EFFECTIVE ECG FINGERTIP SENSOR BASED

BIOMETRIC IDENTIFICATION

BY

TUERXUNWAILI

A thesis submitted in fulfilment of the requirement for the

degree of Doctor of Philosophy in Computer Science

Kulliyyah of Information and Communication Technology

International Islamic University Malaysia

OCTOBER 2018

ii

ABSTRACT

After 9/11, security and safety become one of the main concerns of governments around

the world. Automatic accurate individual identification and authentication systems are

becoming more critical in day-to-day activities like money transactions, access control,

travel, medical services, and numerous others. The most prominent individual

identification methods are ID cards, passwords, fingerprint, tokens, and signatures.

Despite the large-scale deployment, these methods are vulnerable to identity

falsification. The electrocardiogram (ECG) signal is very robust against identity

forgery. However, many recent ECG systems demand longer time for recognition,

which makes it hard to deploy an ECG based biometric system as a commercial product.

This thesis studies a fast and effective ECG fingertip identification system in real time.

The objective of the study is to reduce identification time. It is implemented in two

steps, first is feature extraction, 3 features in a heartbeat are identified, they are simple

but prominent features with discriminate characters. Second is segmentation where

signals are sliced into 5 heartbeats to reduce the acquisition time. Then, 5 classification

algorithms used to achieve up to 96% accuracy. A popular deep learning algorithm is

also used for classification purpose and yields 94.12% accuracy. Through experiments,

it is concluded this fingertip ECG recognition system can be used as an identifier for a

small population.

iii

خلاصة البحثABSTRACT IN ARABIC

، أصبح الأمن والسلامة أحد الاهتمامات الرئيسية للحكومات في جميع أنحاء العالم. أصبحت أنظمة التعرف 11/9بعد وصول والسفر ر أهمية في الأنشطة اليومية مثل المعاملات المالية والتحكم في العلى الهوية والتوثيق الأوتوماتيكية الدقيقة أكث

والخدمات الطبية والعديد من الأنشطة الأخرى. وأبرز وسائل تحديد الهوية الفردية هي بطاقات الهوية ، وكلمات المرور ، رضة واسع النطاق ، إلا أن هذه الأساليب عوبصمات الأصابع ، والرموز المميزة ، والتوقيعات. على الرغم من الانتشار ال

( قوية جدًا ضد تزوير الهوية. ومع ذلك ، فإن العديد من أنظمة تخطيط ECGلتزوير الهوية. تعتبر إشارة تخطيط القلب )القلب الحديثة تتطلب وقتًا أطول للاعتراف بها ، مما يجعل من الصعب نشر نظام البيومترية القائم على تخطيط القلب

الهدف من في الوقت الحقيقي. ECGنتج تجاري. هذه الأطروحة دراسة سريعة وفعالة نظام التعرف على الإصبع كمميزات في ضربات 3الدراسة هو تقليل وقت التحديد. يتم تنفيذه في خطوتين ، الأول هو استخراج ميزة ، يتم تحديد

دقات 5 الثاني هو التقسيم حيث يتم تقسيم الإشارات إلىالقلب ، فهي ميزات بسيطة ولكنها بارزة مع أحرف تمييزية. . كما تستخدم ٪ 99المستخدمة لتحقيق ما يصل إلى دقة 5قلب لتقليل وقت الاقتناء. ثم ، خوارزميات التصنيف

. من خلال التجارب ، استنتج أنه يمكن استخدام ٪ 91.19خوارزمية التعلم العميق الشعبية لغرض التصنيف وتنتج دقة كمعرف لعدد صغير من السكان. ECGنظام التعرف على

iv

APPROVAL PAGE

The thesis of the TuerxunWaili has been approved by the following:

_________________________________

Rizal Mohd Nor

Supervisor

_________________________________

Khairul Azami Sidek

Co-Supervisor

_________________________________

Imad Fakhri Taha Alshaikhli

Internal Examiner

_________________________________

Syed Ahmad Sheikh Aljunid

External Examiner

_________________________________

Teddy Mantoro

External Examiner

_________________________________

Ismaiel Hassanein Ahmed Mohamed

Chairman

v

DECLARATION

I hereby declare that this thesis is the result of my own investigations, except

Where otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

TuerxunWaili

Signature........................................................... Date .........................................

vi

COPYRIGHT PAGE

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF

FAIR USE OF UNPUBLISHED RESEARCH

EFFECTIVE ECG FINGERTIP SENSOR BASED

BIOMETRIC IDENTIFICATION

I declare that the copyright holders of this thesis are jointly owned by the

student and IIUM.

Copyright © 2018 TuerxunWaili and International Islamic University Malaysia. All rights

reserved.

No part of this unpublished research may be reproduced, stored in a retrieval

system, or transmitted, in any form or by any means, electronic, mechanical,

photocopying, recording or otherwise without prior written permission of the

copyright holder except as provided below

1. Any material contained in or derived from this unpublished research

may be used by others in their writing with due acknowledgment.

2. IIUM or its library will have the right to make and transmit copies

(print or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieved

system and supply copies of this unpublished research if requested

by other universities and research libraries.

By signing this form, I acknowledged that I have read and understand the IIUM

Intellectual Property Right and Commercialization policy.

Affirmed by TuerxunWaili

……..…………………….. ……..…………

Signature Date

vii

ACKNOWLEDGMENTS

Firstly, it is my utmost pleasure to dedicate this work to my dear wife Zainura, who

have been taking care of our newborn babies plus two other children day and night in

order to give me enough time to study: thank you for your support and patience.

Secondly, I thank my supervisors Dr. Rizal Mohd Nor and Assoc. Prof. Dr.

Khairul Azami Sidek for giving me the help necessary during all these years.

Importantly, special thanks to Prof. Abdul Wahab Abdul Rahman for his help.

About his encouragement and leadership, I will be forever grateful.

viii

TABLE OF CONTENTS

Abstract .................................................................................................................... ii Abstract in Arabic .................................................................................................... iii Approval Page .......................................................................................................... iv Declaration ............................................................................................................... v

Copyright Page ......................................................................................................... vi Acknowledgments .................................................................................................... vii Table of Contents ..................................................................................................... viii

List of Tables ........................................................................................................... xi List of Abbreviations ............................................................................................... xiii List of Figures .......................................................................................................... xiv

CHAPTER ONE : INTRODUCTION ................................................................. 1 1.1 Introduction ........................................................................................................ 1

1.1.1 Biological Understanding of Electrocardiogram .......................... 4 1.1.2 The Fiducial Points Dependent Identification .............................. 8 1.1.3 The Fiducial Points Independent Identification ............................ 9

1.2 Statement of The Problem ................................................................................. 11

1.3 Objectives .......................................................................................................... 13 1.3.1 Reducing time in signal acquisition step....................................... 13

1.3.2 Shorten time by taking as fewer features as possible.................... 13

1.3.3 Shorten signal length in the segmentation stage ........................... 14

1.3.4 Classification, to design an efficient algorithm ............................ 14 1.4 Research Questions ............................................................................................ 14

1.5 Research Hypotheses ......................................................................................... 15 1.6 Significance of The Study .................................................................................. 15 1.7 Limitations of The Study ................................................................................... 16

1.8 Structure of the thesis ......................................................................................... 17 1.9 Definitions of Terms .......................................................................................... 17

1.9.1 Electrocardiogram (ECG/EKG) .................................................... 17 1.9.2 Electrocardiography ...................................................................... 17

1.9.3 The QRS complex ......................................................................... 18 1.9.4 Multi-Layer Perceptron (MLP) ..................................................... 18 1.9.5 The k-Nearest Neighbours (k-NN) ............................................... 18

1.9.6 The Ramdom Forest (RF) ............................................................. 18 1.9.7 The Support Vector Machine (SVM) ............................................ 19 1.9.8 The Deep Learning (DL) ............................................................... 19

CHAPTER TWO : LITERATURE REVIEW ................................................... 20 2.1 Introduction to Literature Review ...................................................................... 20 2.2 ECG Signal Acquisition ..................................................................................... 21

2.2.1 12 lead ECG Sensor ...................................................................... 21 2.2.2 The Publicly Available ECG Databases ....................................... 22 2.2.3 Long-Term ST (LTSTDB) database ............................................. 25

2.2.4 Two Lead ECG Sensor ................................................................. 25 2.2.5 One Lead ECG sensor ................................................................... 27 2.2.6 The conclusion of ECG signal acquisition .................................... 32

ix

2.3 Filtering .............................................................................................................. 32

2.3.1 Conclusion of filtering .................................................................. 35 2.4 Feature extraction ............................................................................................... 36

2.4.1 Fiducial Dependent Features ......................................................... 37

2.4.2 Fiducial Independent Features ...................................................... 48 2.5 Segmentation Practices ...................................................................................... 51 2.6 Classification Algorithms of ECG Identification .............................................. 54

2.6.1 K-nearest Neighbour (KNN) Classifier ........................................ 55 2.6.2 The Support Vector Machine (SVM) ............................................ 57

2.6.3 Random Forest Classifier (RFC)................................................... 58 2.6.4 Multi-layer Perceptron (MLP) ...................................................... 59 2.6.5 Deep Learning (DL) ...................................................................... 63

2.7 Research gap ...................................................................................................... 65

2.7.1 Segmentation: long segments........................................................ 65 2.7.2 Feature extraction: many features ................................................. 66

2.8 Chapter Summary .............................................................................................. 69

CHAPTER THREE : METHODOLOGY AND FRAMEWORK .................. 72 3.1 Introduction ........................................................................................................ 72 3.2 Stage 1 of Framework: Signal Acquisition From Finger ................................... 73 3.3 Stage 2 of Framework: Filtering ........................................................................ 75

3.4 stage 3 of Framework: Normalization ............................................................... 77 3.5 Stage 4 of Framework: Segmentation ................................................................ 78 3.6 Stage 5 of Framework: Feature Extraction ........................................................ 79

3.6.1 Peak detection practice .................................................................. 79 3.7 stage 6 of framework: Classification ................................................................. 83

3.8 Chapter Summary .............................................................................................. 83

CHAPTER FOUR : EXPERIMENTATION AND RESULTS ........................ 85 4.1 Introduction ........................................................................................................ 85 4.2 Feature Extraction .............................................................................................. 86

4.2.1 Unavailability of P wave and its impact on fiducial features ....... 86 4.2.2 Unavailability of T wave............................................................... 87 4.2.3 What Features in QRS Complex? ................................................. 90

4.2.4 Results of Feature Extraction ........................................................ 99 4.2.5 Inspiration for Decisive Features .................................................. 101

4.3 Origin of ECG Data ........................................................................................... 103 4.3.1 The dataset I: PTB Database ......................................................... 103 4.3.2 Dataset II: PTB Database Extended .............................................. 104

4.3.3 Dataset III: Fingertip Database ..................................................... 105 4.4 Experiment I: classification with Multilayer Perceptron (MLP) ....................... 106

4.4.1 Experiment results......................................................................... 108 4.5 Experiment II: classification with SVM, Random Forest and kNN .................. 109

4.5.1 Experiment results......................................................................... 111 4.6 Experiment III: classification with deep learning .............................................. 113

4.6.1 Experiment Result of Deep Learning ............................................ 116 4.7 Result of Experiments ........................................................................................ 117

CHAPTER FIVE : CONTRIBUTIONS AND FUTURE WORK .................... 119 5.1 Summary ............................................................................................................ 119 5.2 Scientific Contribution ....................................................................................... 121 5.3 Limitations ......................................................................................................... 123

x

5.4 Future work and suggestions ............................................................................. 124

5.4.1 Feature work 1: ............................................................................. 124 5.4.2 Feature work 2: ............................................................................. 124 5.4.3 Feature work 3: ............................................................................. 124

5.4.4 Suggestion: .................................................................................... 125

REFERENCES ....................................................................................................... 126

xi

LIST OF TABLES

Table 1.1 Problems with current commercial biometric security systems 3

Table 1.2 Relations between electrical and mechanical activity of the heart 7

Table 1.3. ECG research takes too long time in identification process 12

Table 2.1 MIT-BIH database 26

Table 2.2 Fiducial features of Biel et.al (30) 43

Table 2.3 Gahi’s extracted features (24) 44

Table 2.4 Extracted features of Zhang 45

Table 2.5 Past works based on feature numbers 47

Table 2.6 Sample length of ECG in some previous studies 54

Table 2.7 Sample length of ECG in some previous studies 65

Table 2.8 feature numbers in some previous studies 67

Table 4.1 Unavailability of P wave and its effect 87

Table 4.2 Unavailability of T wave and its effect 88

Table 4.3 Remaining features in QRS complex 88

Table 4.4 RQ/RS values of subject 90_418 93

Table 4.5 Prominence of features of subject 90_418 (horizontal) 93

Table 4.6 Prominence of features of subject 87_s0330lrem (horizontal) 94

Table 4.7 RQ/RS values of subject 90_418 (vertical) 94

Table 4.8 Prominence of features of subject 90_418 (vertical) 95

Table 4.9 Prominence of features of subject 87_330 (vertical) 95

Table 4.10 Surface of the triangle formed by Q, R and S peaks 97

Table 4.11 Stored triangle surfaces in database 100

xii

Table 4.12 Triangle surfaces as new input 100

Table 4.13 ECG signals from fingertip sensor 105

Table 4.14 Confusion matrix 108

Table 4.15 Evaluation of results of SVM, RFC, kNN 111

Table 4.16 Identification accuracy of SVM, kNN and RFC 112

Table 4.17 Conclusion of Experiments 117

Table 5.1 Experiment results in this study 120

xiii

LIST OF ABBREVIATIONS

CNN Convolutional neural network

DL Deep Learning

DBNN Decision Based Neural Network

ECG/EKG Electrocardiogram

EMG Electromyography

EEG Electroencephalogram

PPG Phonocardiogram

IoT Internet of Things

kNN K-nearest neigbor

MLP Multilayer perceptron

PTB Physikalisch-Technische Bundesanstalt

RF Random forest

RNN Recursive Neural Network

SVM Support Vector Machine

NSRDB MIT-BIH Normal Sinus Rhythm Database

xiv

LIST OF FIGURES

Figure 1.1 Elements of the cardiac conduction system (Diehl, 2011) 5

Figure 1.2 Conventional electrocardiographs and its usage 5

Figure 1.3 ECG signal of subject 16539 in NSRDB database 6

Figure 1.4 P wave, QRS and T wave in a single heartbeat 6

Figure 1.5 28 Fiducial Features from a heartbeat 10

Figure 2.1 Interface of the HeartID ECG acquisition systems 28

Figure 2.2 palm ECG sensor and its software 39

Figure 2.3 ET-600 sensor and ECG recorded 31

Figure 2.4 Chan’s homemade fingertip ECG sensor 32

Figure 2.5 Frequency range of P, QRS and T waves 33

Figure 2.6 relations between feature numbers and accuracy 36

Figure 2.7 Israel’s fiducial features 44

Figure 2.8 Khalil’s feature points 46

Figure 2.9 the multi-layer perceptron network topology 60

Figure 3.1 Framework for Fingertip ECG recognition system 72

Figure 3.2 Fingertip ECG sensor and Signal recorded with the sensor 73

Figure 3.3 Some Commercial handheld ECG sensors in the market 74

Figure 3.4 an original ECG signal with power line interference 75

Figure 3.5 ECG signal free from power line interference 75

Figure 3.6 ECG signal when muscle activity happens 76

Figure 3.7 ECG with Baseline Wandering 76

Figure 3.8 ECG after Baseline Wander cleaned 77

xv

Figure 3.9 ECG signal and R-peaks 81

Figure 3.10 Inverted ECG signal and S-peaks 81

Figure 3.11 Inverted ECG signal and Q-peaks 82

Figure 4.1 ECG without T peak 88

Figure 4.2 Remaining features in QRS complex 89

Figure 4.3 Three level of features in QRS complex 90

Figure 4.4 Imaginary triangles of Q, R and S peaks 91

Figure 4.5 Distance of peaks horizontally 93

Figure 4.6 Distance of peaks vertically 94

Figure 4.7 Triangle formed by Q, R and S peaks 97

Figure 4.8 Steps of the triangle surface method 99

Figure 4.9 Khalil Ibrahim’s data points 102

Figure 4.10 chosen features in a heartbeat 103

Figure 4.11 MLP network Topology 107

Figure 4.12 Comparison of features 109

Figure 4.13 General architecture of DL Classifiers 114

Figure 4.14 an actual deep neural net for ECG 115

Figure 5.1 Feature numbers and accuracy 121

Figure 5.2 chosen three features 122

1

CHAPTER ONE : INTRODUCTION

1.1 INTRODUCTION

Automatic and accurate individual identification and authentication systems are

becoming more critical in day-to-day activities like money transactions, access control,

travel, medicinal services, and numerous others. The most prominent individual

identification methods are ID cards, passwords, fingerprint, tokens, and signatures.

Despite the large scale deployment associated with these kinds of methods, the means

for verification is usually thing-based or information-based which raises genuine

concerns with respect to the danger of identity fraud (Agrafioti, 2011).

In a report of the US Federal Trade Commission published in 2009, identity online fraud

is classified as the number one issue with about 720,000 cases. Bank cards scam

constitutes to 17%, falsification of government documents constitutes to about 16%,

and utility fee cheating is about 15%, occupation scams about 13% and several other

offenses. Among the reported cases, a true identity fraud comprises just a tiny fraction

of the complaints, while identity theft, by all accounts, proved to be the biggest danger

(Commission & others, 2012).

There are following security concerns linked to user identity:

1. Privacy and data safety: A device looks into user ID in order to offer a tailored

service or information. Collect, protect user information and share with other

parties safely remains open research area to be studied.

2. Access control and permission: Permission is given if an object is identified as

an authorized user. Access control is about controlling ways to resource by

denying or allowing user based on given criteria. Authorization is typically

2

given by the use of access controls. Authorization and access control are

important to build up a secure connection between human, devices, and services

(Abomhara, 2014).

There are many possible ways that attack can occur. In short, they are signal

modification, traffic analysis, Denial of Service (DoS), identity fraud and so on. In order

to avoid these threats and to permit authorized use only (Riahi, 2014), It needs an attack

resistant security solution (Abomhara, 2014). Currently, there are four categories of

identification methods (Brainard, 2006):

1. What you know – e.g., the password or passphrase. This is knowledge-

based system.

2. What you do -- e.g., how one signs one's name or speak.

3. What you have -- e.g., a token such as a key or a certificate or such as a

driver's license. This is entity-based system.

4. What you are -- e.g., one's face or other biometric attributes such as a

fingerprint.

The entity-based systems rely on “what he/she possesses” and knowledge-based

identification systems “what he/she remembers” are not attack resistant solutions.

Because they can be easily misplaced, shared, or stolen, forgotten (El-Basioni, El-kader,

& Abdelmonim, 2013).

A more promising approach is to use biometric systems. Biometric recognition

is the science of establishing the identity of individuals based on their measurable

biological (anatomical or physiological) or behavioral characteristics (Shah & others,

2016). Examples of biological biometrics modalities include fingerprint, hand

geometry, iris, face, and ear. Examples of behavioral biometrics modalities are gait,

3

signature, and keystroke dynamics (Shen, Chang, Wang, & Fang, 2010). Biometric

recognition forms a strong bond between a person and his identity as biometric traits

cannot be easily shared, lost, or duplicated. Hence, biometric recognition is

fundamentally superior and more resistant to social engineering attacks than tokens and

passwords (Mudholkar, Shende, & Sarode, 2012). Since biometric recognition requires

the user to be present at the time of authentication, it can also prevent users from making

false refutation claims. Moreover, only biometrics can provide negative identification

functionality where the aim is to set up whether a certain individual is really enrolled in

a system even if the individual might refuse it. Due to these characteristics, biometric

recognition has been widely hailed as a natural and reliable method (Butkus, 2014).

Each biometric modality has its advantages and disadvantages. Vulnerability to

attacks raises genuine concerns with respect to the danger of identity fraud. Table 1.1

presents currently available biometric identity systems and their shortcomings:

Table 1.1 Problems with current commercial biometric security systems

Problems in Commercial Biometric Systems

Compan

y

Disney Alibaba Apple Handyman Hitachi

Product

Descript

ion

Biometric

measurements

are taken from

the fingers of

guests to ensure

Alibaba

creates

Face

recognition

Fingerprint

Scanner

watch

Biometric

Keyless Lock:

Unlock or lock

your entry door

with a quick

Barclays Bank

scans blood in

the veins in the

finger of the

customer. To

4

that a ticket is

used by the

same person

from day to day

in Disney Land

(Wikipedia).

Payment

system

(computer

world)

Used to make

payments

(apple)

scan of your

fingerprint

(finger

PrintDoorLocks

.com)

scan the device

uses light in the

near infrared

spectrum

(geektimes.ru).

Cheatin

g

method

Copy fingerprint

on silicon

Use face mask Copy

fingerprint on

silicon

Copy

fingerprint on

silicon

Cut finger and

use before it

drains blood

Liveness

detectio

n

NO NO NO NO YES for short

time

As stated in Table 1.1, there have been a lot of big enterprises developing or

commercializing biometric identification. However, each has their own short-comings.

For example, fingerprints can be collected on silicon surfaces and an iris scan can be

copied on contact lenses, whereas the face can be recreated on a mask and voice can be

copied through the use of the microphone (Fratini, Sansone, Bifulco, & Cesarelli, 2015).

In the last two decades or so, the electrocardiogram (ECG) has been proposed

as a new biometric modality for person identification (Sidek et al., 2010). ECG is a

medical biometrics like an electroencephalogram (EEG), a phonocardiogram (PPG),

which is traditionally used by doctors to diagnose diseases (Rafik Matta, 2011).

1.1.1 Biological Understanding of Electrocardiogram

The ECG stands for electrocardiogram. It is a Latin word; “electro” means electrical;

cardio is equal to heart in English; gram is recording. Therefore, the electrocardiogram

(ECG) means the electrical recording of the heart (Waechter, 2012). A human heart

5

consists of four compartments: two atriums on the top and two ventricular underneath

(as depicted in Figure 1.1). Hearts electrical activity starts at Peacemaker (Sinoatrial

node). When electric charges travel through two atriums, it generates P wave, when

AV node sends charges down to ventricular this generates QRS complex. When

ventricular repolarize it generates T wave. These 3 waves comprise one cardiac cycle

or one heartbeat. These electric charges would travel from the heart to skin. Then It can

then be obtained by electrical sensing materials from the surface of the body and can be

recorded on various media.

Figure 1.1 Elements of the cardiac conduction system (Diehl, 2011)

The electrocardiograph is a device used to access conditions of the heart (Figure

1.2). It has a number of connector cables which must be attached to the human body;

the gel is required for better connectivity.

6

(a) (b)

Figure 1.2 Conventional electrocardiographs and its usage

ECG is a periodic signal composed of successive heartbeats. It has three basic

elements: P wave, QRS complex and T wave as a result of Ventricular repolarization

(Zhang et al., 2016). The following Figure 1.3 shows the ECG signal with 3 heartbeats:

Figure 1.3 ECG signal of subject 16539 in NSRDB database

Wave morphology of single heartbeat is shown here in Figure 1.4, from right to

left: the red area is T wave, the light green area is QRS complex, and the light green

area is P wave:

Figure 1.4 P wave, QRS and T wave in a single heartbeat

The following table 1.2 shows the coordination of electrical activity of the heart

to its mechanical activity. The mechanical activity is what one can feel about heart

contraction, and expansion.

7

Table 1.2 Relations between electrical and mechanical activity of the heart

Electrical Activity Resulting Mechanical

Activity

Resulting ECG Wave

Formation

Atrial Depolarization Contraction of two

Atriums

Generation of P wave

Ventricular

Depolarization

Contraction of two

Ventricular

Generation of QRS

Ventricular

Repolarization

Relaxation of

Ventricular

Generation of T

By identifying the morphologies of the ECG signal, cardiovascular diseases can

be diagnosed. Furthermore, these morphologies differ from one person to another and

according to recent research, person identification is possible with morphological

pattern matching of the ECG signal (K. Sidek, Sufi, Khalil, & Al-Shammary, 2010).

The following ECG characteristics are reasons why it is suitable to be biometric:

1. Robustness to attacks. Any security system using the ECG signal to recognize

individuals needs no extra computation to assess the originality of the reading.

In addition, it is very difficult to steal or mimic someone else’s signal as it is the

combination of several sympathetic and parasympathetic factors of the human

body. (Rafik Matta, 2011).

2. ECG is a non-intrusive, non-invasive means of identification. Other forms of

biometric identification like face recognition and audio recognition rely on

camera and voice recorders arouse privacy issues among users. The ECG would

not reveal any privacy except heart diseases (FoteiniAgrafioti, 2011).

3. Uniqueness, since hearts structures such as chest geometry, position and size,

and wall thickness differ from person to person, the ECG signals generated by

each person is unique. Therefore, it is possible to discriminate a person from a

group of subjects (Ming Li, Shrikanth Narayanan, 2010).

8

4. Collectability, ECG biometric is very easy to be implemented on computer

keyboards or mice by attaching electrodes to two points across the human body,

these two points can be two fingers (Camara, Peris-Lopez, & Tapiador, 2015).

5. Universality, all human being possess it, it cannot be altered or lost. This is an

important fact against repudiation. Conventional identification requires people

to bring carried devices (Employee access card, Badge etc.). If it is forgotten,

that person will lose his identity (Hassan, Gilani, & Jamil, 2016).

ECG presents a natural shield to identity theft because it provides inherent aliveness

detection; means a person has to be alive in order to give ECG signal. Any security

system using the ECG signal to recognize individuals needs no extra computation to

proof that the signal is genuine. In addition, it is very difficult to steal or mimic someone

else’s signal as it is a combination of several sympathetic and parasympathetic factors

of the human body (Rafik Matta, 2011).

There are two ways to identify people using ECG, fiducial points based

identification and fiducial points independent identification.

1.1.2 The Fiducial Points Dependent Identification

The fiducial-based methods extract temporal, amplitude, area, angle, or dynamic (across

heartbeats) features from characteristic points on the ECG signal. The features include

but are not limited to the amplitudes of the P, R, and T waves, the temporal distance

between wave boundaries (onset and offset of the P, Q, R, S, and T waves), the area of

the waves, and slope information (Odinaka et al., 2012). An advantage of this method

is its accuracy. The disadvantage is their sensitivity to noise.

9

1.1.3 The Fiducial Points Independent Identification

The fiducial independent approaches do not use the fiducial points as a medium but

extract statistical and analytical features from the morphology of the whole signal

waveforms (Paolo Zicari, Abbes Amira, Georg Fisher, James Mclaughlin,, 2012) such

as wavelet coefficients and autocorrelation coefficients (Odinaka et al., 2012).

Non-fiducial approaches have the advantage of not relying critically on the

accurate extraction of fiducial data, which is a difficult task to do (David Coutinho,Ana

L. N. Fred, Mario A.T.Figueiredo, 2010). For instance, finding discrete wavelet

coefficients do need only R peak locations and for some other approaches, they even do

not need any fiducial detection. A downside to non-fiducial features is that it usually

comes as a big array of data (hundreds of thousands of coefficients), which in turn

increases the computational overhead, the memory usage and the need for more training

data. Furthermore, the classifier may be weakened by the dispensable information that

is usually revealed in high-dimension data. The number of derived coefficients is related

to the input dimension (M. M. Tantawi, Revett, Salem, & Tolba, 2013).

This thesis falls into the category of fiducial points based identification as non-

fiducial method is computationally expensive. Therefore measurements in PQRST

morphology are an inevitable step. The features include but are not limited to the

amplitudes of the P, R, and T waves, the temporal distance between wave boundaries

(onset and offset of the P, Q, R, S, and T waves), the area of the waves, and slope

information (Odinaka et al., 2012). The following graph figure 1.5 shows 28 features

can be extracted from one heartbeat. This figure will be used frequently throughout the

thesis.