The project report is prepared for

Faculty of Engineering

Multimedia University

in partial fulfilment for

Bachelor of Engineering

FACULTY OF ENGINEERING

MULTIMEDIA UNIVERSITY

January 2012

ECG BIOMETRIC RECOGNITION WITHOUT

FIDUCIAL DETECTION

by

Kazi Tasneem Farhan

10711118244

Session 2011/2012

Session 2011/2012

i

The copyright of this report belongs to the author under the

terms of the Copyright Act 1987 as qualified by Regulation 4(1)

of the Multimedia University Intellectual Property Regulations.

Due acknowledgement shall always be made of the use of any

material contained in, or derived from, this report.

ii

Declaration

I hereby declare that this work has been done by myself and no portion of the work

contained in this report has been submitted in support of any application for any

other degree or qualification of this or any other university or institute of learning.

I also declare that pursuant to the provisions of the Copyright Act 1987, I have not

engaged in any unauthorised act of copying or reproducing or attempt to copy /

reproduce or cause to copy / reproduce or permit the copying / reproducing or the

sharing and / or downloading of any copyrighted material or an attempt to do so

whether by use of the University‟s facilities or outside networks / facilities whether

in hard copy or soft copy format, of any material protected under the provisions of

sections 3 and 7 of the Act whether for payment or otherwise save as specifically

provided for therein. This shall include but not be limited to any lecture notes,

course packs, thesis, text books, exam questions, any works of authorship fixed in

any tangible medium of expression whether provided by the University or otherwise.

I hereby further declare that in the event of any infringement of the provisions of the

Act whether knowingly or unknowingly the University shall not be liable for the

same in any manner whatsoever and undertakes to indemnify and keep indemnified

the University against all such claims and actions.

Signature: ________________________

Name: Kazi Tasneem Farhan

Student ID: 1071118244

Date: 9th

January 2012

iii

Acknowledgement

Firstly I would like to thank Allah. I believe without His blessing and mercy I would

not have been able to come so far. Then I would like thank my parents for their

tremendous prayers and moral support. I want to thank them for believing in me.

I would like to express my sincerest thankfulness to my supervisor Dr. Khazaimatol

S. Subari. Her tremendous support and guidance is truly mesmerising. Her

friendliness, co-operation and kindness have helped me a lot throughout my work.

Once again I would like to thank Miss Shima for being so patient with me.

I would also like to mention a research officer Syed Syahril. I would like to express

my gratitude towards him because he has been very helpful and kind. I would like to

thank a senior and Master‟s researcher Mr. Rameshwor Prasad Shah who has been

assisting and guiding me with my research work.

Not to forget my friends who have been a tremendous support as well during my

duration of this research.

iv

Abstract

The structure and functions of the heart is studied. This gives idea of how the heart

works in the human body. The ECG signal is studied by looking into its features and

how they are related to the heart. The method of extraction of ECG signal from the

heart and various forms of obtaining is also studied because it helps in analysing the

signal.

The various types of noise in the ECG and the different methods to filter the signal

are studied. A database of signal is collected and pre-processed using a chosen filter

and window to get a clean and sharp signal. The autocorrelation based feature

extraction method is used to extract features from the signal. Principal component

analysis is used to reduce the dimension of the features obtained from

autocorrelation.

The features obtained are put through a classification stage using a neural network.

The network uses a pattern recognition tool with a feed forward back propagation

network and a training function called scaled conjugate gradient to classify the the

features obtained from each signal.

The scope of the project is based on 35 subjects. A set of 10 trials of signals is

collected from every individual. 9 out 10 trials are used to train the network and the

1 trial from each individual is used to test the network.

The results obtained give a recognition rate of 95%. This means that it is a good

recognition system. It is found that ECG is unique for every individual. So ECG is a

good feature to use in a biometric system.

v

Table of Contents

Declaration...................................................................................................................ii

Acknowledgement ..................................................................................................... iii

Abstract ....................................................................................................................... iv

Table of Contents ........................................................................................................ v

List of Figures ............................................................................................................vii

List of Tables ........................................................................................................... viii

List of Abbreviations .................................................................................................. ix

CHAPTER 1: INTRODUCTION TO ELECTROCARDIOGRAM SIGNAL ........... 1

1.1 Heart Structure ................................................................................................... 1

1.1.1 Surfaces and layers within the heart ........................................................... 2

1.1.2 Major heart structures ................................................................................. 2

1.1.3 Systematic and pulmonary circulation ....................................................... 3

1.2 ECG ................................................................................................................... 4

1.2.1 Limb leads and Augmented limb leads ...................................................... 7

1.2.2 Precordial Leads ....................................................................................... 10

1.2.3 ECG signal labels ..................................................................................... 11

1.2.4 ECG sampling frequency ......................................................................... 12

1.2.5 ECG noise and artifacts ............................................................................ 13

1.2.6 ECG applications ...................................................................................... 14

CHAPTER 2: PREVIOUS WORK AND PROPOSED METHODOLOGY ............ 16

2.1 Previous Work ................................................................................................. 16

2.2 Proposed methodology .................................................................................... 18

2.2.1 Pre-processing .......................................................................................... 18

2.2.2 Feature Extraction..................................................................................... 19

vi

2.2.3 Principle Component Analysis ................................................................. 20

2.2.4 Template matching ................................................................................... 21

CHAPTER 3: PRE-PROCESSING AND FEATURE EXTRACTION

METHODOLOGY .................................................................................................... 23

3.1 Experimental Setup ......................................................................................... 24

3.2 Methodology used ........................................................................................... 25

3.2.1 Pre-processing .......................................................................................... 25

3.2.2 Autocorrelation based feature extraction .................................................. 30

CHAPTER 4: CLASSIFICATION ........................................................................... 32

4.1 Neural Network ............................................................................................... 32

4.1.1 Pattern Recognition Network ................................................................... 33

4.1.2 Feed-forward Back propagation network ................................................. 34

4.2 Process of Classification .................................................................................. 36

4.2.1 Feature Set ................................................................................................ 36

4.2.2 Training the network ................................................................................ 37

4.2.3 Testing the network .................................................................................. 40

CHAPTER 5: DISCUSSION .................................................................................... 43

CHAPTER 6: CONCLUSION .................................................................................. 46

6.1 Future work and recommendation ................................................................... 46

References ................................................................................................................. 47

Appendix A ............................................................................................................... 49

Matlab Code .......................................................................................................... 49

vii

List of Figures Figure 1-1: (a) Anterior view showing surface features, (b) Posterior view ..................................................... 3

Figure 1-2: Willem Einthoven .............................................................................................................................. 4

Figure 1-3: An ECG signal obtained from one heart beat ................................................................................ 4

Figure 1-4: Types of ECG electrodes and its placements on the body ............................................................. 5

Figure 1-5: Circuit used to collect ECG signal ................................................................................................... 7

Figure 1-6: The top three are Limb leads and the bottoms three are Augmented limb leads [1] ................... 7

Figure 1-7: ECG signal from the Limb leads [2] ................................................................................................ 8

Figure 1-8 ECG signal from Augmented Limb leads [2] ................................................................................... 9

Figure 1-9: Einthoven’s Triangle representation of Limb leads and Augmented limb leads [2] ................... 9

Figure 1-10 Position of placement of Precordial leads ..................................................................................... 10

Figure 1-11 ECG signal from the six Precordial leads [2] ............................................................................... 10

Figure 1-12: Noisy Signal (Top) and Filtered Signal (bottom) ........................................................................ 14

Figure 2-1: Noisy signal (Top) and Filtered signal (bottom) ........................................................................... 18

Figure 2-2: Different fiducial points of an ECG signal .................................................................................... 19

Figure 3-1: Process flow diagram of the steps involved in Biometric system ................................................. 23

Figure 3-2: Position of electrode placement to collect ECG data .................................................................... 24

Figure 3-3: Noisy ECG signal ............................................................................................................................ 26

Figure 3-4: Normalised noisy ECG signal ......................................................................................................... 26

Figure 3-5: Noisy ECG signal in frequency domain ......................................................................................... 27

Figure 3-6: Filtered normalised ECG signal ..................................................................................................... 27

Figure 3-7: Filtered ECG signal in frequency domain ..................................................................................... 28

Figure 3-8: ECG from 3 subjects ....................................................................................................................... 28

Figure 3-9: Hamming Window .......................................................................................................................... 29

Figure 3-10: Filtered ECG signal after windowing .......................................................................................... 29

Figure 3-11: Autocorrelated data ...................................................................................................................... 30

Figure 3-12: 400 AC points from the maximum to the right ........................................................................... 31

Figure 3-13: PCA Hotelling’s T2 statistic data from 3 subjects ....................................................................... 31

Figure 4-1: Process flow diagram of a Neural Network [10] ........................................................................... 32

Figure 4-2: A portion of the target matrix for training ................................................................................... 34

Figure 4-3: Feed Forward Network ................................................................................................................... 35

Figure 4-4: Neural Network Training tool ........................................................................................................ 38

Figure 4-5: Performance graph of the network ................................................................................................ 39

Figure 4-6: Training state graph of gradient and validation checks............................................................... 40

Figure 4-7: Confusion matrix with test results of 20 subjects ......................................................................... 41

viii

List of Tables

Table 1-1: Chart on position of placement of electrodes [2] .............................................................................. 6

Table 2-1: Results from using PCA and LDA [9] ............................................................................................. 17

Table 4-1: Subjects assigned to classes .............................................................................................................. 37

ix

List of Abbreviations

ECG Electrocardiogram

PCA Principal Component Analysis

DCT Discrete Cosine Transform

AC Autocorrelation

BP Back-propagation

NN Neural Network

SCG Scaled Conjugate Gradient

SAN Sinoatrial Node

AVN Atrioventricular Node

LA Left atrium

LV Left ventricle

RA Right atrium

RV Right Ventricle

aVL Augmented vector left

aVR Augmented vector right

aVF Augmented vector foot

RL Right leg

LL Left leg

RR R to R peak

AP Action potential

RBFNN Radial basis neural network

LDA Linear discriminant analysis

MSE Mean squared error

MLP Multi-layer perceptron

1

CHAPTER 1: INTRODUCTION TO

ELECTROCARDIOGRAM SIGNAL

Security devices and technology have to be rapidly changing and advancing since

new ways of falsification are coming up all the time. Innovation in this field is of

utmost important to stay ahead of the game. Biometrics is one of the possibilities of

providing such protection. Biometrics refers to a certain physical, biological and

behavioural characteristic of a human being. There many features that are used as

the modalities of biometrics such as physical traits include face, iris and behavioural

include gait and keystroke. The process of using these unique features of everything

human being is done through computing and signal processing.

The problem which remains with these previously mentioned features is that they

can be falsified. In the last few years a lot of researchers have suggested the use

Electrocardiogram (ECG) for biometric recognition. ECG is signal wave used to

represent the electrical activity of heart. It has been found that this measure is unique

for every individual. It is thought to be a promising application in the biometric

field.

A lot of research has been done in this field now. Most of the researches try to study

the ECG signals by looking into its various aspects. This is done to find out the best

procedure of using ECG in biometric system. In this particular project, several

combinations of aspects of the ECG signal will be taken into consideration. Using

these aspects or features the identification process will be done.

1.1 Heart Structure

In order to study the signal it is important to study the biology of the heart. Only by

understanding it we get a good knowledge of the ECG signal. Thus this first chapter

will give a thorough demonstration of the Heart structure and the method used for

the acquisition of ECG signal will be elaborated.

2

1.1.1 Surfaces and layers within the heart

The human heart rests in the thoracic cavity of the human body. The wide superior

portion of the heart from which the great vessels emerge is the base of the heart. The

inferior end pointing to the left is the apex of the heart. It tilted at an angle so that

the inferior surface rests against the diaphragm with two thirds of it at the left side of

the sternum.

1.1.2 Major heart structures

The heart is divided into two sides: the left and the right. Each side has an upper and

lower chamber. So there are a total of four chambers in a human heart. The upper

chamber is referred to as an atrium and the lower as a ventricle. The apex is formed

by the tip of the left ventricle and the two atria form the base of the heart. The four

chambers are marked on their boundaries by shallow grooves called sulci.

The wall of the heart is composed of Cardiac Muscle tissues which have its own

blood supply and circulation, the coronary circulation. The heart is surrounded by

coronary blood vessels. The right and left coronary arteries found on the anterior

surface of the heart supplies blood when the ventricles are resting. The cusps of the

aortic valves cover the coronary artery openings when the ventricles contract.

The great veins of the heart such as superior vena cava, inferior vena cava and

coronary sinus return oxygen poor blood to the right atrium.

The great arteries carry blood away from the ventricles. The large artery is divided

into the right and left pulmonary artery which carries blood to the lungs where it is

oxygenated. Then this oxygen rich blood is returned to the left atrium through the

right and left pulmonary veins. Then the blood passes to the left ventricle where it is

pumped to the large aorta. The aorta then distributes the blood to the systematic

circulation.

3

(a)

(b)

Figure 1-1: (a) Anterior view showing surface features, (b) Posterior view

1.1.3 Systematic and pulmonary circulation

There are two types of circulation: pulmonary and systematic. The function of the

pulmonary circulation is to carry the blood from the left ventricle to the lungs and

back to the left atrium. The systematic circulation functions same like the pulmonary

circulation except that it functions with body tissues instead of lungs. Both the

circulations involve arteries, capillaries and veins. The circulations start from the

heart and come back to the heart in the end.

In short, the blood returning to the heart through the great veins is received by the

thin walled atria. The large thick ventricular walls contract simultaneously to send

the blood from the right ventricle to the pulmonary circulation and from the left

ventricle to the systematic circulation.

4

1.2 ECG

ECG is short for electrocardiogram. This term was first introduced in 1924 by

Willem Einthoven. He won a Nobel Prize for introducing the mechanism of

electrocardiogram [1].

Figure 1-2: Willem Einthoven

Mainly this process digitally records the electrical activity of the heart muscle tissue

at the cell level. It poses no pain or physical discomfort to the patient on whom it is

performed. Several electrodes can be placed on the skin which will make several

simultaneous aspects of spatial phenomenon accessible known as

electrocardiographic leads. This electrical behaviour helps to detect diagnose heart

abnormalities and thickness or damage in the heart muscle before or during heart

attacks. Electrodes are placed on the human body to receive the ECG signals .The

usage of electrodes to receive ECG signals range from a number of orthogonal 3-

lead systems to as many as 80 or 120 leads on some extremely redundant body. [1]

An ECG signal representing a single heart beat is shown below in figure 1-3.

Figure 1-3: An ECG signal obtained from one heart beat

5

Figure 1-4: Types of ECG electrodes and its placements on the body

The standard method used in obtaining the electrical activity of the heart uses 12-

lead electrode system. The position of the lead determines with reference to the heart

position determines the wave‟s morphology and amplitude. A wave can rise up or

fall or be invisible depending on this very phenomenon of spatial orientation. The

lead covers two orthogonal planes in this system: the frontal and transversal plane.

The frontal plane refers to the vertical plane which is made up of the three limbs

bipolar leads (called I, II and III) and three augmented unipolar leads (called aVL,

aVR and aVF). The transversal plane refers to the horizontal plane which crosses the

thorax orthogonally to the frontal plane consists of the six precordial unipolar leads

(called V1, V2, V3, V4, V5 and V6). Vector representation is used differently on

each lead to get the direction along which electric potentials are measured.

The position of the lead determines with reference to the heart position the waves

morphology and amplitude. When the wave goes above the base line it shows a

positive deflection and when it goes below the base line it shows a negative

deflection. A positive deflection means that the recorded wave has travelled towards

the electrode and a negative mean that it has travelled towards it. A summarised

chart on the position of placement of the electrodes is given below in table 1-1.

6

Table 1-1: Chart on position of placement of electrodes [2]

The electrodes are placed on the chest by the process given above to collect the ECG

data. The electrodes are placed in this way because of the location of the heart on

that very place on the chest and the electrical activity involving SAN and AVN takes

place during the heart cycle.

The position of placing the electrodes is shown in the diagram in figure 1-5. Zbody is

the resistance in between the electrodes where Z1 and Z2 are the lumped thoracic

medium resistances. Vecg is the bipolar scalar ECG scalar lead voltage and it is

measured with respect to a different reference potential. The foot has to be

connected to the ground in this process otherwise no signal will be obtained.

7

Figure 1-5: Circuit used to collect ECG signal

In the standard 12 lead electrode system there are two ways of electrode placement.

The first one is the limb and augmented limb leads Bipolar and unipolar are two

different types of lead that is used in obtaining the signals. All lead apart from the

limb leads use unipolar leads. The two ways of electrode placement is further

described below.

1.2.1 Limb leads and Augmented limb leads

Figure 1-6: The top three are Limb leads and the bottoms three are Augmented limb

leads [1]

8

1.2.1.1 Limb leads

In figure 1-6 the top three drawings showing the leads I, II and III are called the

limbs leads. If the three lines on each figure are put together then they form what is

called the Einthoven‟s triangle. The leads use the electrodes in different

combinations.

Lead 1 captures the potential difference in between negative RA electrode which is

on the right arm and the positive LA electrode which is on the left arm.

Lead II captures the potential difference in between negative RA electrode which is

on the right arm and the positive LL electrode which is on the left arm.

Lead III captures the potential difference in between negative LA electrode which is

on the right arm and the positive LL electrode which is on the left arm.

Figure 1-7: ECG signal from the Limb leads [2]

1.2.1.2 Augmented Limb leads:

In figure 1-6 the three drawings on the bottom show the leads aVR, aVL, and aVF

which are otherwise called the augmented limb leads. They are basically derived

from the leads I , II and III. The augmented limb leads are created by combining two

of the three electrodes into a mutual negative pole and the remaining lead is kept as

a positive pole. This modification is known as Wilson‟s central terminal.

Lead aVR which stands for “augmented vector right” places a positive electrode on

the right arm. The mutual negative pole here is a combination of the left arm

electrode and the left leg electrode and thus the signal strength of the positive

electrode on the right arm is augmented.

9

Figure 1-8 ECG signal from Augmented Limb leads [2]

Figure 1-9: Einthoven‟s Triangle representation of Limb leads and Augmented limb

leads [2]

Lead aVL which stands for “augmented vector left” places a positive electrode on

the left arm. The mutual negative pole here is a combination of the right arm

electrode and the left leg electrode and thus the signal strength of the positive

electrode on the left arm is augmented.

Lead aVF which stands for “augmented vector foot” places a positive electrode on

the left leg. The mutual negative pole here is a combination of the right arm

electrode and the left arm electrode and thus the signal strength of the positive

electrode on the left leg is augmented.

10

1.2.2 Precordial Leads

There are six leads under this category and they are also known as the chest leads.

They are placed on different parts of the chest as shown in figure 1-10. The leads

used here are unipolar as was mentioned before. This is because the Wilson‟s central

terminal is used as the negative electrode.

Figure 1-10 Position of placement of Precordial leads

The limb and augmented leads together form the hexaxial reference system which

helps to obtain the electrical activity of the heart on the frontal plane. The leads

work in pair to cover a certain area on the chest. The pairs are V1 and V2, V3 and

V4 and V5 and V6 which covers the anteroseptal ventricular region, the anteroapical

ventricular region and the anterolateral ventricular region respectively. These leads

are used to record the signals from horizontal plane known as the z-axis.

Figure 1-11 ECG signal from the six Precordial leads [2]

11

1.2.3 ECG signal labels

The living cells are negatively charged 90mV) when they are at rest but they are

quickly depolarized by the electrical stimulus. This change in voltage leads to the

mechanical contraction of the heart by compressing and electrically exciting the

proteins in the cells. This contraction leads to the changes in chamber volume. The

electrically activated procedure of contraction and distraction happens repeatedly to

pump blood effectively.

The group of cells located in the upper part of the right atrium known as sinoatrial

node (SAN) electrically initializes a heartbeat by its ability of spontaneous

discharge.

The resulting wave front scatters around all the atrial paths and causes both the atria

to contract which forces blood into the ventricles. The process electrical conduction

is performed at a speed of ca. 4m/s through atrioventricular pathways. The electrical

conductivity is low in the atrioventricular node so the wave front is delayed by

120ms. During this delay the content of atria is transferred through the

atrioventricular valves to the ventricles. Special conducting pathways (Purkinje

fibres) make up for the large ventricles and helps in accelerating the propagation of

the wave front, thus allowing efficient pumping of the blood throughout the body.

Until the relaxation state goes up to 65mV after depolarization the cells do not

receive or transmit electrical stimuli. Thus the wave front dies after receiving the

heart muscle tissue. The refractory period between two heartbeats is around 200ms.

The ECG signal is used to represent the electrical activity of heart over time taken

from the human body surface for the different heart regions. The ECG signals here

refer to the action potential (AP) curves with corresponding characteristics of the

propagating wave front at different heart regions simulated at different stages of the

cardiac cycle. Each heartbeat is represented by the series of five principal waves

known as P, Q, R, S and T. (Refer to figure 1-3)

1. The P-wave shows the beginning of a new beat. It is produced by the atrial

activation as a small amplitude wave. This wave can be positive, negative, or

biphasic (with 2 peaks of opposite polarity). It ranges from 0.08 to 0.1ms.

The section in between the P wave and QRS complex is known as the PR

12

interval. It ranges from 0.12 to 0.20ms. This represents the start of atrial

depolarization until the start of ventricular depolarization.

2. The QRS complex is a combination of three sharp waves that are formed by

the ventricular activation. The initial small wave is called the Q

wave(depolarization on the wall between the ventricles), followed

significantly big wave with opposite polarity called the R

wave(depolarization on the left ventricle external wall) and finally the

ventricular activation is finished by a small wave called the S wave

(depolarization of upper part of ventricles). The Q-wave is negative, the R-

wave is positive, the S-wave is negative and the last positive deflection is R‟.

The duration of it ranges from 0.06 to 0.6ms. The ideal image of the QRs

complex is given below but is practical it depends on the kin of lead that is

being used.

3. The ventricular repolarization is shown by a small T-wave of variable

morphology. Seldom has a small U-wave of unknown origin following it

signifying the last bits and pieces of the repolarization process. The T-wave

can be positive, negative, bimodal or biphasic or be an upwards or

downwards deflection only. The QT interval shows the depolarization and

repolarization of the ventricle which ranges 0.2 to 0.4 seconds.

1.2.4 ECG sampling frequency

A study showed that the RR interval is usually 1ms. A critical error for heart rate

variability is likely to occur if the frequency range happens to fall in between the

threshold value. This is why Timo Bragge at al suggested that the ECG sampling

frequency should at least be 500Hz.

The normal heart rate ranges in between 60 to 100bpm. The usual heart rate

frequency ranges in between 0.50 to 3.0Hz for a heart rate of 30 to 180 bpm. The

typical value for the highest frequency is about 125Hz depending on the age, sex

13

and health of a person. But in case of paediatric ECG processing it sometimes jumps

up to 150Hz. The frequency ranges in some of the ECG application is given below

There are eight amplifiers in the modern-microprocessor based interpretive

machines. They can store and sample eight leads simultaneously –I, II, and V1-V6.

Then the rest of the four leads are stored and sampled – III, aVR, aVL, and aVF.

These machines have enough memory to store all the leads for a 10 second interval

at a sampling rate of 500 samples per second. The clinical bandwidth ranges from

0.05 to 100 Hz. The upper limit is halved to detect arrhythmias so the range for

Intensive Care Unit patients ranges from 0.50 to 50Hz. A heart rate meter eliminates

any non-ORS waves from the ECG signal such as the P and T waves. Thus the

frequency is centred using bandpass filter at 17 Hz. Late potential measurements are

the small frequency events that occur following the QRS complex wave. So the

frequency here ranges up to 500Hz.

1.2.5 ECG noise and artifacts

An ECG signal while being collected is often corrupted by external sources or

noises. These external artifacts may be of artificial or biological in nature. The

artificial sources of noise include power line interference, instrumentation noise,

electrosurgical noise, impulse noise and electrostatic potentials. The biological

artifacts come from motion and muscle artifacts and baseline drift due to respiration.

But the most important biological artifacts come from motion and muscle artifacts.

(Tompkins, W.J., 1993)

The noise in the signals includes high frequency components and low frequency

components. The high frequency components include the noises such as the power

line interference and the low frequency components come from the baseline wander.

In order to retrieve the original signal by removing the artifacts components we have

to filter the signal. The low frequency components usually fall below 0.5 Hz and the

high frequency components are above 40 Hz to 100Hz. There are numerous filters

which can remove these noises. One such example is the FIR band pass filter which

can set its cut off frequencies at 0.5Hz and 40Hz. This filter will only allow

frequencies ranging in between cut-off frequencies to pass through. When selecting

the cut-off frequency it must be made sure that the signal does not get much

14

distorted. The muscle noise remains a problem still because it overlaps with the

actual PQRST of the ECG data. The figure below shows a filtered signal at the

bottom of the figure. It can be seen that the bottom signal is much cleaner after

filtering.

Figure 1-12: Noisy Signal (Top) and Filtered Signal (bottom)

Before collecting a signal there are three things that need to be made sure this

includes electrode placement, electrode selection and skin preparation. In order to

keep the interference at a minimum the electrodes must be placed on smooth

surfaces where there is minimal hair, skin creases, bony protuberances and lesser

muscle. The skin must be clean cleaned, dry, clipped off excess hair and scrubbed to

remove any dead skin cells.

1.2.6 ECG applications

There are various uses made out of the ECG signals. It can be applied in numerous

fields of study as well as applications [2]. This is because of the huge benefits that

can be obtained from the usage of ECG signals.

One of the main areas of its usage is in the medical sector. ECG is widely used in

the medical sector for determining the various conditions of the heart. It also shows

other body conditions alongside like electrolyte abnormalities. Temporal durations

15

of the heart‟s electrical phenomena and their variations over time are parameters of

primary clinical relevance. Other than that low variations of cycle-based parameters

are also considered clinically important. Biometrics is another large field of study

where the ECG signals is highly studied and used. The increasing crime and

falsification rates have led to the search for unique traits that can be used to create a

good security system. ECG is considered to be an exceptional trait which would be

hard to falsify. It is good for biometric recognition applications because its unique

features such as relative onsets of the various peaks, beat geometry, and responses to

stress and activity give personalised characteristic of an individual. The ECG

features also vary according to age, sex and way of lifestyle (caffeine intake,

exercising, and body weight). All these features can prove to be very useful in the

biometric field. Although this inter-individual variability might cause problem in the

identification process in the long term due to the changes in the ECG features that

come over time but this can be fixed by updating the database of the biometric

system regularly. This also reduces the probability of the system being falsified

since it will be updated periodically.

Besides the quality which makes it hard to falsify there are other sides which makes

its robust to the environmental factors because the heart lays highly protected deep

inside the thoracic cavity.

This is a promising field of study but it requires thorough understanding and

knowledge of the signal and its intra-individual variability features.

16

CHAPTER 2: PREVIOUS WORK AND

PROPOSED METHODOLOGY

The previous chapter explains the basic things related to an ECG signal. This

chapter will look more into the details as to what kind of methodology is used to do

ECG signal processing. A brief description will be given from some previous studies

that have been done related to this topic. Following that the proposed methodology

for this project will be discussed.

A biometric recognition system has four vital segments. They are [21]

(i) Experimental Setup: The data acquisition method is described.

(ii) Pre-processing: The signals recorded are filtered to remove noise

(iii) Feature Extraction: The unique features of the signals are extracted

(iv) Classification: An individual is identified

2.1 Previous Work

In one of the previous study the recorded signals are filtered to remove noise. The

Principle Component Analysis (PCA) is used with Linear Discriminant Analysis

(LDA) for classification purpose for feature extraction and dimensionality reduction.

Radial Basis Neural Networks (RBFNN)-based classifier is used for classification.

The ECG is collected using industry standard ECG hardware equipped with PC-

interface with one channel electrocardiograph. The ECG is transferred via this

interface into the computer and stored as a binary file.

The project uses real world ECG data of nine persons with twenty complete cardiac

cycles for each collected at different times. Lead 1 from the Einthoven‟s triangle is

used. The sampling rate of the signal is at 128Hz. The high frequency components

of the signals recorded are removed by using a zero phase digital filter with a cutoff

frequency, fc at 0.5Hz which removes the baseline drift from the signal. As

mentioned earlier in chapter 1 that the dominating noise results from the muscle

artifacts. In this paper a wavelet denoising with soft threshold of the Wavelet

coefficients is used. This is because the muscle artifacts are additive and is modelled

with Gaussian white noise.

17

The ECG signal after filtration is segmented into full cardiac cycles. This causes the

baseline P and T wave to vanish because this segment is influenced by heart rate

variations. The ECG signal is repetitive and is a random process which is why a

sequential probabilistic model known as the Markov model is used [9]. This

segmentation will assign each signal sample to a class.

A matrix is formed where each segmented cardiac cycle is a row vector with finite

number of elements. The columns of the matrix are the training cycles per person

that are strongly correlated. The input data in then sorted according to its correlation

matrix where the first 20 vectors are used in PCA. PCA is a popular technique which

is used to reduce the dimension of discriminative information into a small number of

coefficients [9].

LDA is applied on the features extracted from PCA which reduces the features from

20 down to 9. The application of LDA also increases the class discriminativity.

The classification stage uses a Radial Basis Function Neural Network (RBFNN).

RBFNN consists of asset of input nodes and a hidden neuron layer where each

neuron has special type of output radial basis function which is centred at the mean

vector of a cluster in feature space. In the output layer the outputs of the hidden

neurons are summed up using a linear output function. There are two classifiers: one

which classifies the PCA projections and another which classifies the LDA

projections. Both the classifiers are realised by the RBFNN [9].

The results obtained for each person from the two types of feature extraction method

is given below in the table 2-1.

Table 2-1: Results from using PCA and LDA [9]

18

It can be seen from the table 2-1 that the recognition rate with PCA based feature

extraction is higher than with LDA. In fact some of the values under LDA are lower

than in PCA.

2.2 Proposed methodology

The signals recorded will be filtered using a Butterworth bandpass filter. The pre-

processed signal will then be used to extract fiducial or non-fiducial features. The

fiducial feature extracts the temporal, angular or amplitude features of the signal.

The non-fiducial feature extracts discriminative information from the signal without

localizing any fiducial markers. Then the extracted features are classified using a

template matching method.

2.2.1 Pre-processing

A recorded ECG signal usually has a lot of noise in it. So is this stage a way has to

be found so that the noise is removed. There are two types of noise which are make

up the high frequency components such as power line interference and low

frequency components such as the baseline wander in the signal. A Butterworth

band-pass filter of order 6 with cutoff frequencies at 0.05 and 40Hz is used.

Figure 2-1: Noisy signal (Top) and Filtered signal (bottom)

19

2.2.2 Feature Extraction

In order to study a signal different features have to be extracted from it. These

extracted features can be fiducial or non-fiducial. Fiducial points refer to the

angular, temporal and amplitude features of a signal. Non-fiducial features extract

discriminative information from the ECG trace without having to localize fiducial

markers. This means non-fiducial points extracts patterns globally whereas fiducial

points extracts information local to a single heartbeat

2.2.2.1 Fiducial features

The figure below shows the different kind of fiducial features that can be extracted

from the signal. The features chosen to be extracted were the P, Q, R, S,T, RQ, RS,

RP, PT amplitudes and the RR, QRS, PT, ST, QR and RS intervals. A thresholding

method using the Pan and Tompkins algorithm were used to determine the P, Q, R,

S and T. Then from the determined points the rest of the temporal and amplitude

features were to be extracted.

Figure 2-2: Different fiducial points of an ECG signal

20

2.2.2.2 Non-fiducial features:

The pre-processed signal has to be segmented into non overlapping windows. The

normalised autocorrelation (AC) for every window is obtained. The AC coefficients

are further reduced by using Principle Component Analysis (PCA) method.

Thus the method can be called AC/PCA. When the denoised signal is autocorrelated

the actual fiducial location will no longer required be found. The AC blends in sum

of all the samples in a window to sequence of sum of products [4]. The AC takes

into account the representative and highly distinctive features of the signals

recorded. Even though the signal is non-periodic but this will still work because the

signal is highly repetitive.

Windowing is done to cut out a certain portion of the signal. The AC is performed

on the windowed ECG. The data window of length N has to be longer than the heart

beat rate. The relative distances and cycle lengths of ECG varies over time

according to a subject‟s physical or mental or other states. The AC is shift invariant

so it will gather the similar features over multiple heart beat cycles. The normalized

autocorrelation coefficients Ȓxx[m] can be computed as:

Ȓxx[m] = ∑

| |

(1)

Where x[i] is the windowed ECG for i=0,1… N-|m| - 1), x[i+m] is the time shifted

form of the windowed ECG with a time lag of m = 0, 1, ...(M − 1); M<<N, where N

is the length of the windowed signal.

2.2.3 Principle Component Analysis

This uses orthogonal transformation to convert a set of correlated variables into a set

of uncorrelated variables which are called the principle components. The features

extracted from the AC are run through the PCA to compress the discriminative

information into low number of coefficients. The PCA is a linear transform

represented by

Y = PTX (2)

Where Y = transformed data

P = linear transformation matrix

21

X = Data set

The variables in the data set X are correlated to each other in varying degrees. The

transformed data output obtained gives the variables for the various linear

combinations of the variables in X. The target is to get the covariance matrix of the

transformed data in diagonal format. The matrix representation can be written as:

∑y = diag {λ1, λ2……., λM} (3)

The equation for ∑y may be written as

∑y =

YY

T =

(P

TXX

TP) = P

T ∑x P (4)

In the equation above the columns of P consists of the eigenvectors of ∑x. A

diagonal matrix Λ is formed from the eigenvalues in ∑x. Hence the eigenvector and

eigenvalue decomposition is done according to the equation below:

∑x P = PΛ (5)

Since the ∑x is symmetric so P is an orthogonal matrix and the eigenvalues of ∑x are

real numbers.

Thus for the orthogonal matrix P:

PT ∑x P = P

T PΛ and (6)

PT ∑x P = Λ (7)

This makes ∑y = Λ. The eigenvalues found are the results with a polynomial degree

M. The leaves the final stage which is to find the eigenvectors corresponding to the

eigenvalues and arrange them in descending-energy order.

The original basis has a dimension of MxM but in the result the basis is reduced in

size to MxL (where L<<M). The factors with negligible energy are eliminated which

reduces the size of the result obtained.

2.2.4 Template matching

At this stage it is necessary to reduce the size of a large class number problem to a

small-class number problem. This is the main idea behind the classification stage.

22

This is what the template matching method does. It reduces the number of class and

makes the possibility of classification easier by making the scope smaller. The

performance of the system is enhanced because the search space is smaller.

This can be performed using different distance metrics. One such distance metrics in

the Euclidian distance. [4] The computation is done based on the equation below.

D(x1, x2) =

√ (8)

This equation calculates the normalized Euclidean distance D between two feature

vectors x1 and x2. In this case the feature vectors are the features (covariance matrix)

obtained from PCA. C is this equation refers to the dimension of the feature vectors.

This will make the computation fair for the different dimensions that x has.

The PCA results obtained from the trained samples will be compared with the PCA

result that is obtained from a test input. The distance will be computed for the test

input with all the feature vectors stored for the trained samples.

The minimum distance found will lead us to the correct person. This is because the

stored feature vector for a subject must have been close to that of the test input

features vectors. Thus the stored feature vector giving the minimum distance is the

correct person. Hence the identification is done.

23

CHAPTER 3: PRE-PROCESSING AND

FEATURE EXTRACTION

METHODOLOGY

This chapter will focus on the process of how the data was collected and processed.

This will be done based on the process explained in chapter1 on ECG collection

method and chapter two which explains the pre-processing and feature extraction

method. The ECG records database used in this project are recorded by one of the

senior students who have been working on a project related to biometrics. Still the

process of collection is relevant to the report and shall be explained in details in the

later sections of this chapter.

This chapter will also discuss in detail the method chosen to process the signal. The

different stages of operation performed on the dataset and the results obtained will

be presented for better understanding. This will give a clear picture as to why the

discussed algorithm is chosen. The process flow diagram of the whole process is in

the figure 3-1.

Figure 3-1: Process flow diagram of the steps involved in Biometric system

ECG collection • Experimental Setup

Filter and Window • Pre-processing

Autocorrelation • Feature Extraction

PCA • Dimension reduction

Neural Network • Classification

24

3.1 Experimental Setup

The ECG database used in this project was collected using Lead 1. The software

used to collect and measure the data was Matlab R2009b software. It was used in

combination with the hardware gMobilab+ by gTech, Guger Technologies,

electrodes and personal computers. The figure below shows the position of the

electrodes placed on the human body.

Figure 3-2: Position of electrode placement to collect ECG data

The electrode place on the lower left and right rib form the Einthoven‟s triangle as

was mentioned in chapter 1.

The data was collected from 35 subjects with a age, weight and height between 21-

25 years old, 50 to 100 kg and 160 to 200 cm respectively. When the signals were

collected the subjects were made to sit in a comfortable position on a sofa. They

were given some time to come to a state of relaxation. The palms were rested on the

handles of the sofa on the either side of the person. The signals were obtained from

Ch 5 -ve

GND

Ch 5 +ve

25

the subjects at a sampling frequency of 250Hz. There were 10 sets of signals

collected per person. Each signal was recorded for a length of 60 seconds time [2].

3.2 Methodology used

This part will go through the method used in signal processing and collecting the

features from every signal. There is several stage of work done in each of these two

stages. In general the steps are:

Pre-processing

The first 30 seconds of the signal are taken.

The signal is normalised

ECG records are filtered to remove noise

The filtered signal is windowed

Feature Extraction

The normalised autocorrelation coefficient s are extracted from

the filtered signal

The PCA covariance matrix is generated for classification

The similar steps are repeated for all the 35 individuals in creating biometric

recognition system.

3.2.1 Pre-processing

The ECG signals in the database are filtered to remove the noise. The signals are not

only have high and low frequency components of noise. It also has muscle noise

which overlaps with the original signal. Traditional methods like a bandpass filter

can often degrade a signal. Wavelet can de-noise a signal without appreciable

degradation [3]. This is a why a daubechies wavelet „db6‟ which is Discrete Wavelet

Transform (DWT) function at scale 12 is used. This removes the noise from power

line interference and baseline wander. All the signals are standardized at a sampling

frequency of 256Hz.

26

Figure 3-3: Noisy ECG signal

Figure 3-4: Normalised noisy ECG signal

The signal in figure 3-2 is not so sharp and clear. The second figure 3-3 shows the

signal is normalised within -1 to 1.

27

Figure 3-5: Noisy ECG signal in frequency domain

The unfiltered signal in the frequency domain in figure 3-4 shows a spike at 50Hz.

This noise is from power line interference.

Figure 3-6: Filtered normalised ECG signal

The figure 3-5 shows a de-noised signal. The signal after filtering gives a much

clean and sharp signal.

28

Figure 3-7: Filtered ECG signal in frequency domain

The filtered signal in the frequency domain in figure 3-6 shows no spike at the

50Hz. This is evidence that the signal is filtered properly.

A workspace folder is created where all the ECG records are stored. When the

program is running each signal passes through the pre-processing stage and goes

onto the feature extraction stage one after another. This way signal from all the 35

individuals are filtered before they are taken in for feature extraction. The figure 3-7

is evidence of the fact that each person has their own unique ECG signal. Filtered

and normalized signals of 3 subjects are shown.

Figure 3-8: ECG from 3 subjects

29

Figure 3-9: Hamming Window

Figure 3-10: Filtered ECG signal after windowing

ECG is a repetitive signal. When it is replicated after being sampled there happens

to be spectral leakage [15]. In order to avoid discontinuities of the repetitive

structure when it is tiled and reduce spectral leakage a window function is used.

Hamming window is used on the filtered signal to avoid any spectral leakage. This

will help to give a much smoother transition.

30

3.2.2 Autocorrelation based feature extraction

The reason for choosing this non-fiducial over fiducial detection is because it is fully

automatic. The main function of autocorrelation is to extract a set of significant data

from a given set of data. The purpose it serves is that it will represent the data given

to it in lower dimension. Since is ECG is highly repetitive it becomes easier to

extract the autocorrelation features from it. In Chapter 2 it was mentioned under the

proposed methodology that AC is shift invariant so it will gather the similar features

over multiple heart beat cycles [4]. It will then blends in sum of all the samples in a

window to sequence of sum of products. The AC takes into account the

representative and highly distinctive features of the signals recorded.

The figure 3-10 shows the autocorrelated data obtained from the windowed signal.

The next figure 3-11 shows the segmented autocorrelated data by taking 400 data

points from the maximum. QRS complex provides least variability under different

conditions [4]. So 400 from the maximum point of the autocorrelated data is taken

which is equivalent to the length of QRS complex. The first figure shows the

autocorrelated data of the windowed signal. The next figure zooms to 400 AC points

from the maximum point of autocorrelated data.

If the window length is 5 seconds then the autocorrelated data will be present over

10 seconds.

Figure 3-11: Autocorrelated data

31

Figure 3-12: 400 AC points from the maximum to the right

Then Principal Component Analysis is applied on the autocorrelated data to further

reduce the dimension [9]. It discards the insignificant factors and thus reduces the

dimension. It does so using linear transformation. The PCA generates the

Hotelling‟s T2

statistic which gives the multivariate distance of each observation

from the center of the data set. Figure 3-13 shows the plot of the T2

statistic of 3

individual. It can be seen clearly that each is different from another.

Figure 3-13: PCA Hotelling‟s T2 statistic data from 3 subjects

These T2

statistic shall be used as input features for the classification stage in next

chapter. The process flow diagram of the whole system is shows in figure.

32

CHAPTER 4: CLASSIFICATION

This chapter is about the last stage in this project. This is regarding the classification

of the various features of the different subjects according to the class assigned to

them. A set of features is given into the classification stage for it to differentiate

among the features and be able to assign them to particular individuals. A class is

created for each individual and under the class a large set of data is given for the

system to train itself on the variations in data an individual might have. Based on

this logic the recognition stage is completed.

4.1 Neural Network

The method used for classification in this project in the neural network (NN). The

name given to this network is based on the biological network in which the

biological neurons are functionally connected in a nervous system [16]. In a similar

the NN that is going to be used in the classification stage has artificial neurons in

interconnected groups. These groups use the connections for computational

purposes. It will take in inputs and adjust its structure (weights) to generate a pattern

that connects the input to the output. The process is shown in the flow diagram

below.

Figure 4-1: Process flow diagram of a Neural Network [10]

In the flow diagram in figure 4-1 the network is given an input. Its function is to try

and match the output to the target by adjusting its weight until the output is a close

match to the target.

This process can be compared with the way the neurons in a human brain works. It

will works in its own way and find out a solution on its own. It does not rely on

33

anything to find out the solution. There are things that a human might not notice but

it will not be missed by the neural network. It processes information using the high

degree of interconnection in between its massive simple processing units which

work together to achieve massive parallel distributed processing. The neural

network is remarkable specially for its

Adaptive learning: Ability to learn to do a task based the input given to it

Self-organisation: While learning it organises the data given to it

Real time operation: Computations are carried out in parallel

Fault tolerance: Performance is degraded by partial degradation of a network

4.1.1 Pattern Recognition Network

There are several functions that can be implemented by the neural network. This

classification requires a pattern recognition tool. Thus the pattern recognition

function was chosen. A matrix where each of its columns contains the feature

vectors obtained from an individual. A target matrix is also fed along with it which

will have the same number of columns as the input matrix. The purpose of the target

matrix is to show in each of its column the class to which the corresponding column

in the input belongs to.

There are a total of 35 subjects from whom signals are collected. For each subject

the feature vectors are generated. A total of 10 trials are collected per subject. One

trial among the 10 for each subject is kept to be used as test input. The other 9 trials

from the 35 people are used to form the training set (input) matrix which has 315

columns of feature vectors. A target matrix of the size 35 by 315 is formed. Since

the training set matrix and the target matrix has the same number of columns thus

each column in the target matrix corresponds to a column in the training set matrix.

So each column in the target matrix can be assigned to an individual by setting a row

in each column of the target matrix to 1. If the first 9 feature vectors column in the

training set input belongs to person 1 then the class 1 in the target matrix will have

each of first 9 columns will have their first row as 1. A portion of the target matrix is

shown in the table in figure 4-2 for understanding.

34

Figure 4-2: A portion of the target matrix for training

The one set of data kept separately from each subject will lead to a set of 35 data‟s.

Thus a matrix of 35 columns of feature vectors will be formed to be used as test

input signals in the network that will be formed after training the network with the

training set matrix and the target matrix.

The input space is shared by all the neurons. One analog neuron is assigned for each

pattern that is to be recognised [2]. Several neural nets are working in parallel for

pattern recognition trying to find features that are common along the columns in the

matrix. These features are passed into another to find the closest match. This is how

the neural network works in the process of recognition. All that the network needs

is that it is given sufficient data for training itself for pattern recognition and an

output with proper structure algorithm.

4.1.2 Feed-forward Back propagation network

A feed forward back-propagation network is used. It is the simplest kind of neural

network. The signal travels from the input to the output which means in one

direction only. There is no feedback from any part to some other part in the network.

It has three layers which include an input layer, a hidden layer and an output layer.

35

Figure 4-3: Feed Forward Network

A multi-layer perceptron network is used in the feed forward network which makes

it so useful. Multiple layers are interconnected with each other where each layer is a

computational unit. There is one neuron in each layer which is connected to all the

other neuron in the subsequent layer.

The function of the network is to try and get a close match of the output to the

target. It will keep on training itself with different weights and parameters. The real

intelligence on the network lies in the adjusting of weights. A MLP uses several

kinds of learning method. The method of adjusting the weight uses a learning

algorithm known as Back Propagation (BP). An error function is pre-defined in the

network. The job of the network is to compare the output to the target matrix which

indicates the correct answer and compute the error. The error will then be sent back

through the network which the algorithm will use to adjust its weight of each this.

This process will be repeated until the error value is small. When the error becomes

small this mean the network has determined the target function.

The learning method used by the back-propagation network is called a supervised

leaning method [19]. It mainly back-propagates the errors through the network. It

has the ability to calculate the desired output from the input given. The method used

in back-propagation is derived from the delta rule. The Delta rule uses a gradient

descent algorithm from the family of Conjugate Gradient algorithms. The training

function used in this project is the scaled gradient conjugate (SCG).

The SCG is a second order Conjugate gradient algorithm. It assists in achieving the

target functions of the several variables. There are two main reasons for choosing it.

They are:

36

1. It uses a step sizing scaling mechanism which prevents it from line searching

per learning iteration and which in turn reduces the time consumption on its

part [7].

2. It uses the model trust region from the Levenberg-Marquardt algorithm with

the conjugate gradient approach. This makes SCG faster than any other

second order algorithm [20].

The parameters used in the training process are epochs, show, goal, time, min_grad,

max_fail, sigma and lambda.

The weight vector is a point in the weight space. Minimisation is an iterative process

to minimize the approximation of the function [2]. The error function in the SCG

with respect to the power w:

Eqy y) = E w) + E‟ w)Ty +

y

TE” w)y (1)

The critical point in Eqy(y) must be found to obtain the minimum of Eqy(y). The

equation for finding the critical point is:

Eqy(y) = E” w)y + E‟ w) = 0 (2)

The neural network has a limitation which is it works on trial and error basis. The

network has to be trained again and again by changing it parameters and adjusting

its weight. Although this process is a bit time consuming but ultimately it is able to a

give an optimum output.

4.2 Process of Classification

In this stage the every process done from the start with training the network and then

testing along with the results obtained will be explained in details.

4.2.1 Feature Set

ECG signals were collected from 35 subjects. The number of trials collected per

subject was 10. The signals were collected at a sampling frequency of 256Hz. That

means there are a total of 350 separate signals altogether. Now as was explained in

the previous sections out of the 10 trials per person 9 were taken for training the

system. That means 315 signals were used to train the system in total. The 1 signal

37

kept aside from each person will add together to make 35 signals to be used for

testing.

The 315 signals would each give a set of feature vector. A matrix is formed with 35

columns where each column represented features extracted from one signal. Thus

training matrix has 9 columns of features extracted per person. This will help to

form the target matrix where each of its columns will correspond to a certain subject

and the row with a value of one will indicate the person.

4.2.2 Training the network

At first when the neural network start is to choose the pattern recognition tool since

we are trying to recognize individual using certain feature collected from their

signals. Then the training matrix and it corresponding target matrix will be loaded.

The training matrix will be used as a set of input. The training matrix will show

which feature vector belong to which individual. The system will be simulated based

on these to matrices provided. The network has to be trained several times until the

percentage error is small. While training the network repeatedly some of the features

like the number of neurons may be altered to achieve a good performance and lower

error percentage from the system. As can be seen from the table below that each

class is assigned to one individual.

Table 4-1: Subjects assigned to classes

38

The network will be trained according to class to see the variations in features that

one subject might have. This will help for it to figure out the function for the

network. The different aptitudes of the network during training and testing is terms

of time, performance, success rate, failure rate, iterations and other criterions will be

elaborately discussed.

The neural network training interface is shown in figure 4-4. At the top we can see

the architecture of the network. The layer of the network can be seen. The 50 written

below the hidden layer shows 50. The default is 10. But by trial and error it was

found that 50 neurons at the hidden layer gave the best result. The goal of the mean

squared error is to reach 0 according to the network. The validation check shows 6

maximum failures. So this means if the maximum failure is 6 then the system will

stop its process. The gradient value also has to be accounted for because as soon as

its minimum value is met the system will halt too. From the training tool it can be

seen that the target was met after 97 iterations and at a gradient of 0.000746.

Figure 4-4: Neural Network Training tool

39

The plots section in the training tool shows different methods of analysing the

network. The first one is the performance graph. This is derived from the training,

validation and testing set. Now when we give the training matrix and target matrix

for training the network the system automatically takes a certain percentage of the

training matrix data for validation and testing. In this case the percentage stand at

15%. This is done because the validation will indicate the system to stop if they

maximum failure reaches the number of validation samples. That means the network

generalization is no longer showing any improvements. The test samples are mainly

used to check the performance of the network. That means to see how effective the

network is. The graph in figure 4-5 will show a representation of it.

Figure 4-5: Performance graph of the network

It can be seen from the graph in figure 4-5 that the training data has crossed the

threshold of the Mean Squared error long before the validation and test data. The test

data remains on the top showing that the test performance is good which means the

network is working in the correct part. The validation is right below the test curve.

Both the validation and test curve pass the best threshold at the same time. The

40

circle on the graph shows the point where the best iteration is achieved which is at

91 iterations. This is the returning point of the network.

Another graph to be observed is the training state graph. This graph in figure 4-6 is

plotted based on the training state during the training of the network. This will show

how the goal to meet an MSE of 0 was achieved. The graph will show the gradient

flow and validation checks in two separate graphs.

Figure 4-6: Training state graph of gradient and validation checks

4.2.3 Testing the network

The test samples taken by the network itself during the training are small. Thus it is

better to check with a larger database. The matrix that was created with a feature

vector set from 1 signal from each of the 35 person will be used to test the system. A

target matrix created for it will also be used.

After the target function is achieved with the lowest error percentage the network is

tested with a set of input. This is done to check how correct the network that is

41

formed is. It is necessary to check if the network works properly with a different set

of input. Thus an input matrix with a feature set obtained from each subject is used

to test the network. A target matrix corresponding to the input matrix is also formed.

The test matrix with 35 columns formed where each column represents the feature

vector extracted from each signal kept from a person. It must be made sure the trial

number used for the each of the 35 subjects is the same. If trial 10 is being used for

testing then all the input taken from all the subjects must be trial 10. There are

several aspects that will be studied in the process. The very first one will show the

percentage of recognition on a confusion matrix. The confusion matrix for 35

subjects is too large to see the details properly so a confusion matrix with 20 inputs

tested is given below in figure 4-7.

Figure 4-7: Confusion matrix with test results of 20 subjects

42

A confusion matrix is used to represent the target classes with respect to the number

of inputs. It will show the whole matrix of the results obtained. But the green

diagonal line shows the correct results. This line is an infusion of the target input

matrix with the number of classes. A 1 in each of the boxes along the line shows that

the individual has been recognised. A 0 in the box means that the individual could

not be recognised. In the blue which is in the last column of the last row shows the

percentage of recognition. The percentage value written in green colour in the blue

box shows the recognition percentage. And the value is set at 100%. That means 20

out of 20 people were correctly identified. This is a very good result.

In the same way the recognition rate for 35 subjects were found to be at 95%. This is

a very good result too. Thus it is an efficient biometric system.

43

CHAPTER 5: DISCUSSION

This chapter will provide and overall discussion on all that have been done starting

from the collection of ECG signals until recognition of an individual using a

person‟s ECG signal characteristics. There were some drawbacks, benefits and

observations made while working with this project. They will be discussed here

elaborately.

The first chapter discusses the Heart structure and also explains the procedure of

obtaining ECG from it. It shows the different ways that ECG can be collected from a

human. In this experiment the ECG is obtained using Lead 1 forming an Einthoven‟s

triangle. This is one thing that is different from many other projects where the ECG

is taken in other different forms.

The second chapter discusses a previous study where a biometric recognition system

is made. Its process is described so give an overview of the processes and steps

involved in creating a biometric recognition system. The other reason for writing

about that specific paper was to explain how the PCA method was used previously.

PCA was going to be used in this project in a different way. That chapter also

described the initial proposed methodology. The initial methodology had a pre-

processing stage but the feature extraction part provided two possible ways to handle

it. One was fiducial feature detection and the other was the non-fiducial feature

extraction. The fiducial feature extraction was related to extracting temporal,

amplitude and angular features. The non-fiducial feature was based on extracting the

auto correlated data and reducing the dimension of the autocorrelated data using

PCA.

The third chapter showed the methodology that was followed. In the pre-processing

stage a wavelet filter (daubechies) was used instead of the bandpass filter. This was

done because the wavelet filter was found to have caused lower damage to the signal

when filtering it. Then the signal was windowed to avoid the possibility of spectral

leakage. The other reason was that it was found that the recognition rate with

windowing was better.

In the feature extraction part both the fiducial and non-fiducial points were tried but

the non-fiducial features gave better result. Fiducial points were not chosen to work

44

with because this process chose specific data from the signal and worked with them.

This limits or ignores other details that the signal might have. Then the

programming is also longer and time consuming. Whereas the non-fiducial points

extracted by autocorrelation are more global. It does not focus on specific attributes.

It takes out all the important data by itself from the signal and ignores the

insignificant ones. This is another disadvantage in fiducial points because we do not

know which features will be good for usage. The features in ECG are always

varying in the signals even for the same person. Sometimes some features do not

yield good results. This adds another reason to why choosing fiducial points is not a

good idea. So ultimately the chosen method for feature extraction is autocorrelation.

The autocorrelated feature extraction extracts a large number of features. So we

focus on 400 data points of it taken from its maximum point to the right. This is the

region of QRS complex which was found not to change much for the same person a

lot over time. Still data from autocorrelation is a lot so PCA is used to further reduce

the dimension. The Hotelling T2 statistic obtained is the focus here. The Hotelling T

2

statistic data extracted from the autocorrelated data of each signal is taken as the

final features to be used for the classification stage.

In the fourth chapter the features extracted for each person will be used to classify

and determine the correct individual. In order to do this a neural network is chosen.

This is chosen because it is a very powerful tool which can see or point out things

that a human can miss. It works like a human brain biological neural network. Each

neuron is a computational unit and is interconnected to other neurons in the

subsequent layers. So the results are massive simple processing units which work

together to achieve massive parallel distributed processing. The requirement from it

is that it should be able to recognise a pattern of data and specify it to a certain class.

This is why a pattern recognition tool in the neural network is chosen. A feed

forward network is chosen because it is the simplest and easiest among all the other

networks. The network uses Multi-layer perceptron. Each layer in MLP is a

computational unit. Thus there are multiple computational units working together to

achieve the best results. The MLP uses a learning algorithm known as the Back-

propagation which has an error function in it. The error function calculates the error

every time the network is trained with data. The target is to minimise the error. The

45

back-propagation uses supervised learning to send the error back through the

network until the target is met. The error minimisation is done using the scaled

conjugate gradient function for its benefits like it does not use line searching per

learning iteration which reduces the time consumption on its part and its faster than

any other second order algorithm.

Chapter 4 also shows the various performance of the network from different

perspective and they have shown good result as well. So all in all it is a good idea to

choose neural network for the classification stage.

The last step in chapter 4 where after the network is created it is tested with some

input and it has a high recognition rate. For 20 individuals it has given a recognition

rate of 100%. And with 35 people it has given 95% recognition rate. Although all

the 35 individuals have not been identified correctly but still the percentage is

comparatively high and good. It can be concluded that it is a good biometric

recognition system.

46

CHAPTER 6: CONCLUSION

The method of using fiducial detection in recognition is not a reliable option. Rather

non-fiducial is showing good result and reliable and durable performance. It has

computational advantages too compared to the fiducial detection. This is faster than

the fiducial detection algorithm.

In a previous research paper a biometric recognition system was designed using

autocorrelation (AC) based feature extraction and dimensionality reduction using

Discrete Cosine Transform(DCT). The research of the AC/DCT was based on 11

subjects and the recognition achieved was 90%. The methods used in this project are

1. The filtering of the signal which uses a wavelet filter which damages the

signal less

2. The whole filtered signal is windowed to avoid spectral leakage.

3. The dimensionality reduction feature uses a different algorithm known as the

Principal Component Analysis(PCA) from which the Hotelling T2 statistic

data is used for classification

The results obtained for a scope of 35 individuals gave a result of 95%. Thus it can

be concluded that this is a better system than the AC/DCT.

6.1 Future work and recommendation

The system has shown good performance and result. Thus it would be good idea to

carry out further research on this in the future. The results that have been shown

with 35 individuals are not 100%. When the numbers of individuals were 20 the

recognition was 100%. For higher individuals the percentage has gone down. This is

something that can be improved in the future.

A possible way to improve this would be gather a larger database. ECG signals tend

to change over time. This means more signals have to be collected from the

individuals over a long period of time so that as many variations as possible in the

signal of a person can be captured. Then train the network so that the neural network

has a better idea of the variability of the features of a person. Thus the network can

base its decision from a large range of data.

47

References

[1] Augustyniak, P., & Tadeusiewicz, R. (2009). Ubiquitous Cardiology: Emerging

Wireless Telemedical Applications. United States of America: Information

Science Reference. pp.11-54

[2] MADZRI, F. R. B.(2010). Study and Classification of ECG signals(Unpublished

Master‟s Thesis). Multimedia University, Cyberjaya, Malaysia

[3] Jianchu Yao,Senior Member IEEE and Yongbo Wan, A Wavelet Method for

Biometric Identification Using Wearable ECG Sensors, Proceedings of the 5th

International Workshop on Wearable and Implantable Body Sensor Networks,

in conjunction with The 5th International Summer School and Symposium on

Medical Devices and Biosensors,The Chinese University of Hong Kong,

HKSAR, China. Jun 1-3, 2008

[4] Konstantinos N. Plataniotis, Dimitrios Hatzinakos, Jimmy K. M. Lee, Edward S.

Rogers Sr. Department of Electrical And Computer Engineering, University Of

Toronto, ECG Biometric Recognition Without Fiducial Detection, 2006.

[5] YongjinWang, Foteini Agrafioti, Dimitrios Hatzinakos, and Konstantinos N.

Plataniotis, Analysis of Human Electrocardiogram for Biometric Recognition,

August 2007

[6] Yogendra Narain Singh and Phalguni Gupta, ECG to Individual Identification

[7] Zulhadi Zakaria, Nor Ashidi Mat Isa, Shahrel A. Suandi, Member IEEE,

Universiti Sains Malaysia, A study on the neural network training algorithm for

Multiface detection in static images, 24-26 February 2010.

[8] Yongbo Wan and Jianchu Yao, Senior Member, IEEE, A Neural Network

to Identify Human Subjects with Electrocardiogram Signals,2008

[9] Ognian Boumbarov, Yuliyan Velchev, Strahil Sokolov, Technical University of

Sofia, 8, Kl. Ohridski str., Sofia 1000, Bulgaria, ECG Personal Identification in

Subspaces using Radial Basis Neural Networks, IEEE International Workshop

on Intelligent Data Acquisition and Advanced Computing Systems: Technology

and Applications, 21-23 September 2009

[10] Robyn Ball and Philippe Tissot, Division of Nearhsore Research, Texas A&M

University, Demonstration of Artificial Neural Network in Matlab

[11] S.Zahra Fatemian, Student Member, IEEE, Dimitrios Hatzinakos, Senior

Member, IEEE, The Edward S. Rogers SR. Department of Electrical and

Computer Engineering,University of Toronto, 10 King's College Circle,

Toronto, A New ECG Feature Extractor For Biometric Recognition

[12] Y. Gahi, M. Lamrani, A. Zoglat, M. Guennoun, B. Kapralos, K. El-Khatib,

Département Math-Info, Faculté des Sciences de Rabat, 4 Avenue Ibn Battouta

48

B.P. 1014 RP, Rabat, Maroc, University of Ontario Institute of Technology,

2000 Simcoe Street North, Oshawa, Ontario, Canada, Biometric Identification

System Based on Electrocardiogram Data

[13] Loh Sik Hou, Khazaimatol S. Subari and Syed Syahril, Faculty of Engineering,

Multimedia University, Cyberjaya, Selangor, Malaysia, QRS complex of ECG-

based Biometrics in Two Level Classifier

[14] Supervised Learning in Neural Networks, 2012 Marcus bros. Retrieved from

http://www.neuralnetworksolutions.com/nn/supervised3.php

[15] MATLAB 'spectrogram' params, Retrieved from

http://stackoverflow.com/questions/5887366/matlab-spectrogram-params

[16] Neural network, Wikipedia, Retrieved from

http://en.wikipedia.org/wiki/Neural_network

[17] Artificial neural network, Wikipedia, Retrieved from

http://en.wikipedia.org/wiki/Artificial_neural_network

[18] Feedforward neural network, Wikipedia, Retrieved from

http://en.wikipedia.org/wiki/Feedforward_neural_network

[19] Backpropagation, Wikipedia, Retrieved from

http://en.wikipedia.org/wiki/Backpropagation

[20] Scaled Conjugate Gradient (SCG), 1995, Retrieved from

http://www.csc.kth.se/~orre/snns-

manual/UserManual/node243.html#SECTION0010172000000000000000

[21] Justin Leo Cheang Loong, Khazaimatol S Subari, Rosli Besar and Muhammad

Kamil Abdullah, A New Approach to ECG Biometric Systems: A Comparitive

Study between LPC and WPD Systems, 2010

49

Appendix A

Matlab Code

1. Training Program S = load('trainingsamples.mat');

C = struct2cell(S);

for m= 1:315

A = cell2mat(C(m));

N = 20*256;

my_signal=A(1:N,5);

x= my_signal;

% Normalised Signal [-1 1]

x = x - mean (x ); % cancel DC conponents

x = x/ max( abs(x));

N = length (x); % Signal length

t = [0:N-1]/256; figure (1)

plot(t,x);

% Filtered Signal

[thr,sorh,keepapp] = ddencmp('den','wv',x);

xd = wdencmp('gbl',x,'db6',3,thr,sorh,keepapp);

w = hamming(N,'periodic');

sig=w.*xd;

% Autocorrelation

r=xcorr(sig,'coeff');

k=length(r);

txcorr = [0:k-1]/256;

plot(txcorr,r)

[x y]=max(r);

a=txcorr(y);

ny=y+400;

b=txcorr(ny);

xnew= r(y:ny);

k=length(xnew);

txcorr = [0:k-1]/256;

plot(txcorr,xnew);

% Principal Component Analysis

[coeff(:,m),score(:,m),latent(:,m),tsquare(:,m)] = ...

princomp(xnew);

plot(txcorr, tsquare(:,m));

end

50

2. Testing program

S = load('trainingsamples.mat');

C = struct2cell(S);

for m= 1:35

A = cell2mat(C(m));

N = 20*256;

my_signal=A(1:N,5);

x= my_signal;

% Normalised Signal [-1 1]

x = x - mean (x ); % cancel DC conponents

x = x/ max( abs(x));

N = length (x); % Signal length

t = [0:N-1]/256; figure (1)

plot(t,x);

% Filtered Signal

[thr,sorh,keepapp] = ddencmp('den','wv',x);

xd = wdencmp('gbl',x,'db6',3,thr,sorh,keepapp);

w = hamming(N,'periodic');

sig=w.*xd;

% Autocorrelation

r=xcorr(sig,'coeff');

k=length(r);

txcorr = [0:k-1]/256;

plot(txcorr,r)

[x y]=max(r);

a=txcorr(y);

ny=y+400;

b=txcorr(ny);

xnew= r(y:ny);

k=length(xnew);

txcorr = [0:k-1]/256;

plot(txcorr,xnew);

% Principal Component Analysis

[coefftest(:,m),scoretest(:,m),latenttest(:,m),tsquare

...test(:,m)] = princomp(xnew);

plot(txcorr,tsquare(:,m));

end

51

3. Training and Input target matrix

target=zeros(35,315);

training_target=[target(1,1:9)+1 target(1,10:315)

target(2,1:9) target(2,10:18)+1 target(2,19:315)

target(2,1:18) target(2,19:27)+1 target(2,28:315)

target(2,1:27) target(2,28:36)+1 target(2,37:315)

target(2,1:36) target(2,37:45)+1 target(2,46:315)

target(2,1:45) target(2,46:54)+1 target(2,55:315)

target(2,1:54) target(2,55:63)+1 target(2,64:315)

target(2,1:63) target(2,64:72)+1 target(2,73:315)

target(2,1:72) target(2,73:81)+1 target(2,82:315)

target(2,1:81) target(2,82:90)+1 target(2,91:315)

target(2,1:90) target(2,91:99)+1 target(2,100:315)

target(2,1:99) target(2,100:108)+1 target(2,109:315)

target(2,1:108) target(2,109:117)+1 target(2,118:315)

target(2,1:117) target(2,118:126)+1 target(2,127:315)

target(2,1:126) target(2,127:135)+1 target(2,136:315)

target(2,1:135) target(2,136:144)+1 target(2,145:315)

target(2,1:144) target(2,145:153)+1 target(2,154:315)

target(2,1:153) target(2,154:162)+1 target(2,163:315)

target(2,1:162) target(2,163:171)+1 target(2,172:315)

target(2,1:171) target(2,172:180)+1 target(2,181:315)

target(2,1:180) target(2,181:189)+1 target(2,190:315)

target(2,1:189) target(2,190:198)+1 target(2,199:315)

target(2,1:198) target(2,199:207)+1 target(2,208:315)

target(2,1:207) target(2,208:216)+1 target(2,217:315)

target(2,1:216) target(2,217:225)+1 target(2,226:315)

target(2,1:225) target(2,226:234)+1 target(2,235:315)

target(2,1:234) target(2,235:243)+1 target(2,244:315)

target(2,1:243) target(2,244:252)+1 target(2,253:315)

target(2,1:252) target(2,253:261)+1 target(2,262:315)

target(2,1:261) target(2,262:270)+1 target(2,271:315)

target(2,1:270) target(2,271:279)+1 target(2,280:315)

target(2,1:279) target(2,280:288)+1 target(2,289:315)

target(2,1:288) target(2,289:297)+1 target(2,298:315)

target(2,1:297) target(2,298:306)+1 target(2,307:315)

target(2,1:306) target(2,307:315)+1 ];

target_test=zeros(35,35);

input_target=[target_test(1,1)+1 target_test(1,2:35)

target_test(2,1) target_test(2,2)+1

target_test(2,3:35)

target_test(2,1:2) target_test(2,3)+1

target_test(2,4:35)

target_test(2,1:3) target_test(2,4)+1

target_test(2,5:35)

52

target_test(2,1:4) target_test(2,5)+1

target_test(2,6:35)

target_test(2,1:5) target_test(2,6)+1

target_test(2,7:35)

target_test(2,1:6) target_test(2,7)+1

target_test(2,8:35)

target_test(2,1:7) target_test(2,8)+1

target_test(2,9:35)

target_test(2,1:8) target_test(2,9)+1

target_test(2,10:35)

target_test(2,1:9) target_test(2,10)+1

target_test(2,11:35)

target_test(2,1:10) target_test(2,11)+1

target_test(2,12:35)

target_test(2,1:11) target_test(2,12)+1

target_test(2,13:35)

target_test(2,1:12) target_test(2,13)+1

target_test(2,14:35)

target_test(2,1:13) target_test(2,14)+1

target_test(2,15:35)

target_test(2,1:14) target_test(2,15)+1

target_test(2,16:35)

target_test(2,1:15) target_test(2,16)+1

target_test(2,17:35)

target_test(2,1:16) target_test(2,17)+1

target_test(2,18:35)

target_test(2,1:17) target_test(2,18)+1

target_test(2,19:35)

target_test(2,1:18) target_test(2,19)+1

target_test(2,20:35)

target_test(2,1:19) target_test(2,20)+1

target_test(2,21:35)

target_test(2,1:20) target_test(2,21)+1

target_test(2,22:35)

target_test(2,1:21) target_test(2,22)+1

target_test(2,23:35)

target_test(2,1:22) target_test(2,23)+1

target_test(2,24:35)

target_test(2,1:23) target_test(2,24)+1

target_test(2,25:35)

target_test(2,1:24) target_test(2,25)+1

target_test(2,26:35)

target_test(2,1:25) target_test(2,26)+1

target_test(2,27:35)

target_test(2,1:26) target_test(2,27)+1

target_test(2,28:35)

target_test(2,1:27) target_test(2,28)+1

target_test(2,29:35)

53

target_test(2,1:28) target_test(2,29)+1

target_test(2,30:35)

target_test(2,1:29) target_test(2,30)+1

target_test(2,31:35)

target_test(2,1:30) target_test(2,31)+1

target_test(2,32:35)

target_test(2,1:31) target_test(2,32)+1

target_test(2,33:35)

target_test(2,1:32) target_test(2,33)+1

target_test(2,34:35)

target_test(2,1:33) target_test(2,34)+1

target_test(2,35)

target_test(2,1:34) target_test(2,35)+1 ];

4. Classification testing

outputs = sim(net,tsquaretest);

outputs = round(outputs);

figure(1);

plotconfusion(input_target,outputs)