SPATIAL ANALYSIS OF SIGNAL DURING EPILEPTIC SEIZURE ON FLAT ELECTROENCEPHALOGRAPHY

GOH CHIEN YONG

A thesis submitted in fulfilment of the requirements for the award of the degree of

Doctor of Philosophy (Mathematics)

Faculty of Science Universiti Teknologi Malaysia

JANUARY 2017

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To my beloved family, fo r your love and support. To my friends, fo r your wits, intelligence and guidance in life.

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ACKNOW LEDGEMENT

First and foremost, I would like to express my deepest gratitude and appreciation to my supervisor, Prof. Dr. Tahir Bin Ahmad and my co-supervisor, Assoc. Prof. Dr. Normah Binti Maan, for their supervision, advices and encouragement. Their kindness and generosity in sharing knowledge and experiences are very much appreciated.

I am also greatly indebted to Universiti Teknologi Malaysia (UTM) for offering me the fast-track programme. Being a fresh graduate, the life of PhD study has been a great challenge yet an exciting journey.

I am very grateful to have financial support from Zamalah Universiti Teknologi Malaysia (Zamalah UTM) for one year and MyPhD scholarship (MyBrain 15 Program) by Ministry of Higher Education (MOHE) for three years.

Besides, I am also heartfelt thankfulness to all of my friends who have shared their knowledge with me. I would like to take this opportunity to extend my deepest appreciation to those who have helped me in the research and completed the thesis.

In addition, I would also like to thank the developers of the utmthesis LATEX project for making the thesis writing process a lot easier for me. Thanks to them, I could focus on the content of the thesis, and not waste time with formatting issues. Those guys are awesome.

Finally yet importantly, I am eternally grateful to my beloved family who always give me the positive incentive by listening, understanding, enlightening, supporting and encouraging me to continue the studies. These are all about their love and care to me.

Goh Chien Yong, UTM

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ABSTRACT

The human brain is the most complex structure in the universe. It is made up of billions of nerve cells called neurons. Studies on human brain were started centuries ago whereby various diagnosis instruments and techniques have been developed to understand it. Epilepsy is the second most common brain disorder. Electroencephalogram (EEG) was invented and widely used for recording human brain electrical activities. It is considered as the best tool which has been used in epileptic analysis. However, the best visual inspection still highly relies on experienced electroencephalographers or neurophysiologists. Due to this restriction, extraction of the hidden information from EEG signal during epileptic seizure is important. In this work, a new spatial interaction model which is based on basic gravity model is developed and applied on flat Electroencephalography ( f E E G) . The model is used to study the interaction among clusters on f E E G . The images of these interactions are then verified and compared to interaction images of spherical domain model of charges in the brain. The strength of interaction force inside the spherical domain of charges’ path is calculated. The results showed that the interaction of the clusters are not directly proportional to distance, potential difference of cluster and size of cluster’s charge. This study concurs the chaotic behavior of epileptic seizure as advocated by Iasemidis and his fellow researchers.

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ABSTRAK

Otak manusia ialah struktur yang paling rumit dalam alam semesta. Ia terbentuk daripada berbilion sel saraf yang dikenali sebagai neuron. Kajian

terhadap otak manusia telah dijalankan sejak berabad-abad yang lalu dengan pelbagai instrumen dan teknik diagnosis telah dibangunkan untuk memahaminya. Sawan

merupakan penyakit kedua yang sangat biasa dikaitkan dengan gangguan otak. Elektroensifalogram (EEG) telah dicipta dan digunakan secara meluas untuk mencerap aktiviti elektrik dalam otak manusia. Ia adalah peralatan terbaik yang diguna untuk menganalisa sawan. Namun demikian, pemeriksaan terbaik secara visual masih

bergantung kepada pakar elektroensifalografi atau neurofisiologi yang berpengalaman. Disebabkan oleh kekangan ini, pengekstrakan maklumat tersembunyi daripada isyarat EEG semasa serangan sawan adalah amat penting. Dalam kajian ini, satu model

ruang interaksi baru yang berasaskan model graviti asas telah dihasilkan serta diaplikasikan ke atas Elektroensefalogram Rata (fE E G ). Model ini digunakan untuk mengkaji interaksi antara gugusan f E E G . Imej-Imej daripada interaksi ini seterusnya ditentusahkan dan dibandingkan dengan imej interaksi yang berdasarkan

model domain sfera cas-cas di dalam otak. Kekuatan daya interaksi di dalam domain sfera laluan cas-cas ditentukan. Keputusan menunjukkan bahawa interaksi antara

gugusan adalah tidak berkadar terus dengan jarak, beza keupayaan gugusan dan saiz cas gugusan. Kajian ini mendapati perilaku serabut daripada serangan sawan seperti dinyatakan oleh Iasemidis dan rakan penyelidiknya.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION iiDEDICATION iiiACKNOW LEDGEMENT ivABSTRACT vABSTRAK viTABLE OF CONTENTS viiLIST OF TABLES xiLIST OF FIGURES xii

LIST OF ABBREVIATIONS xviLIST OF SYMBOLS xviiiLIST OF APPENDICES xix

1 INTRODUCTION 11.1 Introduction 11.2 Research Background 21.3 Problem Statement 41.4 Research Objective 51.5 Scope of Research 51.6 Significance of Findings 51.7 Thesis Outline 6

2 LITERATURE REVIEW 92.1 Introduction 92.2 Epilepsy and Seizure 92.3 Chaos Theory on Epilepsy 122.4 Electroencephalogram 14

2.4.1 EEG techniques 152.4.2 Scalp EEG Instrument and Recording

Technique 17

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2.5 Fuzzy Topographic Topological Mapping 18

2.6 Flat EEG 192.7 What is Spatial Analysis 232.8 History of Spatial Analysis 252.9 Definition and Category of Spatial Analysis 302.10 Spatial Interaction 322.11 Modification of Gravity Model 352.12 Data visualization 392.13 EEG Data As Spatial Information 392.14 Conclusion 40

3 SPATIAL INTERACTION M ODEL FO R FLAT EEG 413.1 Introduction 413.2 Flat EEG As Spatial Information 413.3 Initial Spatial Interaction Model of Flat EEG 423.4 Improved Spatial Interaction Model of Flat EEG 473.5 Spatial Interaction Image of Flat EEG 563.6 Conclusion 57

4 IMPLEMENTATION 584.1 Introduction 584.2 Spatial Interaction Image Per Second 59

4.2.1 Constant Values in Spatial InteractionModel 59

4.2.2 Resolution of Spatial Interaction Image 614.2.3 Fuzzy Membership of Pixels 61

4.3 Standardised Spatial Interaction Image 624.3.1 Outlier 624.3.2 Interquartile Range 63

4.4 Conclusion 65

5 VERIFICATION 715.1 Introduction 715.2 Three Dimensional Spatial Interaction Image

Projection 715.3 Spatial Interaction Cube Image 765.4 Conclusion 77

6 THREE DIMENSIONAL SPATIAL INTERACTIONIMAGE PRO JECTIO N VERSUS SPATIAL INTERAC­TION CUBE IMAGE 80

6.1 Introduction 806.2 Data Comparison of Two Models 806.3 Data Analysis on Three Dimensional Spatial

Interaction Image Projection 816.3.1 Effect of clusters quantity 816.3.2 Effect of radius of clusters 836.3.3 Effect of potential difference 846.3.4 Effect of distance between clusters 84

6.4 Data Analysis of Spatial Interaction Cube Image 876.4.1 Effect of clusters quantity 876.4.2 Effect of radius of clusters 876.4.3 Effect of potential difference 886.4.4 Effect of distance between clusters 90

6.5 Conclusion 92

7 INTERSECTION W ITH SPHERICAL DOMAINMODEL OF CHARGES 937.1 Introduction 937.2 Intersection with Three Dimensional Spatial Inter­

action Image Projection 937.2.1 Contributors of Intersection of Three

Dimensional Spatial Interaction Image Projection and Spherical Domain Modelof Charges 95

7.3 Intersection with Spatial Interaction Cube Image 987.3.1 Contributors of Intersection of Spatial

Interaction Cube Image and Spherical Domain Model of Charges 99

7.4 Conclusion 103

8 CONCLUSION 1058.1 Concluding Remarks 1058.2 Significance of Future Research 1068.3 Suggestion for Future Study 106

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REFERENCES

Appendices A - C

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114-121

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Causes of death among people with epilepsy. (Epilepsy. et al.,2005) 13

2.2 Comparison between Isaac Newton’s law of universalgravitation and gravity model of spatial interaction. 33

2.3 The functions of three classes of gravity models. 343.1 Five cluster centers with its potential difference for t = 5. 463.2 The spatial interaction index of five cluster centers for t = 5. 473.3 Example of three cluster centers. 533.4 Position of P and ^ Ci(Pk). 543.5 Result of example of three cluster centers on (3 x 3 pixels)

platform. 563.6 Numerical results of Example 3.1. 574.1 Cluster centers of potential difference of patient A during

epileptic seizure. 60

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LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Human Brain. 2

1.2 Release of neurotransmitters. 31.3 EEG system. 41.4 Research Framework. 8

2.1 Epilepsy patient during seizure. 122.2 EEG signal. 142.3 EEG system for epileptic analysis. 152.4 Scalp EEG. 162.5 Intracranial EEG. 172.6 Ten-twenty electrode system. 182.7 FTTM Version 1. 19

2.8 Fauziah’s EEG coordinate system. 202.9 EEG projection. 202.10 Flat EEG. 212.11 Flat EEG with clusters. 222.12 Graph of optimal number of cluster versus time. 222.13 Previous Work Done. 242.14 Example of spatial data. 252.15 The star map in Lascaux caves. 262.16 Example of square point lattice. 262.17 Example of PPA. 272.18 Example of NNA. 272.19 Example of Krigin technique used in precipation analysis. 282.20 GIS technology. 292.21 Isaac Newton’s law of universal gravitation. 33

2.22 The changing theoretical basis of spatial interaction models. 352.23 Effect of ft exponent on the interaction index. 372.24 Effect of A exponent on the interaction index. 382.25 Effect of a exponent on the interaction index. 383.1 Flat EEG at t = 5. 46

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3.2 Graph of distance versus spatial interaction index at t = 5. 473.3 Digital f E E G with labeled electrodes with respect to 10-20

system. 483.4 Grey-scale graphic of Spatial Interaction Image for Example

3.1. 574.1 Flow of Spatial Interaction Image model for 1 pixel. 664.2 EEG signal of patient A. 674.3 fE E G of patient A during epileptic seizure. 674.4 Spatial Interaction Images of patient A during epileptic

seizure. 684.5 Histogram of total interaction index from t =1 to t =10. 69

4.6 Boxplot of total interaction index from t =1 to t =10. 694.7 Spatial Interaction Images of patient A during epileptic

seizure with respect to time. 705.1 Process of EEG data transforms into 3 dimensions. 735.2 Process of three dimensional projection of Spatial Interaction

Image. 745.3 Three dimensional projection of Spatial Interaction Image of

Patient A during epileptic seizure. 755.4 Process of spatial interaction model applies on Spherical

Domain Model of Charges. (Noraini, 2012) 78

5.5 Spatial interaction model on Ce e g of Patient A duringepileptic seizure. 79

6.1 Percentage of “strong interaction region” of two models vstime. 81

6.2 Percentage of “strong interaction region” vs cluster numbervs time. (Three Dimensional Spatial Interaction Image Projection). 82

6.3 Percentage of “strong interaction region” versus radius of clusters versus time. (Three Dimensional Spatial InteractionImage Projection). 83

6.4 Percentage of “strong interaction region” versus potentialdifference of clusters versus time. (Three Dimensional Spatial Interaction Image Projection). 85

6.5 Percentage of “strong interaction region” versus distancebetween of clusters versus time. (Three Dimensional Spatial Interaction Image Projection). 86

6.6 Percentage of “strong interaction region” vs cluster number

vs time. (Spatial Interaction Cube Image). 88

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6.8

6.9

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

7.10

7.11

7.12

A.1B.1

B.2

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Percentage of “strong interaction region” versus radius of clusters versus time. (Spatial Interaction Cube Image). 89Percentage of “strong interaction region” versus potential difference of clusters versus time. (Spatial Interaction Cube Image). 90Percentage of “strong interaction region” versus distance between of clusters versus time. (Spatial Interaction Cube Image). 91Process of calculating interception of Three Dimensional Spatial Interaction Image Projection and Spherical Domain Model of Charges. 94Intersection percentage of Three Dimensional Spatial Interaction Image Projection and Spherical Domain Model of Charges. 95Intersection percentage versus clusters number. (Three Dimensional Spatial Interaction Image Projection). 96Intersection percentage versus radius of clusters. (Three Dimensional Spatial Interaction Image Projection). 97Intersection percentage versus potential difference of clusters.(Three Dimensional Spatial Interaction Image Projection). 98Intersection percentage versus distance between of clusters.(Three Dimensional Spatial Interaction Image Projection). 99Process of calculating interception of Spatial Interaction Cube

Image and Spherical Domain Model of Charges. 100Intersection percentage of Spatial Interaction Cube Image and Spherical Domain Model of Charges. 101Intersection percentage versus clusters number. (Spatial Interaction Cube Image). 101Intersection percentage versus radius of clusters. (Spatial Interaction Cube Image). 102

Intersection percentage versus potential difference of clusters.(Spatial Interaction Cube Image). 103Intersection percentage versus distance between of clusters.(Spatial Interaction Cube Image). 104Ethical Approval by HUSM. 114Spatial Interaction Images of patient B during epileptic seizure with respect to time. 115Three dimensional projection of Spatial Interaction Image of patient B during epileptic seizure. 117

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B.3 Spatial interaction model on Ceeg of patient A duringepileptic seizure. 119

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LIST OF ABBREVIATIONS

EEG - Electroencephalogram

HUSM - Hospital Universiti Sains Malaysia

HKL - Hospital Kuala Lumpur

ILAE - International League Against Epilepsy

PSE - Photosensitive Epilepsy

SUDEP - Sudden unexpected death in epilepsy

SPECT - Single-photon emission computed tomography

fMRI - Functional magnetic resonance imaging

PET - Positron emission tomography

NIRS - Near-infrared spectroscopy

MEG - Magnetoencephalography

iEEG - intracranial EEG

EMG - electromyography

FTTM - Fuzzy Topographic Topological Mapping

MC - Magnetic Contour Plane

BM - Base Magnetic Plane

FM - Fuzzy Magnetic Field

TM - Topographic Magnetic Field

FRG - Fuzzy Research Group

Ceeg - Fauziah’s EEG coordinate system

f E E G - Flat EEG.

PC - Partition Coefficient

CS - Compactness Separability

FCM - Fuzzy c-Means

PPA - point pattern analysis

NNA - Nearest neighbour analysis

RVT - Regionalized variable theory

NCGIA - National Center for Geographic Information and Analysis

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TFL - Toblers First Law

MAUP - Modifiable Areal Unit Problem

ESDA - Exploratory Spatial Data Analysis

GIS - Geographic information system

CCG - Centre for Computational Geography

CSISS - Center for Spatially Integrated Social Science

USGIF - United States Geospatial Intelligence Foundation

CGA - Center for Geographic Analysis

IQR - Interquartile Range

i.e. - that is

e.g. - for example

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LIST OF SYMBOLS

R - set of real numbers

R+ - set of positive real numbers

G - is an element of

^ - is mapping to

= - is equal to

= - is not equal to

= - is greater than or equal to

> - is greater than

< - is less than or equal to

< - is less than

£ - the sum of

Z - set of integers

U - union

A \ B - belong to set A and not to set B

AC - complement of set A

C - is subset of

I - such that

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LIST OF APPENDICES

APPENDIX TITLE PAGE

A Ethical Approval by HUSM 114B Patient B 115

C Publications 121

CHAPTER 1

INTRODUCTION

1.1 Introduction

Brain is the most magnificent organ of a human body that plays the character of controlling all of our thinking and movement. It is a neural network which built by billions of nerve cells, namely neurons. The neural activity of human brain starts between 17th and 23rd week of the prenatal development (Tyner et al., 1983). In addition, all the building blocks of human brain, such as brain cells, brain molecules, neurotransmitters and synapses are almost identical in all animals. The question is: Does it mean that the larger brain is made of larger number of neurons? In other words, does larger brain process larger computational abilities than smaller brain?

Generally, an adult human brain’s weight is between 1.2-1.5 kilograms, but elephant brain’s weight is between 4-5 kilograms (see Figure 1.1) (Mink et al., 1981). Human brain has the same general structure as the brains of other animals, especially mammals. Human is the only one can generate higher consciousness associated with ingenuity, such as writing, planning, and so on. Scientists believed that these abilities may come from humans that have a more developed cerebral cortex than others, thus human has the largest number of neurons in the cerebral cortex.

Even the human brain is taking an important role as centre of intelligence, interpreter of the senses, initiator of body movement, and controller of behaviour. It is only about 2% of a human’s body weight. However, human devotes 20-25% of basal metabolism for it, whereby other vertebrate species use only 2-8% (Mink et al., 1981). Centuries ago human did start to study on human brains, but to date, they still view the brain as nearly incomprehensible. In fact, the human brain is known as the most complex structure in the known universe.

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Figure 1.1: Human Brain. (From http://bricspl.com/human-brain/index.html)

Since the human brain is such important to an individual, when a disorder come to the brain, his normal living may be devastating. In general, brain disease may come in a few forms, such as infections, trauma, stroke, seizures, and tumours. The complexity of the human brain induces scientists fail to detect the cause of some brain diseases, for example Alzheimer’s disease and epilepsy seizure (Bao et al., 2009). There are some cases have resulted in the permanent damage. On the contrary there are

treatments for other cases, which may involve surgery, medicines, or physical therapy.

While normal brain functioning, millions of tiny bioelectrical charges between nerve cells are produced for transmitting information. However if there is excessive

and hypersynchronous activities of the neurons that causing a miniature brainstorm, the human may experience epileptic seizure (Sanei and Chambers, 2013). During seizure, patient may have uncontrolled muscle movements, sensory disturbances, loss

or alteration in consciousness. In addition, if the seizure is recurrent and unprovoked, the patient is potentially having epilepsy.

1.2 Research Background

Life electrical signals are signals that generated by human brain, which resulted by the transportation of neurotransmitters from one neuron to another neuron (see

Figure 1.2). It is believed that the signals not only presenting the brain function or motion, but also deliver messages about status of the whole body (Tyner et al., 1983). This assumption has brought out the motivation to study the signal processes of human

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brain. A variety of techniques and instruments were invented for the study, including Electroencephalogram (EEG) (see Figure 1.3).

Mitochondrion

Receptor site Neurotransmitter

Figure 1.2: Release of neurotransmitters.(from http://www.epilepsyresearch.org.uk/about-epilepsy/background-to-

seizures/background_to_seizures_synapse/)

The first epilepsy surgery was on 25th May 1886, operated by Victor Horsley at National Hospital for the Paralysed and Epileptic at Queen’s Square in London (Wolf et al., 2001). The term of “epileptogenous focus” was first introduced by Horsley (1886). After that, the epilepsy surgery come to the first big wave. However the successfulness of the surgery was very limited, with a mortality of 5-7%. This result urged neurosurgeons to come up with another way to treat the epilepsy, such as the introduction of phenobarbital in 1912.

In the 1940s, EEG was dev eloped to provide the necessary localization for diagnosis and treatment of brain lesions. This new development was brought the epilepsy surgery to the second wave, which started by Penfield and Flaniein (1950) with Bailey and Gibbs (1951). To date, EEG is still considered as the best method

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Figure 1.3: EEG system.(from https://freudforthought.wordpress.com/2015/05/06/seeing-inside-the-brain/)

used in epileptic analysis.

Even though EEG can record all of the electrical activity in the brain during seizures epileptic, but the output is in the waveform, which makes the visual inspection become very subjective and hardly allows any statistical analysis or standardization. In order to overcome this problem, the transformation from high dimensional EEG signal to lower dimension of image form is conducted via flat EEG ( f E E G ) (Zakaria, 2007). This transformation allows EEG signals to be compressed and analyzed.

Researchers believe that through further improvement of this technique of Flat EEG, the origin of electrical activity inside the brain may be possible to be determined.

1.3 Problem Statem ent

The Universal Law of Gravitation states that a particle acts forces on every other particles in the universe. However, Coulomb’s Law claims that the magnitude and sign of electric force are determined by the electric charge (Gregersen, 2011). According to Coulomb’s Law, charges particles will repel each other and create

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electrical repulsive force when they are alike charges. This phenomena may be true also for life electrical signals that generated by human brain, which carry positive charges (+K and +Na). These information are hidden in transformed EEG Signal (f E E G ). Therefore, this research will attempt to extract the interaction relationship (repulsion forces) through spatial analysis.

1.4 Research Objective

The main objective if this research is to identify the interaction relationship of clusters on f E E G . In doing so, a simulation model is first to be built to show the spatial interaction among clusters on fE E G in image form. From there, the factor/factors which impact on spatial interaction forces of clusters will be identified. Furthermore, the model will be developed into three dimensional view and verify with Spherical Domain if Charges.

1.5 Scope of Research

In this research, the process of the clustering the EEG signal on Flat EEG was proposed by Zakaria (2007) and will be directly used as input data of spatial analysis. These data were obtained from Hospital Universiti Sains Malaysia (HUSM) and Hospital Kuala Lumpur (HKL).

1.6 Significance of Findings

The contributions of the findings in this study are:

1. a mathematical model that can describe the interaction relationship of clusters on Flat EEG;

2. introduce an application of fuzzy membership function into spatial interaction model;

3. a spatial interaction model that can represent the interaction among EEG data during seizure in image form.

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1.7 Thesis Outline

This thesis contains seven chapters. Its framework is shown in Figure 1.4. Chapter 1 provides the general information about the research, including the research background, problem statement, research objectives, scope of research and the significance of the findings. It enables readers to have an overview on concept of the whole thesis.

Chapter 2 gives the literature review of epilepsy, seizure, electroencephalogram and flat electroencephalography. Some medical information such as definition of epilepsy and seizures, type of electroencephalography technique will be presented in this chapter. Also, the mathematical background of spatial analysis will be documented in this chapter. The history of spatial analysis will be discussed, where it expands the application from cartography and surveying to ecology, epidemiology, environmental resource management and many other criteria. Furthermore, the contentious of classifying spatial analysis techniques and general review of spatial interaction models will be presented.

The construction of the spatial interaction model on the platform of Flat EEG will be given in Chapter 3, which includes an introduction of a membership function on the gravity model. An example will be discussed in this chapter.

The implementation of the model will be given in Chapter 4, the output of the model is spatial interaction image for every single second, which is a greyscale image. In chapter 5, the model will be improved by standardising the image by introducing a simple statistical method, namely interquartile range. This standardised method will represents the output in a series of colour image.

The spatial interaction model will then be verified in Chapter 6 by the Spherical Domain Model of Charges. Additionally, the application of the model to the Spherical Domain Model of Charges will be carried out. The outputs of the projection of spatial interaction image from Flat EEG into Spherical Domain Model of Charges and the application of spatial interaction model on Spherical Domain Model of Charges will be compared. Besides, the intersection among Spherical Domain Model of Charges and Spatial Interaction Image will also be identified through slicing technique and analysis in Chapter 7.

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Finally, Chapter 8 concludes this thesis which highlighting the significance of the research and providing some suggestions for future research.

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SPATIAL ANALYSIS OF ELECTROENCEPHALOGRAM SIGNAL DURING EPILEPTIC SEIZURE OF FLAT EEG.

CHAPTER 1Introduction

CHAPTER 21. Epilepsy and Seizure2. Electroencephalogram3. Flat EEG4. History of Spatial Analysis5. Definition and Category of Spatial Analysis6. Spatial Interaction7. EEG Data As Soatial Information

M ethodology________________________

CHAPTER 31. Flat EEG As Spatial Information2. Initial Spatial Interaction Model of Flat EEG3. Improved Spatial Interaction Model of Flat EEG4. Spatial Interaction Image Model of Flat EEG

Im plementation & Verification______

CHAPTER 4 & 51. Spatial Interaction Image Per Second2. Spatial Interaction Image for Some Period of Time

> Interquatile Range

CHAPTER 6 & 71. Projection by Spherical Domain Model of Charge2. Spatial Interaction Image Model of Spherical Domain Model

of Charge3. Slicing Technique

CHAPTER 8Conclusion

Figure 1.4: Research Framework.

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117

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