UNDERGRADUATION THESIS Topic PERSONAL AUTHENTICATION BY SINGLE-CHANNEL ECG SIGNALS

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HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

SCHOOL OF ELECTRONICS AND TELECOMMUNICATIONS

UNDERGRADUATION THESIS

Topic:

PERSONAL AUTHENTICATION BY

SINGLE-CHANNEL ECG SIGNALS

Student: VU THI MINH

Biomedical Engineering Class Advanced Program – Course 58 Supervisor: Dr NGUYEN VIET DUNG Argue Officer:

Hanoi, 1-2019

Thesis Assessment

(For Supervisor)

Supervisor: Nguyen Viet Dung

Subject: Personal authentication by single- channel ECG signals

Choose the appropriate scores for students following the criteria below: Very poor (1); Poor (2); Average (3); Good (4); Excellent (5)

The combination of theory and practice (20)

1

Clearly state the urgency and importance of the subject, the problems

and assumptions (include purposes and relevance) as well as the

scope of application of the thesis

1 2 3 4 5

2 Update the latest research results (national/international) 1 2 3 4 5

3 Clearly state the study/problem solving methodology in detail 1 2 3 4 5

4 Have stimulation/experimental results and describe obtained results

Ability to analyze and evaluate the results (15)

5 Clear working plan, including objectives and methodology based on

systemically theoretical study results 1 2 3 4 5

6 Results are presented logically and easy to understand; all results

are satisfactorily analyzed and assessed 1 2 3 4 5

7

In conclusion section, author specifies the differences (if any)

between the results obtained and the initial objectives while providing

arguments to propose possible solutions in the future

1 2 3 4 5

Writing skill (10)

8

The thesis is presented on a prescribed format with logical and nice

structure of chapters (Tables, F

igures are clear with captions, are numbered and explained or

mentioned in the thesis; has alignments, has spaces after full stops

and commas, etc.), has chapter introductions and conclusions, listed

references and citations following regulations

1 2 3 4 5

9 Excellent writing skill (right syntax, scientific style, logical

reasoning, appropriate vocabularies, etc.) 1 2 3 4 5

Science research achievements (5) (choose 1 in 3 options)

10a

Had the published or accepted scientific articles/3rd prize at School

level at student science research conference or higher/3rd scientific

prize (international/national) or higher/registered patents

5

10b

Reported at School-level board in student science research

conference but not achieved 3rd prize or higher/Achieved

consolation prize at other nationally or internationally specialized

competitions such as TI contest

3 Other comments of instructor (Instructor comments on student’s work

attitude and spirit)

Date: …./… /2019

Supervisor (Signature & full name)

Thesis Assessment

(For Argue Officer)

Argue Officer: ………

Subject: Personal authentication by single- channel ECG signals

Choose the appropriate scores for students following the criteria below: Very poor (1); Poor (2); Average (3); Good (4); Excellent (5)

The combination of theory and practice (20)

1

Clearly state the urgency and importance of the subject, the problems

and assumptions (include purposes and relevance) as well as the

scope of application of the thesis

1 2 3 4 5

2 Update the latest research results (national/international) 1 2 3 4 5

3 Clearly state the study/problem solving methodology in detail 1 2 3 4 5

4 Have stimulation/experimental results and describe obtained results

Ability to analyze and evaluate the results (15)

5 Clear working plan, including objectives and methodology based on

systemically theoretical study results 1 2 3 4 5

6 Results are presented logically and easy to understand; all results

are satisfactorily analyzed and assessed 1 2 3 4 5

7

In conclusion section, author specifies the differences (if any)

between the results obtained and the initial objectives while providing

arguments to propose possible solutions in the future

1 2 3 4 5

Writing skill (10)

8

The thesis is presented on a prescribed format with logical and nice

structure of chapters (Tables, Figures are clear with captions, are

numbered and explained or mentioned in the thesis; has alignments,

has spaces after full stops and commas, etc.), has chapter

introductions and conclusions, listed references and citations

following regulations

1 2 3 4 5

9 Excellent writing skill (right syntax, scientific style, logical

reasoning, appropriate vocabularies, etc.) 1 2 3 4 5

Science research achievements (5) (choose 1 in 3 options)

10a

Had the published or accepted scientific articles/3rd prize at School

level at student science research conference or higher/3rd scientific

prize (international/national) or higher/registered patents

5

10b

Reported at School-level board in student science research

conference but not achieved 3rd prize or higher/Achieved

consolation prize at other nationally or internationally specialized

competitions such as TI contest

3 Other comments of argue officer (Argue officer comments on student’s

work attitude and spirit)

Date: …./… /2019 Argue officer (Signature & full name)

ACKNOWLEDGMENTS

Biometrics refers to the recognition of individuals based on physiological or behavioral characteristics Biometric traits include the retina, face, iris, fingerprints, and voice Through these information sources, various methods can recognize individuals However, some limitations of these technologies lead to require higher security The electrocardiogram (ECG) is one of the most commonly known biological signals ECG involves information about the structural and functional cardiac muscle activities, and it is a simple and effective representative of a noninvasive diagnostic method Every individual has characteristic ECG features Therefore, such signals provide strong protection against forgery Recently, more research has focused on extracting ECG features with different method in available database have high accuracies In the thesis, a goal is to extract more suitable features with signals, which are acquired from Kardia Mobile device, thus, identifying individuals

I wish to express my sincere thanks to my instructor Dr Nguyen Viet Dung for his devoted guidance and supervision This practice would not have been completed without his care and dedication in constructively criticizing my work I also wish to express my sincere thanks to the lecturers in School of Electronics and Telecommunications as well as in Hanoi University of Science and Technology who have taught me countless useful knowledge Finally, I special thanks goes to my parents and other family members, who have unconditionally given me all of their support on all fronts They inspired and gave me strengths to finish this work

Hanoi, January 2th 2019

Student

Vu Thi Minh

ABSTRACT

Biosignals contain useful information that can be used to identify individuals beside applications in medical Biological signals can be classified according to various characteristics of the signal, including the waveform shape, statistical structure, and temporal properties Biosignals can prevent to falsify from physical features in biometrics such as face, fingerprint, iris, etc An Electrocardiogram (ECG) measures and records the electrical activity that passes through the heart In this study,

I researched single- channel electrocardiogram (ECG) signal which is got from a device named Kardia mobile designed by AliverCor company and has medical standards certification from FDA A feature set extracted based on the association between Discrete Wavelet Transform (DWT) and Statistic data was propounded and Support Vector Machine (SVM) was exerted for ECG classification Results show that my method achieves approximately 87.5% for data that I collected However, the amount of data used for training is limited

ABSTRACT

Tín hiệu sinh học chứa những thông tin hữu ích có thể được sử dụng để xác định các cá nhân bên cạnh các ứng dụng trong y tế Tín hiệu sinh học có thể được phân loại theo các đặc tính khác nhau của tín hiệu, bao gồm dạng sóng, cấu trúc thống kê

và tính chất thời gian Tín hiệu sinh học có thể ngăn ngừa việc làm giả các đặc điểm vật lý trong sinh trắc học như khuôn mặt, dấu vân tay, mống mắt, v.v Điện tâm đồ (ECG) đo và ghi lại hoạt động điện đi qua tim Trong nghiên cứu này, tôi đã nghiên cứu tín hiệu điện tim đơn kênh (ECG) được lấy từ một thiết bị có tên Kardia mobile

do công ty AliverCor thiết kế và có chứng nhận tiêu chuẩn y tế từ FDA Một bộ tính năng được trích xuất dựa trên sự liên kết giữa biến đổi Wavelet rời rạc (DWT) và dữ liệu thống kê đã được đưa ra và máy vector hỗ trợ (SVM) đã được sử dụng để phân loại ECG Kết quả cho thấy phương pháp của tôi đạt được xấp xỉ 87.5% đối với dữ liệu mà tôi đã thu thập Tuy nhiên lượng dữ liệu được sử dụng bị hạn chế

CONTENTS

ACKNOWLEDGMENTS 6

ABSTRACT 7

LIST OF FIGURES 11

LIST OF ACRONYMS 14

CHAPTER 1: FUNDAMENTAL BACKGROUND INFORMATION 16

1.1 Biometrics authentication 16

1.1.1 Types of biometrics 16

1.1.2 Basic components of biometric system 25

1.1.3 Some criteria of biometrics 25

1.2 Electrocardiography (ECG/ EKG) 26

1.2.1 Definition 26

1.2.2 ECG waveform 27

1.2.3 Different noise in ECG signals 31

1.3 Wavelet Transform (WT) and Discrete Wavelet Transform (DWT) 33

1.3.1 Fundamental Concepts and Overview of Wavelet Transform 33

1.3.2 Multi-resolution Analysis and Continuous Wavelet Transform 37

1.3.3 Multi-resolution Analysis: Discrete Wavelet Transform 43

1.3.4 Wavelet families 50

1.4 Statistics data 58

1.5 Machine learning (ML) 59

1.5.1 Types of machine learning 59

1.5.2 Support Vector Machine (SVM) 61

1.5.3 K- nearest neighbor (KNN) 69

CHAPTER 2 SIGNAL PREPARATION 74

2.1 ECG Acquisition 74

2.1.1 ECG Recording equipment: Kardia mobile 742.1.2 Web plot digitizer 762.2 Experiment set up 78CHAPTER 3: DESIGNING ECG BASED PERSONAL AUTHENTICATION SYSTEM 813.1 Block diagram 81 3.1.1 Pre-processing 823.1.2 Feature extraction Algorithm from DWT using Daubechies wavelet 833.1.3 Classification 863.2 Results and Discussion 87CONCLUSION 91

LIST OF FIGURES

Figure 1.1 Types of biometrics 17

Figure 1.2 Retina scanning 17

Figure 1.3 Iris sample 19

Figure 1.4 Fingertip 20

Figure 1.5.Fingerprint matching mechanism 20

Figure 1.6 Automatic face recognition system 21

Figure 1.7 Sample voice clip as shown in sound editor 22

Figure 1.8 Hand/Palm-print and hand/palm-print features 24

Figure 1.9 Flow chat of a biometrics 25

Figure 1.10 Schematic anatomy of the human heart 27

Figure 1.11 Typical cardiac waveform 27

Figure 1.12 Normal sinus rhythm [21] 29

Figure 1 13 Sinus Bradycardia [21] 29

Figure 1 14 Sinus Tachycardia [21] 30

Figure 1.15 Schematic representation of various QRS complex configurations and normal ranges of wave amplitudes and durations [23] 30

Figure 1.16 The classical ECG curve with its most common waveform 31

Figure 1.17 60 Hz Power Line Interference[25] 32

Figure 1.18 ECG signal which contains EMG noise [26] 32

Figure 1.19 Sine wave with frequencies at 3Hz, 10 Hz and 50 Hz [29] 34

Figure 1.20 The FT of 50Hz signal [29] 35

Figure 1.21 Example of a signal has high frequency components for short 38

Figure 1.22 Cosine signals corresponding to various scale [29] 41

Figure 1.23 Illustration of the entire process CWT [29] 43

Figure 1.24 The subband coding Algorithm 43

Figure 1.25 The scale function and mother wavelet function of Haar wavelet 51

Figure 1.26 Meyer Scaling function 52

Figure 1.27 Fourier transform of the scaling function of the Meyer wavelet 52

Figure 1.28 Fourier transform of Meyer wavelet 53

Figure 1.29 Meyer wavelet 54

Figure 1.30 Real part of the Morlet wavelet for ωo = 5 55

Figure 1.31 Imaginary part of the Morlet wavelet for ωo = 5 58

Figure 1.32 The Daubechies scaling function for N = 4 57

Figure 1.33 The Daubechies wavelet for N = 4 58

Figure 1.34 ML techniques 60

Figure 1.35 Types of algorithm 61

Figure 1.36 Example of the training data 62

Figure 1.37 Example of the separating hyperplane 63

Figure 1.38 Several separating hyperplanes 63

Figure 1.39 Margin 64

Figure 1.39 Analysis of the SVM problem 65

Figure 1.40 The point’s closest to separable plane of the two classes is circled 67

Figure 1.41 Soft SVM margin a) Noise data, b) Near linearly separable data 68

Figure 1.42 Introduce slack variables ξn. 68

Figure 1.43 Map of 1NN 70

Figure 2.1 The Kardia mobile 75

Figure 2.2 Kardia software 76

Figure 2.3 Obtained ECG from Kadia 77

Figure 2.4 WebPlot digitizer 78

Figure 2.5 Putting fingers on device 80

Figure 3.1 Raw signal after digitizer 81

Figure 3.2 Histogram of origin signal (above) and synthesis signal (below) 82

Figure 3.3 Daubechies 4 wavelet 84

Figure 3.4 Daubechies 6 wavelet 84

Figure3.5.Frequency components of each decomposition level and the corrresponding frequency bands 85

Figure 3.6 Signal and power spectrum before and after filtering 87

Figure 3.7 Reconstructed signal level 1, 2, 3, 4 88

LIST OF ACRONYMS

CO Correlation distance metric

CHAPTER 1: FUNDAMENTAL BACKGROUND

INFORMATION

Biometrics is a technology used to identify, analyze, and measure an individual’s

physical and behavioral characteristics A biometric system is a technology, which takes an individual physiological, behavioral, analyzes it, and identifies the individual

as a genuine or malicious user Biometric identification consists of determining the identity of a person The aim is to capture an item of biometric data from this person

It can be a photo of their face, a record of their voice, or an image of their fingerprint This data is then compared to the biometric data of several other persons kept in a database In this mode, the question is a simple one: "Who are you?" Biometric authentication is the process of comparing data for the person's characteristics to that person's biometric "template" in order to determine resemblance The reference model is first store in a database or a secure portable element like a smart card The data stored is then compared to the person's biometric data to be authenticated Here

it is the person's identity which is being verified In this mode, the question being asked is: "Are you indeed Mr or Mrs X?"[1]

Personal biometric authentication: In this problem, I just let one person enter the

system, so I just need to identify the person who are you or not

Overall biometric authentication: The problem will allow many people who had

database in the system to enter it

In my project, I solve the Personal biometric authentication problem I indicate the signal that is of A, or not A person

1.1.1 Types of biometrics

Biometric devices are many types, but majorly there are five types of biometrics security which are commonly used Biometrics is basically the recognition of human being personality that are unique to each human, which includes facial recognition, fingerprints, voice recognition, retina scans, palm prints, and more has shown in Figure 1.1 Biometric technology are used to keep the devices safe in the best way to

ensure that people stay out of their valuable assets and information, and will find that using any one of these five biometrics security, devices is a great way to keep things safe.[2]

Figure 1.1 Types of biometrics

o Retina scanner

Retina scanning is a biometric verification technology that uses an image of an individual’s retinal blood vessel pattern as a unique identifying trait for access to secure installations The human retina is a thin tissue composed of neural cells that is located within the posterior part of the eye as shown in Figure 1.2

Figure 1.2 Retina scanning

Due to the complex shape of the capillaries that deliver the retina with blood, all and sundry's retina is unique The network of blood vessels within the retina is so complicated that even identical twins do not proportion a comparable sample Even though retinal styles can be altered in instance of diabetes, Glaucoma or retinal degenerative disorders, the retina typically remains unaffected from birth till dying Due to its unique and unchanging nature, the retina seems to be the maximum precise and dependable biometric [3]

o Iris Scanning

Iris recognition uses digital camera technology, with slight infrared illumination lowering specular reflection from the convex cornea, to create photographs of the detail-wealthy, elaborate systems of the iris as shown in Figure 1.3 Converted into digital templates, those snap shots offer mathematical representations of the iris that yield unambiguous wonderful identity of an individual Iris reputation efficiency is not often impeded by using glasses or contact lenses Iris technology has the smallest outlier (folks that cannot use/enroll) group of all biometric technologies [4] Moreover, it has a small template size that allows speedy comparisons making iris recognition technology particularly well suited for one-to-many identifications Even genetically identical individuals have distinct iris textures which further confirm that

it is a highly accurate and reliable technique Because of its pace of contrast, iris reputation is the handiest biometric technology nicely perfect for one-to-many identity Advantage of iris reputation is its balance, or template sturdiness, a single enrollment can closing an entire life There are few benefits of the use of iris as biometric identification: it's far an inner organ this is properly included against damage and wear by a rather obvious and touchy membrane (the cornea) [5]

A new technology requires substantial investment and hence may not be suitable for small organizations It is quite difficult to perform iris recognition from a distance larger than a few meters and the subject to be identified needs to be co-operative The subject should hold his or her head still and look into the camera Iris recognition is also susceptible to poor quality of images as well as associated failure to enroll rates

When it comes to biometrics, the iris has several major advantages compared to a fingerprint:

- You do not spread the information around every time you touch something

- The iris stays virtually unchanged throughout a person’s life A fingerprint, on the other hand, can be dirtied, scarred or eroded

- You cannot use a fingerprint with dirty or sweaty hands Irises, however, have

no such problem.[6]

However, both retina scanners and iris scanners have proven to be easy to trick simply by using a high-quality photograph of the subject’s eyes or face

Figure 1.3 Iris sample

o Finger print scanner

Fingerprints are the graphical glide-like ridges gift on human palms Finger ridge configurations do no longer exchange for the duration of the life of a person besides due to accidents including bruises and cuts on the fingertips This belongings makes fingerprints a totally attractive biometric identifier Fingerprint-based (Figure 1.4) totally private identification has been used for a very long time As a long way as fee

is going, the fingerprint scanning is on the lower stop of the dimensions The most inexpensive fingerprint scanners are those that best scan the actual print, though the dearer ones really experiment the presence of blood in the fingerprint, the scale and shape of the thumb, and plenty of different features as shown in Figure 1.5 Those

costlier structures in reality capture a 3D photo of the fingerprint, thereby making it

a great deal more difficult for the fingerprint to be counterfeited [7]

Figure 1.4 Fingertip

Figure 1.5.Fingerprint matching mechanism

Tests conducted by the International Biometric Group on fingerprint systems of participating vendors found that the false acceptance rate of these systems ranged from 0% to 5% On the same day of enrolment, tests were conducted for false rejection rate and it ranged from 0% to 35% However, when the tests were conducted six weeks later the FRR’s ranged from 0% to 66% Systems from some vendors worked very well while others had some accuracy problems When vendor-independent tests were conducted by the FVG2006, it found that FAR was held constant at a rate of 0.01% This rate is considered sufficient for most authentication scenarios

o Facial biometrics

Each individual has a distinctly unique face, even two twins that cannot differentiate by human’s eye There are sure markers that enable these biometric acknowledgment scanners to in a split second recognize the uniqueness of every individual examining their facial elements Face Recognition System (Figure 1.6) measures and matches the unique characteristics for the purposes of identification or authentication Often leveraging a digital or connected camera, facial recognition software can detect faces in images, quantify their features, and then match them against stored templates in a database

Figure 1.6 Automatic face recognition system

Several factors can affect the accuracy of facial biometrics It might not work well under poor lighting conditions, the presence of sunglasses or other objects that partially cover the subject’s face and low resolution images Also the face of a person changes over time The accuracy of some facial recognition systems is also affected

by the variation in facial expressions and for this reason most countries allow only neutral facial expressions in passport photos

A real-world facial biometrics accuracy test verified passport photos of passengers against a lesser quality live scan photo taken within the border control gates itself The top performing vendor in the National Institute of Standards Technology (NIST) test achieved a FRR of 1.1% Thus, face recognition systems

definitely provide better accuracy when compared to live guards performing a manual comparison of passport photos with the passport holders

o Voice recognition

Voice or speaker recognition is the ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands Voice recognition has gained prominence and use with the rise of AI and intelligent assistants, such as Amazon's Alexa, Apple's Siri and Microsoft's Cortana Each person in the world has a unique voice pattern as shown in Figure 1.7, even though the changes are slight and hardly noticeable to the human ear On the other hand with uncommon voice recognition programming, those moment contrasts in every individual's voice can be noted, experienced and validated to enable the access to the individual that has quality pitch which is a correct one, and at the same time voice level also

Figure 1.7 Sample voice clip as shown in sound editor

Voice recognition systems enable consumers to interact with technology simply

by speaking to it, enabling hands-free requests, reminders and other simple tasks Surprisingly it can be effective at differentiating two people who have almost identical voice patterns Voice recognition software on computers requires that analog audio be converted into digital signals, known as analog-to-digital conversion For a computer to decipher a signal, it must have a digital database, or vocabulary, of words or syllables, as well as a speedy means for comparing this data to signals The

speech patterns are stored on the hard drive and loaded into memory when the program is run A comparator checks these stored patterns against the output of the A/D converter an action called pattern recognition

Background noise can produce false input, which can be avoided by using the system in a quiet room There is also a problem with words that sound alike, but that are spelled differently and have different meanings for example, hear and here Feeding the wrong voice cannot always be avoided in voice recognition as well as the voice capturing machine should be near to the user

o Palm vein pattern recognition

Palm vein is one of the most secure biometrics and is the world’s first contactless personal identification system It works by capturing the vein pattern image of an individual while radiating it with near-infrared rays Its speciality is that it can detect the vein pattern on the human palm with utmost precision When the sensor emits a near-infrared ray towards the palm of the hand, the blood flowing through these back

to the heart with reduced oxygen absorbs the radiation and causes the veins to appear

as a black pattern This pattern is then recorded and stored in an encrypted format in

a database, token or smart card as a reference for future comparison

By placing your hand on a scanner, you not only have a unique fingerprint pattern, but the size and shape of your entire hand is also very unique as shown in Figure 1.8

It differs to a unique finger impression in that it likewise contains other data, for example, touch, indents and symbols which can be utilized when contrasting one palm with another Hand prints can be used for criminal, forensic or commercial applications [8] The main difficulty of hand print is that the print changes with time depending on the type of work the person is doing for an extended duration of time

Figure 1.8 Hand/Palm-print and hand/palm-print features

It uses the information that is contained within a person’s body to confirm his or

her identity Therefore it is highly accurate because the vein pattern of the human

palm is not only complex but it is also unique to each individual It uses a combination

of image recognition and optical technology to scan the normally invisible vein

pattern of the human palm This makes it highly resistant to any spoofing methods

such as impersonation, counterfeiting or any other dishonest actions Moreover, it is

designed in such a way that it can detect only the vein pattern of living persons It

also has a fast scanning process and does not need any contact which means that this

technology meets the strict hygiene requirements that are usually required for usage

in public environments [9]

o Live-biosignal authentication

Besides the popular authentication methods listed above, bio-signal also

researched to recognize individuals because of their features such as Electromyogram

(EMG) and Electroencephalogram (EEG)

Electromyography (EMG) is known as process of recording the electric activities

of muscles This signal is Electromyogram Electromyogram is an activity potential

signal generated when muscle contracts, which is controlled by the nervous system

and produced during muscle contraction The signal represents the anatomical and

physiological properties of muscles [10]

The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application [11]

Based on different individual features, they can distinguish with other individuals However, both EMG and ECG signals are just only used in medical, not widely popular

1.1.2 Basic components of biometric system

There are four general steps in a biometric system taking to perform identification and verification as shown in Figure 1.9

• Acquire live sample from candidate (Use sensors)

• Extract prominent features from sample (Use processing unit)

• Compare live sample with samples stored in database (Use algorithms)

• Present the decision (Accept or reject the candidate)

Figure 1.9 Flow chat of a biometrics

1.1.3 Some criteria of biometrics

False Acceptance Rate (FAR) – It is the measure of possibility that a biometric

system will incorrectly identify an unauthorized user as a valid user [12]

Number of Identification Attemps

A biometric system providing low FAR ensures high security

(1.1)

False Reject Rate (FRR) – It is the measure of possibility that the biometric system

will incorrectly reject an authorized user as an invalid user [12]

Number of Identification Attemps

If FAR is equals to FRR, the obtained results are the best

1.2 Electrocardiography (ECG/ EKG)

The electrocardiogram (ECG), resulting from the electrical conduction through the heart needed for its contraction, is one of the most recent traits to be explored for biometric purposes [13, 14] Despite being far from as developed or widespread as face or fingerprint biometrics, the ECG offers unique advantages in terms of universality, uniqueness, permanence, and liveness assurance, that attest its potential for the recognition of individuals [15,16]

1.2.1 Definition

An EKG, also called an ECG or electrocardiogram, is a recording of the heart's electrical activity It is a quick and painless procedure EKGs captures a tracing of cardiac electrical impulse as it moves from the atrium to the ventricles These electrical impulses cause the heart to contract and pump blood [17]

The leads are placed on specific locations of the body of the person to record ECG either on graph paper or on monitors The human heart contains four chambers i.e., Right Atrium, Left Atrium, Right Ventricle and Left Ventricle The upper chambers are the two Atria’s and the lower chambers are the two Ventricles Under healthy condition, the heartbeat begins at the Right Atrium called Sino Atria (SA) node and

a special group of cells send these electrical signals across the heart This signal travels from the Atria to the Atrio Ventricular (AV) node The AV node connects to

a group of fibers in Ventricles that conducts the electrical signal and transmits the impulse to all parts of the lower chamber, the Ventricles To ensure that the heart is functioning properly this path of propagation must be traced accurately [18].The basic structure of heart is depicted in Figure 1.10

(1.2)

Figure 1.10 Schematic anatomy of the human heart

To determine the potential use of ECG as a biometric, it is necessary to evaluate how ECG satisfies the requirements for biometric characteristics

A "perfect" biometric characteristic should be:

o Universal, i.e., each individual possesses this characteristic

o Easily measured, i.e., it is quite easy technically and convenient for an individual to obtain the characteristic

o Unique, i.e., there are no two individuals with identical characteristics

o Permanent, i.e., the characteristic does not change over time

"Good" biometric characteristics can to a greater or lesser extent satisfy these requirements, depending on the purpose and application of biometric system

1.2.2 ECG waveform

Each heart beat displayed is a sequence of electrical waves characterized by peaks and valleys ECG mainly provides two kinds of information One is the duration of the electrical wave passing through the heart and it will decide whether the electrical activity is normal ,or slow, or irregular Second is the amount of electrical activity passing through the heart muscle that helps to find whether the parts

of the heart are too large or overworked The frequency range of an ECG signal is 0.05– 100 Hz and its dynamic range is 1–10 mV The ECG signal is characterized

by five peaks and valleys represented by the letters P, Q, R, S, T Sometimes U wave

is also present The performance of ECG analysis is based on the accurate and reliable detection of the QRS complex as well as T- and P waves [19] [20] An ideal ECG wave is as shown in Figure 1.11:

Figure 1.11 Typical cardiac waveform

o P waves are the wave of atrial depolarization ( amplitude A< 0.25 mV, time

<0.1s)

o T waves are atrial repolarization that is not seen on the ECG because

ventricular depolarization is stronger

o QRS waves are ventricular depolarization state (total time: 0.08s)

- Q wave A 0.3 mV, time 0.03s (begin ventricular depolarization

process)

- R-wave A <= 2mV (depolarization simultaneously both ventricles)

- S wave: bottom polar ventricular depolarization (as Q wave)

o ST, T and U : repolarization of the ventricles ( amplitude < 0.5mV,

time =0.2s)

The P-wave represent the activation of the two atria, the upper chambers of the heart, while the QRS complex and T-wave represent the excitation of the lower chamber of the heart, the ventricles QRS detection is one of the fundamental issues

in automatic ECG signal analysis After QRS complex has been detected a thorough examination of ECG signal is done The P, QRS and T-waves reflect the rhythmic electrical depolarization and repolarization of the myocardium linked with the contractions of the atria and ventricles [21] The horizontal section of this waveform prior to the P-wave is termed as the baseline or the isopotential line The P-wave corresponds to the depolarization of the atrial musculature The QRS complex gives the combined result of the repolarization of the atria and depolarization

of the ventricles, which occurs almost at same time The T-wave is the wave of ventricular repolarization, whereas the U-wave, if present is normally believed to

be the result of after potentials in the ventricular muscle So the duration amplitude and morphology of the QRS complex is helpful in diagnosing cardiac

arrhythmias, conduction abnormalities, ventricular hypertrophy, myocardial infection and other disease states The usual rate of heart is 60 to 100 beats per minute A slower rate than the normal range is called bradycardia (slow heart) and a higher rate is called tachycardia (fast heart) If the ECG signal is not normal then an Arrhythmia is indicated [21, 22] The waveform of normal ECG waveform and that of the abnormalities is shown in Figures 1.12, 1.13 and 1.14:

Figure 1.12 Normal sinus rhythm [21]

Figure 1.13 Sinus Bradycardia [21]

Figure 1.14 Sinus Tachycardia [21]

It is plausible to assume that an ECG is an almost unique human characteristic because morphology and amplitudes of recorded cardiac complexes are governed by multiple individual factors, in particular by the shape and position of the heart, and the presence and nature of pathologies, among other factors As a result, QRS complexes have a variety of configurations and metrics (Figure 1.15 and Figure 1.16)

Figure 1.15 Schematic representation of various QRS complex configurations and normal ranges

of wave amplitudes and durations [23]

Figure 1.16 The classical ECG curve with its most common waveform

1.2.3 Different noise in ECG signals

Different noises are affected by the ECG signal during its acquisition and transmission Mainly two types of noises are present in the ECG signal Noises with high frequency include Electromyogram noise, Additive white Gaussian noise, and power line interference Noises with low frequency include baseline wandering The noises contaminated in the ECG signal may lead wrong interpretation There are different denoising techniques are available in the literature [24]

o Power line interference

It consist of 50/60Hz pickup and harmonics The interference is mainly caused by Electromagnetic interference by power line, Electromagnetic field (EMF) by the nearby machines, Stray effect of the alternating current fields due to loops in the cables, Improper grounding of patient or the ECG machine ,The electrical equipment induce 50/60 Hz signals in the input circuits of the ECG machine Example, air conditioner, elevators and X-ray draw heavy power, line current [25]

Figure 1.17 60 Hz Power Line Interference[25]

o Electromyogram (EMG) Noise

The Electromyogram (EMG) noise is generated from electrical activity of the muscle EMG consist of maximum frequency of 10 KHz Sections of ECG may be interfered and corrupted by surface EMG which causes difficulties in data processing and analysis[26]

Figure 1.18 ECG signal which contains EMG noise [26]

o Baseline Wander

Baseline wander is a low-frequency noise component present in the ECG signal This is mainly due to respiration, and body movement The frequency content of baseline wander is usually in the range below 0.5 Hz; however, increased movement

of the body during the latter stages of a stress test further increases the frequency content of baseline wander [27] This low frequency noise, Baseline wander causes problem in detection and analysis of peak

o Channel Noise

Channel noise introduces when ECG signal is transmitted through channels This

is due to the Poor channel conditions It is mainly like white Gaussian noise, which contains all frequency components [25] E.g AWGN

o Electrode Contact Noise

Electrode contact noise is caused by the loss of contact between the electrode and the skin, which effectively disconnects the measurement system from the subject The noise is of duration 1s [28]

o Motion artifacts

Motion artifacts are transient base line changes caused by changes in the skin impedance with electrode motion As this impedance changes, the ECG amplifier sees a different source impedance which forms a voltage divider with the amplifier input impedance therefore the amplifier input voltage depends upon the source impedance which changes as the electrode position changes [28]

electrode-1.3 Wavelet Transform (WT) and Discrete Wavelet Transform (DWT)

1.3.1 Fundamental Concepts and Overview of Wavelet Transform

Mathematical transformations are applied to signals to obtain a further information from that signal that is not readily available in the raw signal There are number of transformations that can be applied, among which the Fourier transforms are probably

by far the most popular Most of the signals in practice, are time-domain signals in their raw format That is, whatever that signal is measuring, is a function of time In other words, when we plot the signal one of the axes is time (independent variable), and the other (dependent variable) is usually the amplitude When we plot time-domain signals, we obtain a time-amplitude representation of the signal This representation is not always the best representation of the signal for most signal processing related applications In many cases, the most distinguished information is hidden in the frequency content of the signal The frequency spectrum of a signal is basically the frequency components (spectral components) of that signal The frequency spectrum of a signal shows what frequencies exist in the signal [29]

The frequency is measured in cycles/second, or with a more common name, in

"Hertz" For example, the electric power we use in our daily life in the US is 60 Hz (50 Hz elsewhere in the world) This means that if you try to plot the electric current,

it will be a sine wave passing through the same point 50 times in 1 second Now, look

at the Figures below The first one is a sine wave at 3 Hz, the second one at 10 Hz, and the third one at 50 Hz

Figure 1.19 Sine wave with frequencies at 3Hz, 10 Hz and 50 Hz [29]

In order to measure frequency, or find the frequency content of a signal, we used Fourier Transform (FT) If the FT of a signal in time domain is taken, the frequency-amplitude representation of that signal is obtained In other words, we now have a plot with one axis being the frequency and the other being the amplitude This plot tells us how much of each frequency exists in our signal The frequency axis starts from zero, and goes up to infinity For every frequency, we have an amplitude

value For example, if we take the FT of the electric current that we use in our houses,

we will have one spike at 50 Hz, and nothing elsewhere, since that signal has only 50

Hz frequency component No other signal, however, has a FT which is this simple For most practical purposes, signals contain more than one frequency component The Figure 1.20 shows the FT of the 50 Hz signal:

Figure 1.20 The FT of 50Hz signal [29]

Often times, the information that cannot be readily seen in the time-domain can be seen in the frequency domain Let us look at an ECG signal, which is an example of biological signals The typical shape of a healthy ECG signal is well known to cardiologists Any significant deviation from that shape is usually considered to be a symptom of a pathological condition This pathological condition, however, may not always be quite obvious in the original time-domain signal Cardiologists usually use the time-domain ECG signals, which are recorded on strip-charts to analyze ECG signals Recently, the new computerized ECG recorders/analyzers also utilize the

frequency information to decide whether a pathological condition exists A pathological condition can sometimes be diagnosed more easily when the frequency content of the signal is analyzed This, of course, is only one simple example why frequency content might be useful Today Fourier transforms are used in many different areas including all branches of engineering

Although FT is probably the most popular transform being used (especially in electrical engineering), it is not the only one There are many other transforms that are used quite often by engineers and mathematicians Hilbert transform, short-time Fourier transform, Wigner distributions, the Radon Transform, and of course our featured transformation , the wavelet transform, constitute only a small portion of a huge list of transforms that are available at engineer's and mathematician's disposal Every transformation technique has its own area of application, with advantages and disadvantages, and the wavelet transform (WT) is no exception

FT (as well as WT) is a reversible transform, that is, it allows to go back and forwarding between the raw and processed (transformed) signals However, only either of them is available at any given time That is, no frequency information is available in the time-domain signal, and no time information is available in the Fourier transformed signal The natural question that comes to mind is that is it necessary to have both the time and the frequency information at the same time As

we will see soon, the answer depends on the particular application and the nature of the signal in hand Recall that the FT gives the frequency information of the signal, which means that it tells us how much of each frequency exists in the signal, but it does not tell us when in time these frequency components exist This information is not required when the signal is so-called stationary

The FT gives the spectral content of the signal, but it gives no information regarding where in time those spectral components appear Therefore, FT is not a suitable technique for non-stationary signal However, if we are only interested in what spectral components exist in the signal, but not interested where these occur, FT can be used for non-stationary signals In this case, if this information is needed, i.e.,

if we want to know, what spectral component occur at what time (interval) , then

Fourier transform is not the right transform to use For practical purposes, it is difficult to make the separation, since there are many practical stationary signals, as well as non-stationary ones Almost all biological signals, for example, are non-stationary Some of the most famous ones are ECG (electrical activity of the heart, electrocardiograph), EEG (electrical activity of the brain, electroencephalograph), and EMG (electrical activity of the muscles, electromyogram) Summarily, the FT gives what frequency components (spectral components) exist in the signal When the time localization of the spectral components is needed, a transform giving the Time-frequency representation of the signal is needed

The Wavelet transform is a transform of this type It provides the time-frequency representation (There are other transforms, which give this information too, such as short time Fourier transform, Wigner distributions, etc Often times a particular spectral component, occurring at any instant can be of particular interest In these cases, it may be very beneficial to know the time intervals these particular spectra components occur For example, in EEGs, the latency of an event-related potential is

of particular interest Event-related potential is the response of the brain to a specific stimulus like flash-light, the latency of this response is the amount of time elapsed between the onset of the stimulus and the response Wavelet transform is capable of providing the time and frequency information simultaneously, hence giving a time-frequency representation of the signal The WT was developed as an alternative to the STFT The STFT will be explained in great detail in the second part of this tutorial It suffices at this time to say that the WT was developed to overcome some resolution related problems of the STFT To make a real long story short, we pass the time-domain signal from various high pass and low pass filters, which filters out either high frequency or low frequency portions of the signal This procedure is repeated, every time some portion of the signal corresponding to some frequencies being removed from the signal

1.3.2 Multi-resolution Analysis and Continuous Wavelet Transform

Although the time and frequency resolution problems are results of a physical phenomenon and exist regardless of the transform used, it is possible to analyze any signal by using an alternative approach called the multi-resolution analysis (MRA) MRA analyzes the signal at different frequencies with different resolutions Every spectral component is not resolved equally as was the case in the STFT MRA is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies This approach makes sense especially when the signal at hand has high frequency components for short durations and low frequency components for long durations Fortunately, the signals that are encountered in practical application are often of this type Figure 1.21 shows a signal of this type It has relatively low frequency component throughout the entire signal and relatively high frequency components for a short duration somewhere around the middle

Figure 1.21 Example of a signal has high frequency components for short

The Continuous Wavelet Transform

The continuous wavelet transform was developed as an alternative approach to the short-time Fourier Transform to overcome the resolution problem The wavelet analysis is done in a similar way to the STFT analysis, in the sense that the signal is

multiplied with a function, similar to the window function in the STFT, and the transform is computed separately for different segments of the time-domain signal However, there are two main differences between STFT and CWT:

- The Fourier Transforms of the windowed signals are not taken, and therefore single peak will be seen corresponding to a sinusoid, i.e., negative frequencies are not computed

- The width of the window is changed as the transform is computed for every single spectral component, which is probably the most significant characteristic of the wavelet transform [29]

The continuous wavelet transform of a time series x (t) defined as follows:

ѱ (t) is the transforming function or mother wavelet with effective length (t) that

is usually much shorter than the target time series x (t) The term mother wavelet gets its name due to two important properties of the wavelet analysis The term “wavelet” means a small wave The smallest refers to the condition that this window function is

of finite length The wave refers to the condition that this function is oscillatory The term mother implies that the functions with different regions of support that are used

in the transformation process are derived from one main function, or the mother wavelet In other words, the mother wavelet is a prototype for generating the other window functions

The variables are s and τ, where s is the scale or dilation factor that determines the characteristic frequency so that its variation gives rise to a spectrum and τ is the translation in time so that its variation represents the sliding of the wavelet over x (t) The wavelet spectrum is thus customarily displayed in a time–frequency domain For low scales, i.e., when |s|≪ 1, the wavelet function is highly concentrated (shrunken compressed) with frequency contents mostly in the higher frequency bands Inversely, when |s| ≫ 1, the wavelet is stretched and contains mostly low frequencies

(1.3)

For small scales, a more detailed view of the signal occurs (known also as a higher resolution), whereas for larger scales, a more general view of the signal structure can

be expected

The term “translation” is used in the same sense as it is used in the STFT; it is related to the location of the window, as the window is shifted through the signal This term, obviously, corresponds to the time information in the transform domain However, we do not have a frequency parameter, as we had before for the STFT Instead, we have scale parameter which is defined as 1/frequency The term frequency

is reserved for the STFT

The parameter “scale” in the wavelet analysis is similar to the scale used in maps

As in the case of maps, high scales correspond to a non-detailed global view (of the signal), and low scales correspond to a detailed view Similarly, in terms of frequency, low frequencies (high scales) correspond to a global information of a signal, whereas high frequencies (low scales) correspond to a detailed information of

a hidden pattern in the signal (that usually lasts a relatively short time) Cosine signals corresponding to various scales are given in Figure 1.22