Physiological Signal Simulator Signal Simulator FYP 1. Project Breakdown Figure 1 System Diagram 1.1...
Transcript of Physiological Signal Simulator Signal Simulator FYP 1. Project Breakdown Figure 1 System Diagram 1.1...
Physiological Signal Simulator Méabh Malone 09547771 Electrical & Electronic Engineering College of Engineering and Informatics, National University of Ireland, Galway
FYP
Project Supervisor Dr. John Breslin Submission Date: 19th December, 2012
Physiological Signal Simulator FYP
Abstract
Physiological signal simulators exist in medical training facilities to reproduce the physiological signals of
patients with different diseases. Such a system could potentially monitor heart-rate, blood pressure and
other physiological indicators. However, these systems are very expensive when sold commercially. The
aim of this project is to develop a multi-purpose physiological signal simulator which can be used for
research and development purposes at NUI, Galway. The development of such a system provides a
lower cost solution and will advance the development of iPhone and iPad medical applications at NUI,
Galway. It has the potential to communicate with devices such as phones and monitors to provide data
and results for medical research and technology development.
Physiological Signal Simulator FYP
Table of Contents Abstract .......................................................................................................................................................... 2
Table of figures ............................................................................................... Error! Bookmark not defined.
Introduction ................................................................................................................................................... 5
1. Project Breakdown ................................................................................................................................. 7
1.1 Medical Research and Interview .................................................................................................... 7
1.1.1 Existing Medical Technology: Zigbee ............................................................................................ 7
1.1.2 Interview ....................................................................................................................................... 7
1.2 Physionet.............................................................................................................................................. 9
1.3 LabVIEW ............................................................................................................................................. 10
1.3.1 Advantages of LabVIEW .............................................................................................................. 10
2. Proposals for tackling project .............................................................................................................. 11
3. Progress to date ................................................................................................................................... 12
3.1 Concepts of the ECG .......................................................................................................................... 12
3.1.1 The Heart .................................................................................................................................... 12
3.1.2 ECG .............................................................................................................................................. 13
3.1.3 Sinus Rhythm .............................................................................................................................. 15
3.1.4 Analysis of P-QRS-T Complex ...................................................................................................... 15
3.2 Selecting Signals from Physionet ....................................................................................................... 19
3.3 Reproducing signals with LabVIEW .................................................................................................... 22
3.3.1 Initial LabVIEW Program ............................................................................................................. 22
3.4 UI Design ............................................................................................................................................ 23
3.5 Website .............................................................................................................................................. 27
4. Task List and Project Plan ..................................................................................................................... 27
5. Conclusion ............................................................................................................................................ 27
Bibliography ................................................................................................................................................. 28
References ................................................................................................................................................... 28
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Table of Figures Figure 1 System Diagram .............................................................................................................................. 7
Figure 2 Heart Chambers ............................................................................................................................ 13
Figure 3 Depolarisation of myocyte cells .................................................................................................... 14
Figure 4 Contraction of the myocardium as depolarisation wave moves through heart ........................... 14
Figure 5 Depolarisation and repolarisation wave ....................................................................................... 14
Figure 6 the sinus node ............................................................................................................................... 15
Figure 7 P-QRS-T Complex .......................................................................................................................... 15
Figure 8 the P Wave .................................................................................................................................... 16
Figure 9 QRS Complex ................................................................................................................................. 17
Figure 10 ST Segment.................................................................................................................................. 17
Figure 11 T Wave ........................................................................................................................................ 18
Figure 12 Regions of the heartbeat ............................................................................................................ 19
Figure 13 Screenshot of Normal Sinus Rhythm on Physionet .................................................................... 21
Figure 14 Screenshot of Loop with Delay Block Diagram ........................................................................... 22
Figure 15 Screenshot of Front panel ........................................................................................................... 22
Figure 16 User Interface Design Idea .......................................................................................................... 23
Figure 17 Flowchart of State Machine so far .............................................................................................. 24
Figure 18 Screenshot of "Idle" state Block Diagram ................................................................................... 25
Figure 19 Screenshot of "Load signal" state Block Diagram ....................................................................... 25
Figure 20 Screenshot of "Update graph" state Block Diagram ................................................................... 26
Figure 21 Screenshot of Front Panel UI so far with plot of Arrhythmia samples ....................................... 26
Physiological Signal Simulator FYP
Introduction
Simulation is the imitation of the conditions of a situation, or the representation of the behaviour or
characteristics of one system through the use of another system, called a simulator. Simulators are
designed to reproduce the operations of a complex system and are especially used to produce a
computer model of the process. Simulation can be performed using a hardware model or by running a
software program or through a combination of both.
Simulators are used in training and education as well as to design or develop computer models of
natural and human systems to analyse past events and predict future ones. In communication and
computer network research network simulators are used to predict the behavior of a computer network
by modeling it with different devices and levels of traffic. Performance and efficiency can be then be
analysed and decisions made based on the results. Simulation programmes based on differential
equations can produce mathematical models to predict future events and behaviours such as expected
population growth, global warming, stock rise and fall etc. The use of simulation technology in sports is
having a major impact in optimising the performance of individual athletes and improving the
development of many team sports. A main application of simulators is in the training of a wide range of
professionals for jobs in which real-world training would prove too dangerous and/or costly and many of
which would have to take place in extreme working environments which are almost impossible to
reproduce on an ongoing basis. Both the Military and Marine core recruit and train soldiers using war
simulated environments constructed as video games. Simulators’ are used by Airline pilots in flight
simulation training and by Surgeons and medical professionals to simulate situations and practice
procedures in the hope of reducing error.
The use of simulation technology is vital to the advancement of the world of medicine. Continuous
advancements in technology have resulted in the development of new and better methods for the
teaching and practicing of medicine. One of the key innovations in the field of health care is the use of
medical simulation. The future of medical training relies on this visual - based learning tool. Medical
simulation is a branch of simulation technology involved in education and training across various
medical fields. It combines health care professionals and physicians with industry professionals such as
computer scientists, researchers, educators and engineers. It can involve simulation of human patients
and their physiological signals, educational documents, casualty assessment, military situations and
emergency response.
Physiological Signal Simulator FYP
The use of simulators adds a new dimension to the world of medical training and has many advantages.
Its main purpose in the training of medical professionals is to reduce accidents and percentage error
during surgery, prescription, and general practice. It allows physicians to practice procedures as many
times as they need to without putting the patient at risk.
It puts the student in critical scenarios in which a rapid response is needed. If it was a real-life situation
with a real patient a senior doctor would step in and make the decision but the use of a simulation
environment means errors can be made and allowed to reach their conclusion. The student doctors’ can
then see the results of their decisions and actions. It also allows the educator to control the training
environment and the simulation speed can be varied or stopped to allow for better learning. This new
technology can move medicine from the old method of seeing a procedure once then performing it, to a
new method of seeing it once, practicing it many times, then performing it. Decreasing the percentage
error and increasing percentage of success.
A physiological signal simulator is a system that reproduces and simulates physiological signals (e.g. ECG,
RR, EEG, accelerometer, blood pressure, respiration etc.). They are extremely useful to reproduce the
physiological signals of a patient with different disease characteristics. Such a system could potentially
monitor heart-rate, blood pressure and other physiological indicators. The signals are downloaded from
a database on a website, this allows the doctor to analyse the signals of a “virtual” patient and
eliminates the need of an actual patient to be present when gathering signals.
Physiological simulators exist in medical training facilities and are excellent visual learning tools to assist
trainee medical professionals. However these systems are very expensive when sold commercially. It is
not only medical professionals who need to use these systems.
At the moment there are a number of engineers, researchers, educators and PHD students in NUI,
Galway working on projects involving the simulation and analysis of physiological signals. New software
programs and smart phone applications are being developed in NUI, Galway, many of which need to
simulate physiological signals for patients with specific cardiac profiles (e.g. heart failure NYHA class III,
arrhythmia, etc) as part of the development. But due to the lack of time of the PHD students and
researchers, the quality of the simulated signals used in the development of these applications is poor.
Therefore, a system which could simulate, analyse and provide feedback on physiological signals could
potentially make a significant difference to the research and development of new medical applications
and technology at NUI, Galway.
Physiological Signal Simulator FYP
1. Project Breakdown
Figure 1 System Diagram
1.1 Medical Research and Interview
1.1.1 Existing Medical Technology: Zigbee
Currently the medical device industry uses a wireless communication network called Zigbee to connect
different devices together.
Zigbee technology is the main existing communication technology used in monitoring the physiological
signals of patients. It is a low-cost, low-power, wireless mesh network that addresses the
communication needs of sensor and control networks in a wide range of markets including commercial,
residential, energy, consumer, health and industrial sectors. Zigbee wirelessly enables medical devices
such as vital sign monitors, physiological signal simulators, ventilators and infusion pumps to collect and
store data centrally. The software is designed to be easy to develop on small, inexpensive
microprocessors.
It operates on the ISM (industrial, scientific and medical) radio band width of 868 MHz in Europe and
915MHz in the United States. The data transmission rate varies from 20 – 900 kbps. The disadvantages
of using Zigbee include its low-transmission rate, and also it can only connect and transmit data
between devices over a small distance.
1.1.2 Interview
I hope to gain a professional insight into the practical, everyday use of this system, to gain feedback on
my project and to see if there is potential to improve the development of such systems in the future. I
Physiological Signal Simulator FYP
also want to find out what existing medical technologies are used in different fields of the Irish
Healthcare system. As part of my research I conducted an interview with Orthopaedic surgeon at
Cappagh and Blanchardstown hospitals and lecturer at the College of Surgeons, Dr. Paddy Kenny. I hope
to find out if these types of simulators are used in his area of medicine and are they used as training
tools in Irish Universities, and if not why not. I hope to study and use their feedback, real-life experience
and perspective to get the most out of my project and develop the system to best suit their needs. I am
also interested in hearing their opinion on the use of iPhone and iPad apps in medicine.
1. Have you used a physiological signal simulator system? What do you think of the use of these simulator
systems in medicine?
I have no experience of physiological signal simulator systems. Surgical simulators are in widespread use
as a teaching tool for learning to do operations and are a good form of education but do not replace or
replicate the live surgical situation.
2. What types of simulators are used in the field of surgery? (surgery, pediatrics)
Computer based simulators as well as physical models are used for simulation. We also do cadaveric
work for teaching.
3. Do you use Zigbee, or any other medical technology system, or have you heard of it?
I have not heard of Zigbee. We use Traumacad and NIMIS which are radiology systems. We use various
programmes for data collection.
4. What improvements if any could be made to the current simulator systems used by surgeons?
I don't know enough about them to answer this question
5. Are physiological signal simulators currently used as a training tool for medical students in Ireland?
I don't know
6. Do you think they are a good idea and offer benefits to trainee doctors (allowing them to practice
procedures as many times as they need to without putting the patient at risk)?
I think that they are an excellent idea as a training tool.
7. Do you think the use of a physiological signal simulator system such as my project can reduce accidents
and percentage error during surgery, prescription, and general practice?
I think that this type of system would make it easier to teach junior doctors how to do operations, as
they will have a better idea of what to expect when they come to operate on a patient. I think that it will
possibly shorten the amount of time that a junior doctor needs to spend in training. Currently an
orthopaedic surgeon does at least 9(most do 11-13) years training after leaving medical school before
Physiological Signal Simulator FYP
they can qualify to be a consultant. Our trainees are very well supervised by a senior surgeon when
learning operations and therefore I am not sure that errors would or can be reduced.
8. Is there anything you think I could add to the system in the future to improve it/ offer more benefits to
doctors?
The introduction of simulators which can be accessed easily and at minimal cost would be a great step
forward.
9. Do you or your colleagues use iPhone/smart phone apps for surgery/medicine? What apps do you use?
I use apps for data collection; I have text books and surgery manuals on my phone. We have the ability
to view x-rays, scans etc remotely.
10. Are medical apps for iPad/iPhone becoming part of the everyday life of a surgeon?
Yes. More and more all the time.
11. Would a smart phone app that could show these signals and data be used by surgeons?
Yes. But ease of access and reliability of the data displayed would be essential.
12 What field of medicine do you think we should aim this app at?
It sounds to me that this app would be ideally suited to cardiology, respiratory or renal medicine for
everyday practical use and for surgical training.
1.2 Physionet Physionet is a database which stores large collections of physiological signals. It includes software that
can be used to analyse these signals, as well as collections of research papers, reference material and
tutorials relating to the signals and software. Physionet receives its data and software from researchers
worldwide. Many of the contributors are clinical and medical researchers, but also from physicists,
mathematicians, computer scientists, educators and students. All signals have been annotated by at
least two cardiologists making the data very reliable and excellent for research, analysis and learning
purposes.
Since physiological signals display astonishing diversity, it is impossible to analyse and categorise signals
from just studying a small number of them. In order to gain an accurate insight into the characteristics of
these signals and what causes certain changes in them we need to study hundreds of them. It is difficult
and expensive to collect and study this large amount of data and requires software which is flexible,
efficient and modifiable to match the research requirements. Physionet provides a solution. This readily
available data and software will assist and increase the speed of my medical research and will allow me
to gain a better understanding of ECG signals. It will increase the accuracy and quality of my data.
Physiological Signal Simulator FYP
1.3 LabVIEW LabVIEW is a graphical programming environment for developing custom applications that interact with
real-world signals in the field of science and engineering. It allows the user to create measurement, test
and control systems using graphical icons and wires. Because LabVIEW programming model is very
similar to standard flowchart notation, it is extremely intuitive and easy to learn. It offers unparalleled
integration with thousands of hardware devices and makes a wide variety of libraries and data analysis
tools available in a single environment.
1.3.1 Advantages of LabVIEW
Faster Development
The intuitive drag and drop graphical functions and interconnecting wires allow the user to program
faster compared to writing lines and lines of code. The flowchart-like model makes it is easy to develop
and maintain code, spot bugs and errors and understand the flow of control.
Integrated Hardware
LabVIEW has built in compatibility with thousands of hardware libraries including signal conditioning,
data and image acquisition and motion control.
Powerful Analysis
LabVIEW features powerful analysis libraries complete with statistics, evaluations, regressions, linear
algebra, signal generation algorithms, time and frequency-domain algorithms and digital filters.
User Interface – Draw Your Own Solution
LabVIEW provides an easy-to-use development environment that allows the user to draw user interfaces
by choosing from hundreds of drag and drop controls. The user can interactively control the system data
and visualise results using graphs and 3D visualisation tools. You can write a program and then rapidly
prototype, design, and modify it in a short amount of time. LabVIEW allows you to develop a complete
solution within one environment. This solution is ideal for my project as it can be modified to include
features such as creating “virtual” patient profiles, dynamic real-time analysis of their vital signals,
comparison of past and current results and report generation for forming a diagnosis.
Physiological Signal Simulator FYP
2. Proposals for tackling project
Physiological Signal Simulator FYP
3. Progress to date
3.1 Concepts of the ECG
I spent time researching and studying the ECG signal before I began any coding. I think it is important to
understand exactly what is happening in the signal in order to develop a system to analyse it. I learned
about what each wave and interval in the ECG corresponds to in the human body and made a
connection between the electrical pulse and the mechanical action of the heart. I stepped through the
cycle of a heart beat and gained a good understanding of how to analyse an ECG signal. I compared a
healthy sinus rhythm to diseased rhythms. I studied a number of different cardiac diseases and how
these affect the characteristics of the ECG. For research purposes I talked to two medical professionals
and interviewed a surgeon to gain medical knowledge and to see the importance of ECG analysis from
the perspective of a doctor.
3.1.1 The Heart
The heart is the organ that supplies blood and oxygen to all parts of the body. It is divided into two
halves by a muscular like wall called the septum. The halves are in turn divided into chambers. The
upper two chambers of the heart are called atria and the lower two chambers are called ventricles. The
atria receive blood returning to the heart from the body and the ventricles pump blood from the heart
to the body. The heart is made up of cardiac muscle which enables it to contract and allows the
synchronization of the heart beat. The heart wall is divided into three layers: the epicardium,
myocardium, and endocardium. I am only concerned with the myocardium, which is the middle
muscular layer of the heart. It has heart muscle cells called myocytes. When the myocardium is
stimulated it electrically contracts. The signals corresponding to these electrical contractions are
recorded on the ECG.
Physiological Signal Simulator FYP
Figure 2 Heart Chambers
3.1.2 ECG
The electrocardiogram (ECG) records the electrical activity of the heart, providing a record of cardiac
electrical activity, as well as valuable information about the heart’s functions and structure. Most of the
information on the ECG represents electrical activity of contraction of the hearts muscle
(“myocardium”). The ECG also produces valuable information about the heart’s rate and rhythm. ECG’s
record the electrical heart activity using skin sensors called electrodes. While in resting state the
myocytes (muscle cells) are polarised negatively.
When myocytes are depolarised they become positively charged and contract. Depolarisation moves as
a wave of positive charges through the heart muscle and causes progressive contraction. This cell-to-cell
conduction of depolarisation is carried by fast moving Na+ ions. When this wave of positive charges
(Na+ions) moves toward a positive electrode there is a simultaneous positive upward deflection
recorded on the ECG.
Physiological Signal Simulator FYP
Figure 3 Depolarisation of myocyte cells
Figure 4 Contraction of the myocardium as depolarisation wave moves through heart
Repolarisation is the recovery phase after depolarisation that brings the myocyte cells back to their
resting negative charge. It is an electrical phenomenon that begins immediately after depolarisation.
Figure 5 Depolarisation and repolarisation wave
Depolarisation Repolarisation
Physiological Signal Simulator FYP
3.1.3 Sinus Rhythm
The heart’s dominant pacemaker, the SA node, initiates a wave of depolarisation (Na + ions) that
spreads outwards from the upper right atrium. The enlarging, circular depolarisation wave flows away
from the SA node in all directions and stimulates the atria to contract as it advances. The ability of the
SA node to generate pace making ability is called automaticity. The simultaneous contraction of the
atria forces blood through the Atrio-Ventricular (AV) valves. The AV valves prevent the backflow of
blood; the AV node shown in diagram below is the only conducting path between the atria and
ventricles.
Figure 6 the sinus node
3.1.4 Analysis of P-QRS-T Complex
The heartbeat signal is made up of five points – P, Q, R, S and T.
Figure 7 P-QRS-T Complex
The P- Wave
Physiological Signal Simulator FYP
Each depolarization wave emitted by the SA node spreads through both atria and produces a positive P
wave on the EKG. Therefore the P wave represents the depolarisation of both atria and hence the
simultaneous contraction of the atria on the ECG.
When the atrial depolarization wave enters the AV node, depolarization slows down producing a brief
pause or delay, allowing time for the blood in the atria to enter the ventricles. This pause is seen on the
ECG.
Figure 8 the P Wave
QRS Complex
Depolarization conducts slowly through the AV node as the charge carriers are slow moving Ca+ ions,
however depolarization rapidly shoots up through the ventricular conduction system beginning in the
His Bundle. This is because the His Bundles and both bundle branches are bundles of Purkinje fibers that
use fast moving Na+ ions for conduction of depolarization. Rapid depolarization continues through the
His Bundle and the Bundle branches. The terminal filaments of the Purkinje fibers depolarize the
ventricular heart muscle. This quickly distributes the positive charge to the ventricular muscle cells. The
depolarization of the ventricular heart muscle causes the ventricles to contract and this corresponds to
the QRS complex on the ECG. The Q wave is the first downward deflection of the complex. The
downward Q wave is followed by an upward R wave. The upward R wave is followed by a downward S
wave.
pause
Physiological Signal Simulator FYP
Figure 9 QRS Complex
ST Segment
The horizontal flat segment of the baseline following the QRS complex is the ST segment. If the ST
segment is elevated above or below the normal baseline it is usually a sign of imminent problems. The
ST segment represents the initial phase of ventricular repolarisation. J is the point of deflection between
S wave and the ST segment.
Figure 10 ST Segment
T Wave
The T wave represents the final “rapid” phase of ventricular repolarisation. Ventricular repolarisation
occurs quickly and effectively here so that the ventricular muscle cells can recover their resting negative
charge. Hence depolarization can begin again and the cardiac cycle continues. Repolarisation is
accomplished by potassium K+ ions leaving the muscle cells.
Physiological Signal Simulator FYP
Figure 11 T Wave
QT interval
The QT interval represents the duration of ventricular contraction and is measured from the start of the
Q wave to the end of the T wave. It is a good indicator of repolarisation and varies with heart rate. The
QT interval is considered normal when it is less than half of the R-to-R interval at normal rates.
Summary
Wave Electrical Activity Physiological Heart Activity
P Atrial depolarization Atria contract
Pause/Delay Conduction delay Blood flow from atria to ventricles
QRS Ventricular depolarization Ventricles contract
ST interval Isoelectric Ventricle segment Initial repolarisation of ventricles
T Ventricular repolarisation Rapid repolarisation of ventricles
Q-T interval Complete ventricular
depolarisation and
repolarisation
Duration for full ventricular
contraction
Physiological Signal Simulator FYP
Figure 12 Regions of the heartbeat
3.2 Selecting Signals from Physionet
I became familiar with the Physionet database and learned how to read information from the files,
select and download ECG signals, convert them to a format which could be read by a LabVIEW program
and then saved hard copies of appropriate formatted files to folders on my laptop. The physiological
signals I used are located in Physiobank.
PhysioBank is an archive with characterised digital recordings of physiological signals and related data
which is used by the biomedical research industry. PhysioBank contains biomedical signals from healthy
and unhealthy subjects, with a variety of conditions.
Each PhysioBank database can contain more than one record, and each recording might have three files:
1. Header file (*.hea file) - a short text file that describes the signals using ID number or URL of the
file, storage format, number of channels, sampling frequency, total number of samples,
calibration data, digitiser characteristics, record duration and starting time.
2. Annotation file - description of features of the signals
3. Binary (*.dat) – file containing digitised samples of signals
Physiological Signal Simulator FYP
The file I used for information was the header file. It was important for me to record the sampling
frequency and bit resolution of the files I saved as these are important parameters when reproducing
the signals with LabVIEW. I also needed to format the files and save them so that they were readable by
LabVIEW. I decided to write/read from a text file. I converted selected ECG signals from .DAT format on
Physionet to CSV format then edited the rows/columns so that the only information saved was the wave
amplitude (mV) and time (ms), as this is the only data needed to plot a waveform graph.
I used the following path to select and edit ECG signals from Physionet:
Physiobank -> Physionet ATM -> ECG -> Select signal -> Edit/Save to CSV -> Format Rows/Columns ->
Save as text file
I studied a number of ECG signals on Physionet before downloading them, including both healthy and
unhealthy rhythms. Cardiac disease characteristics I looked at include:
Atrial Fibrillation
Sleep Apnea
Congestive Heart Failure
Cardiac Arrest
Arrhythmias – Ventricular Tachycardia
Ventricular Fibrillation
Physiological Signal Simulator FYP
Figure 13 Screenshot of Normal Sinus Rhythm on Physionet
Physiological Signal Simulator FYP
3.3 Reproducing signals with LabVIEW
3.3.1 Initial LabVIEW Program
Initially I wrote a program on LabVIEW to reproduce the downloaded ECG file using the sampling
frequency. I needed to include a time delay between when each pulse is plotted. The time delay needs
to be a factor of an incoming parameter. I used the following formula to calculate the time delay:
½*fsamp = delay
The first program I wrote reads the data from file and writes it to the waveform graph with a delay.
Figure 14 Screenshot of Loop with Delay Block Diagram
Figure 15 Screenshot of Front panel
Physiological Signal Simulator FYP
3.4 UI Design I am currently in the process of developing and improving my LabVIEW User Interface. I hope the end
product to be intuitive and user friendly, and to offer many analysis options.
Figure 16 User Interface Design Idea
To help design the backbone of my UI I have decided to use a state machine. A state machine will allow
different user actions or selections to determine the next state of the state machine, where each state
will be a processing segment. I downloaded the JKI State Machine for LabVIEW, a powerful String based
queue state machine template. The state machine is a case structure inside a while loop. It has a core
event handler which is the “Idle” frame of the case structure. The event handler has a timeout frame set
as minus 1 which means it will never execute. The states are executed by wiring in a String of states. It
has a number of initialise states that are called when the program starts to initialise data and the VI. It
then waits in the “Idle” state until the user performs an action. I drew a flow chart of the first few states
of my state machine; I will be adding more states and developing it in the coming weeks.
Physiological Signal Simulator FYP
Figure 17 Flowchart of State Machine so far
I incorporated my earlier code to read data from file and created a “Load signal” state. This means the
machine remains idle until the “Load” button is pressed by the user, and then the user is asked to select
the file they want to read data from. I also created an “Update graph” state. When the data is read from
file the next state updates the waveform graph. I also have a “Stop” state which executes when the user
presses the “Ok” button. This clears the graph and stops the program running. Since the state machine
operates as a Queue I ensured I added my states in the correct FIFO order. The next step is to create an
“Add signal” state so that the user can add another signal to the same graph for comparison and
analysis. I hope to also add a zoom button so that the user can manipulate the graph and zoom in on
different regions of the wave based on the disease characteristics. I am currently working on these
features.
Physiological Signal Simulator FYP
Figure 18 Screenshot of "Idle" state Block Diagram
Figure 19 Screenshot of "Load signal" state Block Diagram
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Figure 20 Screenshot of "Update graph" state Block Diagram
Figure 21 Screenshot of Front Panel UI so far with plot of Arrhythmia samples
Physiological Signal Simulator FYP
3.5 Website A website is available at http://meabhmalonefyp.wordpress.com
It contains back ground of my project, milestones on which my FYP will be graded and documentation of
my progress to date.
4. Task List and Project Plan
Develop LabVIEW UI to allow manipulation and selection of signals
Research existing simulators- software versus hardware
Output signals to Bluetooth port or hardware amplifier
Package of complete software/hardware system
Testing of system
Identification of limits
5. Conclusion
In conclusion I am very interested in the possible applications of a physiological signal simulator system
such as this one, for both medical training and technology advancement purposes. I have a keen interest
in medicine and how the body works and have gained knowledge in this area. The main objective now
is to improve the UI to include more analysis options for the manipulation of signals. The aim is for the
simulator system to be low cost, user friendly, intuitive and to simulate, analyse and provide feedback
on physiological signals. Currently my system can meet all of these aims at a low level. As I improve the
system and adapt it to include Bluetooth or a monitor, I hope that it could potentially help advance the
research and development of new medical applications and technology at NUI, Galway.
Physiological Signal Simulator FYP
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info/figure2.jpg&w=1114&h=836&ei=RkWNUI3DA42yhAfZu4GwAQ&zoom=1&iact=hc&vpx=474&vpy=4
46&dur=274&hovh=194&hovw=259&tx=164&ty=103&sig=112541250704356799777&page=1&tbnh=13
9&tbnw=186&start=0&ndsp=26&ved=1t:429,r:16,s:0,i:111
http://www.physik.uni-freiburg.de/~severin/ECG_QRS_Detection.pdf
http://www.physionet.org/faq.shtml#physionet-what
http://www.ni.com/white-paper/6349/en
References
[1] Dale Dublin, MD, 2000: Rapid Interpretation of EKG’s, 6th Edition
[2] John R. Hampton, 1992: The ECG In Practice, 2nd Edition