effective ecg fingertip sensor based by tuerxunwaili
TRANSCRIPT
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.