('Biorthogonal Wavelet Transformed ECG Signal with Q-Peaks

(green), R-Peaks (black),S-Peaks (red)')

ylabel('ECG+S')

hold off;


xlabel ('Time in seconds');


drawnow;

end


fprintf(1,'\nK>WARNING: Call 9-1-1 & Get Help\n');


handles.Result = 'WARNING: Call 9-1-1 & Get Help';

else

fprintf(1,'\nK>No ST-Changes Detected\n');

handles.Result = 'No ST-Changes Detected';

end


set(handles.edit2,'String',handles.Result);

function edit1_CreateFcn(hObject, eventdata, handles)

if ispc

set(hObject,'BackgroundColor','white');

else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundC

olor'));

end

function edit2_CreateFcn(hObject, eventdata, handles)

if ispc

set(hObject,'BackgroundColor','White');


else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundC

olor'));

end

function figure1_ResizeFcn(hObject, eventdata, handles)

%***************************************************************

% End Of Code

%***************************************************************


Appendix C Testing Results

Dataset Name Sample # MI No MI Total # of Signals

MIT-BIH ST Change Database 327m 268 0 268 5 minutes of data tested 325m 375 0 375 324m 248 62 319 323m 375 85 460 322m 254 241 495 321m 378 0 378 320m 0 411 411 319m 133 407 540 318m 565 169 734 317m 144 213 357 316m 115 425 540 315m 379 0 379 314m 181 252 433 313m 105 345 450 312m 375 0 375 311m 83 308 391 310m 0 514 514

European ST Database e0103m 60 245 305 5 minutes of data tested e0105m 0 273 273 e0107m 0 330 330 e0113m 305 0 305 e0111m 310 0 310 e0119m 240 60 300 e0121m 302 77 379 e0123m 299 71 370 e0125m 349 0 349 e0127m 293 74 367 e0133m 265 0 265 e0139m 156 234 390 e0147m 265 0 265 e0155m 203 148 351

MIT-BIH Noise Stress Database 118e00m 0 361 361 5 minutes of data tested 118e06m 0 361 361 118e12m 0 361 361 118e18m 0 361 361 118e24m 0 361 361

Longterm ST Database s20021m 431 0 431 5 minutes of data tested s20111m 456 0 456


s20141m 590 0 590 s20651 530 0 530 s20231m 511 0 511 s20081m 429 89 518 MIT-BIH Normal Sinus Rhythm Database

16265m 0 298 298

5 minutes of data tested 16272m 0 328 328 18184m 292 179 471 19093m 234 68 302 19140m 196 248 444 19830m 0 156 156 16273m 0 279 279 16420m 273 180 453 16483m 290 57 347 16773m 88 324 412 16786m 0 318 318 17453m 171 248 419 18177m 0 175 175

Results with 100% accuracy for MI Results with 100% accuracy for Normal ECG


Appendix D

Receiver Operating Characteristics Curve Datasheet

Sample # MI NMI Total Percentage Correct

Percentage Wrong Databse Name

327m 268 0 268 100 0 MIT-BIH ST Change 325m 375 0 375 100 0 324m 248 62 319 77.74294671 19.43573668 323m 375 85 460 81.52173913 18.47826087 322m 254 241 495 51.31313131 48.68686869 321m 378 0 378 100 0 320m 0 411 411 0 100 319m 133 407 540 24.62962963 75.37037037 318m 565 169 734 76.97547684 23.02452316 317m 144 213 357 40.33613445 59.66386555 316m 115 425 540 21.2962963 78.7037037 315m 379 0 379 100 0 314m 181 252 433 41.80138568 58.19861432 313m 105 345 450 23.33333333 76.66666667 312m 375 0 375 100 0 311m 83 308 391 21.22762148 78.77237852 310m 0 514 514 0 100 e0103m 60 245 305 19.67213115 80.32786885 European ST e0105m 0 273 273 0 100 e0107m 0 330 330 0 100 e0113m 305 0 305 100 0 e0111m 310 0 310 100 0 e0119m 240 60 300 80 20 e0121m 302 77 379 79.68337731 20.31662269 e0123m 299 71 370 80.81081081 19.18918919 e0125m 349 0 349 100 0 e0127m 293 74 367 79.83651226 20.16348774 e0133m 265 0 265 100 0 e0139m 156 234 390 40 60 e0147m 265 0 265 100 0 e0155m 203 148 351 57.83475783 42.16524217

118e00m 0 361 361 0 100 MIT-BIH Noise Stress Test

118e06m 0 361 361 0 100 118e12m 0 361 361 0 100 118e18m 0 361 361 0 100 118e24m 0 361 361 0 100


s20021m 431 0 431 100 0 Longterm ST change s20111m 456 0 456 100 0 s20141m 590 0 590 100 0 s20651 530 0 530 100 0 s20231m 511 0 511 100 0 s20081m 429 89 518 82.81853282 17.18146718 16265m 0 298 298 0 100 Normal Sinus Rhythm 16272m 0 328 328 0 100 18184m 292 179 471 61.99575372 38.00424628 19093m 234 68 302 77.48344371 22.51655629 19140m 196 248 444 44.14414414 55.85585586 19830m 0 156 156 0 100 16273m 0 279 279 0 100 16420m 273 180 453 60.26490066 39.73509934 16483m 290 57 347 83.57348703 16.42651297 16773m 88 324 412 21.3592233 78.6407767 16786m 0 318 318 0 100 17453m 171 248 419 40.81145585 59.18854415 18177m 0 175 175 0 100

Diagnostic Level

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Documents

Early Myocardial Infarction Detection