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
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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
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
Early Myocardial Infarction Detection 2
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
Early Myocardial Infarction Detection 3
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.
Early Myocardial Infarction Detection 4
• 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
Early Myocardial Infarction Detection 5
• 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.
Early Myocardial Infarction Detection 6
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
Early Myocardial Infarction Detection 7
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
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:
Early Myocardial Infarction Detection
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
Early Myocardial Infarction Detection 49
Early Myocardial Infarction Detection 50
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,
Early Myocardial Infarction Detection 51
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
Early Myocardial Infarction Detection 52
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
Early Myocardial Infarction Detection
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 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)
And from the same period, there was an increase of 8.09% of new and recurrent attacks
of myocardial infarction in the United States (Seen in figure 24).
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 (Seen in figure 25).
Early Myocardial Infarction Detection 53
There’s a huge market for portable medical devices that diagnose heart diseases. Heart
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
United States (Seen
An estimate of myocardial infarction prevalence in the United States (Heart Disease
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
Early Myocardial Infarction Detection
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)
Early Myocardial Infarction Detection 54
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
Early Myocardial Infarction Detection 55
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.
Early Myocardial Infarction Detection 56
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
Early Myocardial Infarction Detection 57
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.
Early Myocardial Infarction Detection 58
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
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
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.
m this device, SMART Medical Devices’
7
Early Myocardial Infarction Detection 67
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.
Early Myocardial Infarction Detection 68
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.
Early Myocardial Infarction Detection 69
Incorporating this algorithm into a 12-lead ECG monitoring system will make a standalone
myocardial infarction detection device.
Early Myocardial Infarction Detection 70
XIII. References
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American Heart Association. (2002). Heart Disease and Stroke Statistics - 2003 Update. Dallas, TX: American Heart Association.
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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
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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)
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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
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of Wavelets”. 8th Seminar on Neural Networks Applications in Electrical Engineering, NEUREL-2006. http://www.ewh.ieee.org/reg/8/conferences.html.
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%*************************************************************** % Get Data & User Inputs %*************************************************************** Fileloc = 'C:\MATLAB7\Work\';
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;
Early Myocardial Infarction Detection 75
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);
%***************************************************************% Manipulate Data So We Only Look At What The User Wants %*************************************************************** D = transpose (D);
Early Myocardial Infarction Detection 76
D = cwt (D, 1:4, 'bior2.4'); %Performing Continuous Wavelet
%*************************************************************** % 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
Early Myocardial Infarction Detection 83
%*************************************************************** % 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;
ST(j) = mean(x(J_index(j):TP_index(j)));
end
if a<b
j = 1:1:a;
ST(j) = mean(x(J_index(j):TP_index(j)));
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)
Early Myocardial Infarction Detection 84
counter2=counter2+1;
else
counter3=counter3+1;
end
end
end
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)
counter2=counter2+1;
else
counter3=counter3+1;
end
end
end
if a>b
for j=1:b
counter1=counter1+1;
if ISO(j)>=ST(j)+0.0001 && ISO(j)>=ST(j)-0.0001|ISO(j)==ST(j)
counter2=counter2+1;
else
counter3=counter3+1;
end
Early Myocardial Infarction Detection 85
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