SIM UNIVERSITY
SCHOOL OF SCIENCE AND TECHNOLOGY
EARLY DETECTION OF EPILEPSY:
COMPARATIVE STUDY
STUDENT
SUPERVISOR
PROJECT CODE
: ANG CHAI YING BRENDA (Y0706295)
: DR RAJENDRA ACHARYA UDYAVARA
: JUL2010/BME/010
A project report submitted to SIM University in partial fulfillment of the
requirements for the degree of Bachelor of Biomedical Engineering
November 2011
Table of Contents
Page
ABSTRACT……………………………………………………………………………5
ACKNOWLEDGEMENT……………………………………………………………...6
LIST OF FIGURES…………………………………………………………………….7
LIST OF TABLES……………………………………………………………………...7
AIMS……………………………………………………………………………………8
OBJECTIVES…………………………………………………….………………...8
SCOPE………………………………………………………………………….…..8
CHAPTER ONE………………………………………………………………………...10
LITERATURE REVIEW…………………………………………………………...10
1.1. Epilepsy………………………………………………………………………...10
1.2. Seizures………………………………………………………………………....10
1.3. The Anatomy of the Human Brain……………………………………………..11
1.3.1. The Brain Structure……………………………………………………...11
1.3.1.1.
The Forebrain………………………………………………....…12
1.3.1.2.
The Midbrain………………………………………………...….12
1.3.1.3.
The Hindbrain………………………………………………...…12
1.3.2. The Neurons……………………………………………………………..13
1.4. Types of Seizures Symptoms…………………………………………………...14
1.4.1. Generalized seizures……………………………………………………..14
1.4.1.1.
Absence seizure………………………………………………….14
1.4.1.2.
Myoclonic seizure……………………………………………….15
1.4.1.3.
Atonic seizure…………………………………………………....15
1.4.1.4.
Tonic seizure…………………………………………………….15
1.4.1.5.
Clonic seizure……………………………………………………15
1.4.2. Partial seizures…………………………………………………………..15
1.4.2.1.
Complex partial seizure…………………………………………15
1.4.2.2.
Simple partial seizure…………………………………………...15
1.5. Electroencephalogram (EEG) and Other Screening Tests……………………..16
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Page
CHAPTER TWO……………………………………………………………………….18
METHODOLOGY…………………………………………………………………18
2.1. Data…………………………………………………………………………….18
2.2. Higher Order Spectra Analysis (HOS)………………………………………...19
2.3. Features Extracted……………………………………………………………..21
2.3.1. Normalized bispectral entropy (Ent1):…………………………………21
2.3.2. Normalized bispectral squared entropy (Ent2):………………………...21
2.3.3. Normalized bispectral cubed entropy (Ent3):………………………….22
2.3.4. Bispectrum phase entropy (EntPh):……………………………………22
2.3.5. Mean bispectrum magnitude: (mAmp)………………………………...22
2.3.6. Weighted Center of Bispectrum ( wc1  wc )………………………….22
2.3.7. Moments of Bispectrum ( H1  H 5 ).......................................................23
2.4. ANOVA Test………………………………………………………………....23
2.5. Types of Classifiers…………………………………………………………...24
2.5.1. Fuzzy Sugeno Classifier……………………………………………….24
2.5.2. Probabilistic Neural Network (PNN)………………………………….24
2.5.3. K-Nearest Neighbor (KNN)……………………………………………24
2.5.4. Decision Tree (DT)…………………………………………………….24
CHAPTER THREE…………………………………………………………………...26
RESULTS……………………………………………………………………………..26
3.1. HOS Features…………………………………………………………………26
3.2. Classifiers Performance Parameters………………………………………….30
3.3. Classifiers Performance Outcome……………………………………………31
DISCUSSION………………………………………………………………………....36
CHAPTER FOUR…………………………………………………………………….37
SUMMARY…………………………………………………………………………..37
CONCLUSION……………………………………………………………………….37
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Page
RECOMMENDATION………………………………………………………………37
CHAPTER FIVE……………………………………………………………………..39
PROJECT MANAGEMENT………………………………………………………...40
CHAPTER SIX……………………………………………………………………....42
REFLECTION……………………………………………………………………….42
REFERENCE………………………………………………………………………...44
APPENDIX A- DATA OF HOS FEATURES………………………………………46
APPENDIX B- MEETING LOGS.......................................................................…...53
APPENDIX C- PROGRAMMING CODES………………………………………...57
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ABSTRACT
______________________________________________________
The project objective is to perform a comparative study on early detection of epilepsy
using Higher Order Spectra (HOS) analysis method. The MATLAB application is used to
extract and determine the features from the various types of electroencephalography
(EEG) signals. The selected features are bispectral entrophy (Ent1), bispectral squared
entrophy (Ent2), bispectrum phase entropy (EntPh), mean bispectrum magnitude
(mAmp), and weighted center of bispectrum (wc1, wc2, wc3 and wc4). They represent the
three mental states. The features are inputs that fed into the different classifiers to
perform automatic classification known as Normal, Background (Pre-ictal) and Epilepsy
(ictal). Early epilepsy detection allows caregivers to have precaution over the epileptic
individual, allow first time individual to seek for early treatment upon diagnosis and
improve their quality of life.
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ACKNOWLEDGEMENT
________________________________________________________________________
I would like to thank SIM University for offering this Biomedical Engineering Degree
program. Through this program, I am enriched with greater knowledge in medical,
healthcare and therefore able to appreciate the importance of life.
I owe my deepest gratitude to a man with great nobilities, my supervisor, Dr Rajendra
Acharya U. His patience, care, motivation and positive virtues have greatly affected my
life and thinking. The field experience he possesses has encouraged and broadens my
horizon in signal processing method and utilizing it to serve as a great tool for mankind.
His patience guidance and valuable advices has made this project completion possible.
I would like to express my gratitude to the following:

Dr Jeremy Teo Choon Meng, scientist of Swiss Federal Institute of Technology
(EPFL) who has helped me by retrieving journal papers and provides constant
encouragements.

Mr. Alvin Ang, a student of Ngee Ann Polytechnic for his precious time in
coaching MATLAB application and guidance on the various types of classifiers.

Bonn University for the EEG database that provided the source of EEG signals.
I would like to thank my family and grandparents for their love, support and
encouragement throughout my course of study and final year project development,
especially to my encouraging husband who motivates and believes in me in achieving my
goals. Lastly, to my great friends Zhu Huifang, Janet Chim, Toh Shin Ni, Vicky Goh,
Chen Yimin, Karen Chong, Zack Lim, Koh Liwei and Ellen Tang who are always there
for me cheering and encouraging me during the project development and upon
completion.
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LIST OF FIGURES
______________________________________________________
Figure 1.1
Figure 1.2
Figure 1.3
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5(a)
Figure 2.5(b)
Figure 2.5(c)
Figure 2.6(a)
Figure 2.6(b)
Figure 2.6(c)
Figure 2.7(a)
Figure 2.7(b)
Figure 2.7(c)
Anatomy of the Human Brain
The Development of the Central Nervous System (CNS)
Neuron Cell
Block Diagram of Project Process Flow
Normal, Ictal and Pre-ictal EEG signals
Testing and Training Ratio for 100 Segment Data
Real Signals Computation at Non-redundant Region
Normal EEG Bispectrum Magnitude Contour Plot
Normal EEG Bispectrum Magnitude Colour Map Plot
Normal EEG Bicoherence Plot
Background EEG Bispectrum Magnitude Contour Plot
Background EEG Bispectrum Magnitude Colour Map Plot
Background EEG Bicoherence Plot
Seizure EEG Bispectrum Magnitude Contour Plot
Seizure EEG Bispectrum Magnitude Colour Map Plot
Seizure EEG Bicoherence Plot
Page
11
11
13
17
18
18
20
31
31
31
32
32
32
33
33
33
LIST OF TABLES
______________________________________________________
Page
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 3.5
Table 3.6
Table 3.7
Table 3.8
Table 3.9
Table 4.0
Bispectral Entrophy (Ent1), Bispectral Squared Entrophy (Ent2),
Bispectral Cubed Entrophy (Ent3), Bispectrum Phase Entrophy
(EntPh), Mean Bispectrum Magnitude (mAmp)
Bispectrum Moments represented by H1 to H5
Bispectrum Weighted Center represented by wc1 to wc4
ANOVA Test perform on 3 classes of EEG data with selected features
Prediction Table
Result of TN,FN,TP,FP,Accuracy,PPV,Sensitivity,Specificity for
Various Classifiers.
Gantt Chart Phase 1
Gantt Chart Phase 2
Gantt Chart Phase 3
Gantt Chart Phase 4
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25
25
28
29
29
37
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AIMS
______________________________________________________
OBJECTIVES
The aim of this project is to do a comparative study of early epilepsy detection. Epilepsy
is a recurring neurological disorder that can occur at any ages. The excessive excitation
of the neurotransmitters released in the cerebral cortex causes such disorder. Three sets of
recorded raw EEG signals were amplified and undergo a band pass filter set at
12dB/octave before HOS analysis. The High Order Spectra (HOS) signal processing
method was selected due to its characteristics that advantageous over other processing
method. The Fourier transform signals apply Fast Fourier Transformation (FFT)
algorithm to compute the data. The frequencies were normalized by Nyquist frequency. A
few normalized HOS features then extracted will undergo the ANOVA test. ANOVA is a
significant test done on the extracted features. This is to qualify the type of features that
will be used. The statistical significance of the selected features is about p<0.0001. The
chosen features were input to the Fuzzy Sugeno, Probabilistic Neural Networks, KNearest Neighbor, and Decision Tree classifiers for automatic classification. Hence, the
three sets of EEG data are classified into Normal, Pre-Ictal and Ictal. This classification
represents the neurological condition.
SCOPE
This project covers the involvement of MATLAB programming ability for the processing
of three classes of EEG signals, features extraction and performs an automatic
classification of epilepsy. The EEG signals are taken from the Bonn University Database.
The raw signals are input to a written MATLAB program that incorporated the Higher
Order Spectra (HOS) processing method. The processed data are the values representing
the features extracted by HOS and are further tested by ANOVA test for clinical
validation. The standard deviation, mean and p-value derived from ANOVA test
determine the accuracy and quality of the extracted features for the classifiers. The
various classifiers (e.g. Fuzzy, PNN, KNN, and DT) will reveal the average classification
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sensitivity, specificity, accuracy and Positive Predictive Value (PPV). From here, we
compare the performance of the various classifiers.
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CHAPTER ONE
______________________________________________________
LITERATURE REVIEW
1.1.
Epilepsy
Epilepsy is a neurologist term that describes a recurring neurological disorder
(recurrent seizures attack). It is neither contagious or a disease, but impinge on anyone
regardless of age, race, gender or social conditions
[3]
. It is most commonly found in
children and older people; however, the onset can occur at any age
[18]
. An epileptic
individual may experience repeated seizures and more than two are usually unprovoked.
According to an estimation made by the Singapore Epileptic Foundation, based on the
prevalence of epilepsy in most developed countries, Singapore has more than 20,000
people with epilepsy [9], 70% of the epileptic cases has no known cause and 30% is due to
reason such as severe head injuries, stroke, brain infection such as meningitis, brain
tumor or brain damage caused by a difficult birth
[9]
. Babies and children with epilepsy
make up about 0.5% to 1% of the population [9].
1.2.
Seizures
Seizures are events of abnormal electrical discharges that occur in the brain
without warning and cause an alteration in physical sensation, behavior or consciousness.
Such alterations in physical conditions are often known as seizure symptoms. The
seizures always happen unexpectedly; individual is unaware of the brief interruption that
occurs in the brain. The experience with seizures is often estimated to last from a few
seconds to a few minutes dependent on the types of seizure they encountered. As the
seizures can be provoked or unprovoked, factors that provoked seizures arise from severe
clinical conditions such as trauma, metabolic, stroke, perinatal (around birth) or infection
[18]
.
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1.3.
The Anatomy of the Human Brain
1.3.1. The Brain Structure
Figure 1.1: Anatomy of the Human Brain.
The human brain is the most complex organ that weighs about 1.5kg
occupies a quantitative volume of about 1,350 cubic centimeters
[5]
[1]
and
. The illustration
shown in Figure 1.1 presents the seven structural components that formed the brain
system. They are the cerebral hemispheres, diencephalon, midbrain, cerebellum, pons,
medulla oblongata and spinal cord. Each region has their unique roles and responsibilities
towards maintaining homeostasis.
Figure 1.2: The Development of the Central Nervous System.
Relating the brain structures in the term of tissue level, a tube of primitive tissue shown
in Figure 1.2 presents the initial development stage of the Central Nervous System (CNS)
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till its maturity. The tissue enlargement highlighted in red, yellow and green regions in
the developed tube, correspond to the three principal divisions of the brain: the forebrain,
the midbrain, and the hindbrain [5].
1.3.1.1.
The Forebrain
Figure 1.2 shows the forebrain comprises the telencephalon and diencephalon. It
is composed of cerebral hemispheres and their ancillary structures
[5]
. The cerebral
hemispheres dominate the human CNS and contain neural machinery that is responsible
for the human thought [5].
The two large functional structures of the diencephalon are thalamus and hypothalamus.
It allows all telencephalic information to travel through. The thalamus serves as processor
for all the information reaching the cerebral cortex from the rest of the central nervous
[8]
system
[8]
, while the hypothalamus regulates autonomic, endocrine, and visceral function
.
1.3.1.2.
The Midbrain
The Midbrain (mesencephalon) in Figure 1.2 controls several sensory and motor
functions, including the eye movement and the coordination of the visual and auditory
reflexes [8].
1.3.1.3.
The Hindbrain
The developed Hindbrain in Figure 1.2 is divided into two distinct regions namely
the metencephalon (pons and cerebellum) and medulla oblongata. The pons conveys
information about any movement from the cerebral hemisphere to the cerebellum
[8]
. The
cerebellum that connects to the brain stem by several peduncles (major fiber tracts)
[8]
modulates the force and range movement and has its involvement in the learning of motor
skills
[8]
. The medulla oblongata is responsible for vital autonomic functions, such as
digestion, breathing and control of the heart rate [8].
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EEG signals are analyzed using reflexology. This study discovers that reflexology
had a positive influence to the brain signals
[11]
. Hence EEG signal carries vital mental
information that associated with the physical conditions of an individual. Another study
then confirms that EEG characteristic and location of the brain using different set of
music rhythm, does excite the brain
[12]
. Therefore in general, we know that the
occurrences of different types of seizure, external stimulus can affect the motor and
sensory function of the body. Symptoms are dependent on the location of the excessive
neuronal excitation. However, not all seizure symptoms appear to be jerky, shaking rapid
or uncontrollably as some seizures are mild.
1.3.2. The Neurons
Figure 1.3: Neuron Cell.
Defining CNS in terms of cellular level, there are about 100 billion nerve cells
(neurons)
[8]
and the connections between the nerve cells (synapses) are estimated to
range from 10 trillion to 100 trillion points
[8]
. The two types of nervous cells present in
the CNS are the neuroglia and neuron. The neuroglias are cells with no known
informational functions, but provide mechanical and metabolic support for the neurons
and maintain homeostasis in the nervous system [5].
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The neuron cell in Figure 1.3 consists of a cell body, dendrites and axon. It is a functional
unit in the nervous system that plays a critical role in processing information and
integrates the influences of the cells from which they receive
[5]
. The cell body contains
the nucleus of the cell and possesses all specialized organelles like mitochondria. The
dendrites processes information obtained from other cells and transmits that information
to the cell body of the neuron. The axon will carry information away from the cell body
to other neurons or muscle cells and terminate at axon terminal. The neuron has many
functionalities and one of its function is capable of releasing chemical substances knows
as the neurotransmitters. They are the chemical messengers of the brain where the
transmission of signals from a neuron to a target cell across a synapse is established
[10]
.
The neurotransmitters that involves in seizures are known as the excitatory
neurotransmitter (glutamate) and the inhibitory neurotransmitter (gamma-aminobutyric
acid)
[18]
. Hence, if the tight balanced between the excitatory and inhibitory
neurotransmitters
[18]
are disrupted. A neuronal misfiring will be activated (abnormal
electrical discharge) and affects the individual with abnormal clinical behaviors.
1.4.
Types of Seizures Symptoms
The two main categories of seizure known as the primary generalized seizures and
partial seizures symptoms are briefly introduced as below.
1.4.1. Generalized seizures
Generalized seizures takes place simultaneously from both sides of the brain and
often occur at an early age [18]. It can be sub-categorized into 5 different types of seizures
such as the following:
1.4.1.1.
Absence seizure
A sudden brief interruption of consciousnesses is experienced. The characteristics
are starring blankly with a glazed facial expression or lip smacking
[23]
. The duration of
the seizure usually last about 2 to 15 seconds. The epilepsy individual often has no
memory of what happens during a seizure but recovers immediately without confusion
after the seizure.
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1.4.1.2.
Myoclonic seizure
A portion or the entire body will experience a brief muscle contraction. Such
seizures often occur in the early morning or while going into sleep.
1.4.1.3.
Atonic seizure
A sudden loss of muscle tone and is prone to falling. The recovery is quick but
epilepsy individual has a higher risk of getting serious injuries to their head or other side
of their body.
1.4.1.4.
Tonic seizure
A sudden stiffness of the body is experienced and prone to risk of falling
backward. Such seizure usually last less than 60 seconds and the recovery is quick.
1.4.1.5.
Clonic seizure
Jerking movements of the body without stiffness on the body and the muscles
involves are both sides of the body.
1.4.2. Partial seizures
The partial seizures originate from either the left or right side of the brain and tend
to occur in a later age [18]. It is sub-categorized into simple partial and complex partial.
1.4.2.1.
Complex partial seizure
The vision of epileptic individual is disturbed. If there is impairment to the
consciousness, the epilepsy individual may experience an uncontrollable lip smacking,
chewing or swallowing. The duration is expected to last about 1 to 2 minutes [7].
1.4.2.2.
Simple partial seizure
It affects specific part of the brain and will spread to a larger part of the brain that
may result in complex partial seizure or a tonic clonic seizure
[23]
. The duration of this
seizure is about 90 seconds and the symptoms are no loss of consiousness, sudden jerking
and sensory phenomenon [23].
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1.5.
Electroencephalogram (EEG) and Other Screening Tests
Epilepsy can be access by a few screening tests such as Magnetic Resonance
Imaging (MRI), blood test or Electroencephalogram (EEG). The MRI is an advanced
brain scan technology that exceeds CAT scan or X-ray performance due to the increase of
slice images of the brain availability and enables the embedded brain thrombosis or
tumours in the brain to reveal vividly. The blood test screens are for chemical causes or
infection detection that may result epilepsy and diagnosis are also important for the
antiepileptic medication prescription for the prevention of rapid seizure manifestation in
epileptic patients.
The Electroencephalogram (EEG) is another process that the electroencephalographers
determine the type and location of the seizures that occur in the epileptic patients. The
surface electrodes are placed on varies position of the hair scalp to record the event of
electrical activities that is taking place in the brain. As the extracted EEG signals are very
small, the electrodes are electronically connected to an amplifier to amplify the EEG data
to viewable waveforms. The electroencephalographers then analyze the waveforms
displayed on the monitor and determine if the patient is suffering from epileptic
disturbance or other types of brain disorders. In the process of EEG recording, the
individual is required to perform some specific stimuli such as breathing heavily,
blinking of eyes or staring into flashing light. This is to diagnose specific cause that
provoked seizure known as reflex epilepsies. The abnormal EEG waveforms recorded are
presented in spikes for the electroencephalographers to spot the trend for seizures in the
epileptic region. The term ictal represents epilepsy and the spikes observe in the EEG
waveforms is known as interictal. Interictal denotes that the spikes occur in between the
seizures. Though EEG is a test to rule out seizures, it is important not confuse with
diagnosing epilepsy. EEG test is an application to provide a medical history of a patient
who experiences regular seizures attack. If the individual has no history of seizure and
has abnormal spikes in their EEG signal, this does not explain that they are abnormal.
They might have other neurological conditions to be rule out. Moreover, if the patient has
a history of recurring seizure and the EEG test happened to be benign, the doctor should
follow up in treating the patient, as epilepsy is a recurring neurological disorder. Hence,
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EEG signals that contain essential information is useful for analysis. It can predict seizure
attacks by reducing anxiety of the epileptic patients and allows an early detection in
improving the quality of life through technology [17].
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CHAPTER TWO
______________________________________________________
METHODOLOGY
Figure 2.1: Block Diagram of Project Process Flow.
2.1.
Data
The EEG signals that are used for this project were extracted from the database of
Bonn University, Germany. The three classes of data are Normal, Background (Pre-ictal)
and Epilepsy (ictal). Each class has 100 EEG segments available for the comparative
study. The normal EEG data were the medical records from five healthy volunteers in
relax conscious state with their eyes open. The ictal EEG data were recorded from five
epilepsy patients during the occurrence of seizures and the pre-ictal EEG data from the
same five patients were recorded when the seizures were absence. All signals were
recorded with an identical 128-channel amplifier system with a sampling rate of
173.61Hz and 12-bit A/D resolution. A setting of 12dB/octave is set to filter the EEG
data. Figure 2.1 shows a sample of the recorded EEG Signals.
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Figure 2.2: Normal, Epilepsy and Pre-ictal EEG Signals.
100 EEG segments represent 100 patients. 100 segments of data are divided into 3 types
of training to testing ratio. The first ratio is 7:3 and vice versa while the last ratio is 7:6:7
which represents training to testing to training ratio. All these combinations of training
and testing are to ensure all the higher order spectra features data are being trained using
classifiers for twice to ensure homogeneity of trained data sets before using different sets
of data for testing. Refer to Figure 2.3 for training to testing ratio illustration.
Training (70%)
Testing (70%)
Training (35%)
Testing (30%)
Testing (30%)
Training (30%)
Training (35%)
Figure 2.3: Testing and Training Ratio for 100 Segment Data.
2.2.
Higher Order Spectra Analysis (HOS)
The nature of the EEG signals is highly dynamic and non-linear [20]. For the past 3
decades, many papers mentioned about performing quantitative analysis to aid the
interpretation of EEG. In the mid 80’s, Lyapunov exponents and correlation dimension
study is carried out, a non-linear method to learn slow wave sleep signal
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[4]
. Since then,
19
non-linear analysis methods gained the interest of many researchers and several possible
clinical applications like seizures predictions were reported. Signal processing methods
differentiate the neurological activities between an abnormal and healthy individual.
Various measurement techniques such as time-domain, frequency domain, timefrequency and non-linear methodologies were also discussed [20]. Recently, HOS analysis
has gained its popularity due to its advantages. From
[13]
, the advantages are high signal
to noise ratio (SNR) due to elimination of Gaussian noise which is good for parameter
estimation. The signal phase properties and magnitude of the time series signal during
reproduction of the signal in frequency is preserved. Lastly, HOS has the ability to
differentiate various non-Gaussian signals for more accurate identification.
Spectra analysis is a detail examination of information in frequency domain using
statistical method. The information can be obtained from images, sound and other timevariant measurement data collected from sensors. The data will be processed using
mathematical operation like Fourier Transform (FT) to obtain the relevant distinct
features that can only be found in frequency domain. This methodology is signal
processing.
Statistical tool is chosen, as the data obtained is usually random, nonlinear and has a
normal distribution. It is common to see second order statistics is being used to interpret
the collected data. However, higher order that is third order statistics has gain popularity
due to more complete characterization of the signal.
This project focuses on third order statistical analysis features using bispectrum.
Bispectrum is the study of nonlinear interactions and the equation is shown below where
X(f) is a discrete fourier transform (DFT) of x(nT). E[.] is the expected value operator.
B( f1 , f 2 )  EX  f1 X  f 2 X   f1  f 2 
(1)
Equation (1) is derived from third order cumulant of the signal using DFT. Normalizing
bispectrum using power spectra which lead to bicoherence and given by
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Bnorm ( f1 , f 2 ) 
EX  f1 X  f 2 X   f1  f 2 
(2)
P f1 P( f 2 ) P( f1  f 2 )
Where P(f) is the power spectra [14].
2.3.
Features Extracted
In this study, seven features were being extracted to differentiate EEG signals.
The bispectral entropies are shown in equation (3), (4) and (5).
0.5
Nonredundant
Region
f2
0.5
1
f1
Figure 2.4: Real Signals Computation at Non-redundant Region.
2.3.1. Normalized bispectral entropy (Ent1):
Ent1   pn log pn where pn 
n

B f1 , f 2 

B f1 , f 2 
(3)
2.3.2. Normalized bispectral squared entropy (Ent2):
Ent 2   qn log qn where q n 
n
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B  f1 , f 2 


2
B  f1 , f 2 
2
(4)
21
2.3.3. Normalized bispectral cubed entropy (Ent3):
Ent 3   rn log rn where rn 
n
B f1 , f 2 


3
B f1 , f 2 
3
(5)
2.3.4. Bispectrum phase entropy (EntPh):
EntPh  n pn log pn
(6)
1
 1bf1 , f 2  n
L 
where n   /    2n / N      2 (n  1) / N , n  0,1, N  1
where pn 
 : Bispectrum phase angle
L: Number of points within the samples in Figure 2.2.
2.3.5. Mean bispectrum magnitude: (mAmp)
mAmp 
1
  B f1 , f 2
L
(7)
where B f1 , f 2  is the bispectrum of the signal
L: Number of points within the samples in Figure 2.2.
2.3.6. Weighted Center of Bispectrum ( wc1  wc )








wcx 
wc1
wc3

iB (i, j )

B(i, j )

iB (i, j )

B(i, j )

iB (i, j )

B(i, j )
, wcy   
, wc2
, wc4







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iB (i, j )

B(i, j )

iB (i, j )

B(i, j )

iB (i, j )

B(i, j )
hence,
(8)
and
respectively
22
2.3.7. Moments of Bispectrum ( H1  H 5 )
The summation of the logarithmic amplitudes of H1 bispectrum is equated as:
H1    log  B f1 , f 2  
(9)
The summation of the logarithmic amplitudes of the diagonal elements in the H2
bispectrum is equated as:
H 2    log  B f k , f k  
(10)
The first order spectral moment of amplitudes of diagonal elements of the bispectrum H3
is equated as:
H 3    log  B f k , f k  
(11)
All the above features are again defined over the principal domain  in figure 2.4.
2.4.
ANOVA Test
ANOVA is defined as analysis of variance. It is a set of statistical models that
provide a test to ensure the mean values of the three groups are different. This test
translates the observed variance within the groups to be different sources of variation and
consider the number of subjects found in the groups
[6]
. In order to be statistical
significant; the observed differences should be high. Hence, all the 14 featured data
extracted by HOS analysis were categorized into Group A, B and C in the ANOVA test
platform. They were noted as normal, background and epilepsy respectively. The input
parameters that undergo ANOVA test are known as bispectral entrophy (Ent1), bispectral
squared entrophy (Ent2), bispectral cubed entrophy (Ent2) bispectrum phase entropy
(EntPh), mean bispectrum magnitude (mAmp), weighted center of bispectrum (wc1, wc2,
wc3 and wc4) and moments of bispectrum (H1-H5). An advantage of using ANOVA over
T- test is that it decreased the chance of committing error in false positive when there is
more than two means.
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2.5.
Types of Classifiers
Four classifiers known as Fuzzy Sugeno Classifier, Probabilistic Neural Network
(PNN), K-Nearest Neighbor (KNN) and Decision tree are used in this work. They are
briefly introduced as below.
2.5.1. Fuzzy Sugeno Classifier
Takagi-Sugeno classifier uses If-Then rule for system identification
[22]
. This
fuzzy controller do not derive classification rule from human operator experiences but
mathematical model. It has demonstrated the benefits of representing linear industrial
system.
2.5.2. Probabilistic Neural Network (PNN)
PNN is a feed forward network consists of three layers, namely input layers,
hidden layers and output layers. It is derived from Bayes Theorem. Donald Specht
replaced sigmoid activation function with a derived statistical activation function [19]. The
main advantage of using PNN is the computational speed where it can be used on realtime classification comparing to typical back propagation neural networks. Moreover, it
has the flexibility of choosing between linear decision probability density function (PDF)
and complex nonlinear PDF.
2.5.3. K-Nearest Neighbor (KNN)
KNN is a memory based learning technique used to classify samples based on the
closest training samples exist in the n-dimensional space. It does not perform comparison
with trained models for classification. A user defined constant, k is used to determine the
test samples belong to which class based on majority votes. Typically, k is a small
positive integer value.
2.5.4. Decision Tree (DT)
DT is a simplified model to predict the outcome base on the inputs. The inputs
formed the tree structure. DT can learn through recursive partitioning method. The
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learning phase is considered complete when all partition nodes have the same value as the
target variables.
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CHAPTER THREE
________________________________________________________________________
RESULTS
The values of the 14 HOS features were processed by their respective MATLAB
program and shown in the following tables.
3.1.
HOS Features
P-value is the gauge used to identify statistical significant differences between
mean values of different groups.
Ent1
Ent2
Ent 3
EntPh
mAmp
Features
Normal
Background
Epileptic
Mean:
0.6920 ±
0.5365 ±
0.6151 ±
4.66 x 10-2
6.66 x 10-2
7.91 x 10-2
0.4780 ±
0.2814 ±
0.3581 ±
9.35 x 10-2
8.57 x 10-2
0.146
0.3402 ±
0.2146 ±
0.2380 ±
0.107
0.334
0.136
3.557 ±
3.5400 ±
3.5170 ±
2.94 x 10-2
5.20 x 10-2
4.96 x 10-2
1.032 x 108 ±
7.313 x 107 ±
2.996 x 1010 ±
8.110 x 107
9.809 x 107
3.659 x 1010
Std Deviation:
Mean:
Std Deviation:
Mean:
Std Deviation:
Mean:
Std Deviation:
Mean:
Std Deviation:
P-value
< 0.0001
< 0.0001
0.0001
< 0.0001
< 0.0001
Table 3.1: Bispectral Entrophy (Ent1), Bispectral Squared Entrophy (Ent2), Bispectral Cubed
Entrophy (Ent3), Bispectrum Phase Entrophy (EntPh), Mean Bispectrum Magnitude
(mAmp).
In Table 3.1, four out of five HOS features have P-value less than 0.0001 that is
obtained using ANOVA test. Hence Ent3 is not being used, as it is not able to show
significant difference between normal EEG and epileptic compared to the rest. Ent1,
Ent2, EntPh and mAmp showed higher value for Normal EEG signals compared to
epileptic EEG signals. One particular HOS feature showed an increase for epileptic signal
that is mAmp mean value while there is no major difference between normal, background
and epileptic for EntPh feature mean value. Table 3.2 showed bispectrum moments
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namely H1, H2, H3 H4 and H5 are not qualified for usage as HOS features for EEG
identification. All five features will be excluded from this study.
Features
H1
H2
H3
Normal
Background
Epileptic
P-value
Too few data
Too few data
Too few data
N. A
7.800 x 1016 ±
5.937 x 1010 ±
1.588 x 1011 ±
6.316 x 1017
1.966 x 1010
4.986 x 1010
4.390 x 1018 ±
3.548 x 1012 ±
2.237 x 1015 ±
3.177 x 1019
5.968 x 1012
2.945 x 1015
Mean:
Std Deviation:
Mean:
Std Deviation:
Mean:
Std Deviation:
Mean:
H4
Std Deviation:
Mean:
H5
Std Deviation:
0.22
0.15
Table 3.2: Bispectrum Moments Represented by H1 to H5.
Features
Mean:
Normal
Background
Epileptic
20.1570 ±
6.7764 ±
14.3380 ±
P-value
< 0.0001
wc1
Std Deviation:
Mean:
15.8
4.3
3.79
11.5610 ±
3.6135 ±
7.5816 ±
< 0.0001
wc2
Std Deviation:
Mean:
11.8
4.22
2.49
18.5860 ±
9.9546 ±
15.068±
< 0.0001
wc3
Std Deviation:
Mean:
2.02
2.33
3.2
8.2306 ±
4.2636 ±
7.6444 ±
< 0.0001
wc4
Std Deviation:
0.882
0.762
1.7
Table 3.3: Bispectrum Weighted Center Represented by wc1 to wc4.
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Bispectrum weighted center (wc) features will be used, as the P-value is less than
0.0001. Similar observation can be made all four wc features mean value is higher for
normal people compared to those who has seizures attack. Table 3.4 showed the
graphical representation of the selected HOS features that coincides with table 3.1, 3.2
and 3.3 values.
Group Mean with 95% Confidence Interval
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Table 3.4: ANOVA Test Perform on 3 Classes of EEG Data with Selected Features.
3.2.
Classifiers Performance Parameters
Classifiers are trained to identify normal, pre-ictal and epilepsy data. Selected
HOS features will be fed into classifiers during learning phase as the inputs together with
the corresponding outputs. This is to let the classifiers to understand the interactions
between the inputs and outputs. Hence, all three classes of data will be used to train the
four classifiers namely, Fuzzy Sugeno classifier, PNN classifier, KNN classifier and DT
classifier. The four classifiers will be tested using known test data input to verify whether
the classifiers can differentiate between normal, pre-ictal and epilepsy states.
The performance of the classifiers for three classes identification when being fed
by unknown input data is being judged by four performance parameters. They are
accuracy, Positive Predictive Value (PPV), sensitivity and specificity. Accuracy is the
ratio used to calculate the correctly identified samples with respect to the total number of
samples used. PPV is the probability of a patient that is having a real seizure attack when
the classifier has identify this patient is having the attack. Sensitivity is the likelihood the
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test provides a positive result with respect to the detection of abnormal features.
Specificity is the likelihood that provides negative result when normal features are
detected. The higher the values for these four performance indicators show higher
chances of correct prediction of unknown data sets.
The four performance indicators are derived from four other parameters that are
True Negative (TN), False Negative (FN), True Positive (TP) and False Positive (FP). In
this study, positive implies seizure attack while negative implies normal. True Positive
represents the number of seizure people whom the test result is positive while False
Negative represents the number of seizure people whom the test result is negative. True
Negative indicates the number of normal people having negative test results while False
Positive indicates the number of normal people having positive test results.
Table 3.5: Prediction Table.
Positive case (P) is defined as the sum of TP and FN while Negative case (N) is
defined as the sum of TN and FP, i.e. P= TP+FN and N=TN+FP. Accuracy is the sum of
TP and TN divided by total samples used. PPV is TP divided by sum of TP and FP.
Sensitivity is the ratio of TP to P while specificity is the ratio of TN to N.
3.3.
Classifiers Performance Outcome
Classifiers TN FN TP FP
FUZZY
PNN
KNN
DT
28
28
28
28
1
1
1
2
59
59
59
58
2
2
2
2
Accuracy PPV Sensitivity Specificity
(%)
(%)
(%)
(%)
96.7
96.7
98.3
93.3
96.7
96.7
98.3
93.3
96.7
96.7
98.3
93.3
95.5
96.7
96.7
93.3
Table 3.6: Result of TN,FN,TP,FP,Accuracy,PPV,Sensitivity,Specificity for Various Classifiers.
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100 segments for each normal, pre-ictal and seizure data are divided into three
equal segments to perform cross validation. One third of the data is used as testing data
while the two third data is used to train. In another word, there are 63 sets of training data
and 27 sets of testing data. The study utilizes Fast Fourier Transform (FFT) for the
efficient computational advantage. Block of 256 samples, equivalent to 1.5 seconds at the
defined sampling rate was used for bispectrum and bicoherence computation. The
bispectrum was calculated using indirect FFT while bicoherence calculated using direct
FFT method. The default settings of 50% overlapping between blocks in Spectral
analysis toolbox
[21]
were used. Equation (1) is used for indirect FFT calculation while
equation (2) is used for direct FFT calculation.
As shown in Table 3.5, the maximum accuracy of 96.7% is demonstrated by
Fuzzy, PNN and KNN classifiers while the minimum accuracy of 95.5 is demonstrated
by DT classifier. The four classifiers showed same PPV value of 96.7% and specificity
value of 93.3%. Sensitivity value of 98.3% is demonstrated by Fuzzy, PNN and KNN
classifier together with DT classifier showing the lowest sensitivity value of 96.7%.
Combing through the entire four classifiers performance index, DT has proven to
be the worst classifier while the other three classifiers has identical performance.
Bispectrum and bicoherence for 3 states can be found from Figure 2.5 to Figure 2.7. For
normal EEG bispectrum contour plot, we can see there is 1 center plot at zero for both f1
and f2 with 6 plots surrounded the center forming an oval shape. This can be further
confirmed from bispectrum plot where a star shape can be observed. A hexagon plot can
be seen on both bispectrum and bicoherence plots of pre-ictal signal. Unlike normal and
pre-ictal plots, Seizure bispectrum and bicoherence plots do not have center plots but
only consist of spread out plots.
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Figure 2.5(a): Normal EEG Bispectrum Magnitude Contour Plot.
Figure 2.5(b): Normal EEG Bispectrum Magnitude Colour Map Plot.
Figure 2.5(c): Normal EEG Bicoherence Plot.
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Figure 2.6(a): Background EEG Bispectrum Magnitude Contour Plot.
Figure 2.6(b): Background EEG Bispectrum Magnitude Colour Map Plot.
Figure 2.6(c): Background EEG Bicoherence Plot.
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Figure 2.7(a): Seizure EEG Bispectrum Magnitude Contour Plot.
Figure 2.7(b): Seizure EEG Bispectrum Magnitude Colour Map Plot.
Figure 2.7(c): Seizure EEG Bicoherence Plot.
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DISCUSSION
The result of the extracted features Ent1, Ent2, EntPh signify the complication or
abnormality of EEG signal that occurs during epilepsy is reduced. Moreover, the values
obtained from the measurement for normal and epileptic EEG signals are distinctive.
Table 3.1 also shows that the statistical analysis of these measurements is extremely
statistically significant with p<0.0001.
In this project, the HOS features analyze the 3 classes of EEG signals (Normal,
Background, and Epilepsy). The results indicate that the variables decrease progressively
from the normal stage to epilepsy stage. These results also signify an outcome that the
brain has lessened the electrophysiological behavior when seizure occurs.
The brain has twelve paired of cranial nerves. The first two pair of cranial nerves comes
directly from the cerebrum while the remaining comes from the brainstem. The vagus
nerve is the essential pathway between the brain and some organs (head, neck, chest and
abdomen) [24]. Majority of the nerve fibers are sensorial nerves that transmit the electrical
impulses from the organ to the brain. Hence, occurrence of epilepsy interrupts the brain
activity.
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CHAPTER FOUR
________________________________________________________________________
SUMMARY
Epilepsy caused danger to life when the seizure occurred during swimming or
walking up the staircase i.e. the person will get drowned or falls from stairs. This has
motivated the studies of various classifiers to have an early detection of seizure attack.
EEG is used to detect the brain signal as brain signals display different pattern compared
to normal brain signal. Due to the random and non-linear characteristic of brain signals,
statistical analysis is being employed. 14 distinct EEG signals features are being
extracted using Higher Order Spectra (HOS) method. The features are being tested using
ANOVA method to choose the statistical significant features to be fed to various
classifiers. Eight features which have p-value less than 0.0001 are chosen. Four
classifiers namely, Fuzzy Sugeno, Probabilistic Neural Networks (PNN), K-Nearest
Neighbor (KNN) and Decision Tree (DT) are being tested against four performance
parameters. Fuzzy, PNN and KNN showed comparable performance. It is proven the
entropies estimators, phase entropy and weighted centers can be used to identify epilepsy.
CONCLUSION
EEG signal obtained from human brain provided neurological behavior
information. Various researches have been done to dissect the EEG signal to understand
the difference between normal EEG, background EEG and epilepsy EEG signals. This
study has shown that nonlinear time series analytical methods like entropies, entropies
phase and weight centers can be used to distinguish between normal EEG and epilepsy
EEG. It is clear to observe that entropies and weight centers are high for normal EEG
compared to epileptic EEG. Through this work, HOS is proven to deliver significant
features to the classifiers.
RECOMMENDATION
For future work, more advance identification algorithm can be used such as fuzzy
neural networks to perform classification. Encouraging results can be found from stocks
BME499 CAPSTONE PROJECT FINAL REPORT
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trading applications
[2]
, traffic prediction application
[16]
and bank failure prediction
[15]
.
Moreover, it can be use for diagnose epilepsy at a very early stage. It can be implemented
real time for diagnosing the Pre-ictal EEG Signal. It can also be further extend to other
clinical diseases such as depression, schizophrenia, and stroke patients and for different
cardiac vascular diseases. This concept can also diagnose the efficacy of the drug; to
check if the drug is efficient to cure epilepsy.
Besides that, the classifiers need to be tested using data that is not included in
training data sets to verify the performance of the classifiers against unknown variables.
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CHAPTER FIVE
PROJECT MANAGEMENT
Table 3.7: Gantt Chart Phase 1
Table 3.8: Gantt Chart Phase 2
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Table 3.9: Gantt Chart Phase 3
Table 4.0: Gantt Chart Phase 4
Table 3.7, 3.8, 3.9 and 4.0 shows my project development progress. Phase 1
shown in Table 3.7 is mainly information gathering and research journals exploration. A
technique that I have learnt from one of my U-core module was mind-mapping technique.
This technique helps me to brainstorm all the important information that is required by
my project. From the key elements I have brainstormed. I add in the details to my mind
map to understand what I have read and understand from my research sources, ranging
from books, online materials and journal papers.
Phase 2 in Table 3.8 describes the tutorial practices and hands on that I have
started on MATLAB application. The yellow legend in Phase 2 table represent there is a
lag of 3 days during the period of drafting Interim report. I was unwell due to fever and
BME499 CAPSTONE PROJECT FINAL REPORT
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headache. Moreover, 10 days were set aside for the completion of my examination
revision for BME 219 Healthcare Administration and BME 217 Healthcare Standard and
Regulation module. Hence, I have a lag in my schedule but I compensate it back by
setting aside 43 days for Literature Review Part 2.
Phase 3 of Table 3.9 reflects the initial programming development for Discrete
Wavelet Transform (DWT) features extraction. The programming is based on the
mathematical equations derived from the journal papers; relevant equations were
computed and programmed by MATLAB application. However, the 6th meeting with my
supervisor on 11th Feb 11 has confirm that the result of DWT method has shown an
insignificant p-value and we are unable to classify these sets of data and hence decided to
try HOS features extraction instead as it has many advantages discussed in some journals.
The try was indeed worthy, after editing and debug the MATLAB program for HOS
(Appendix B). The results of the extracted features were excellent after running through
ANOVA test. From there, I proceed to the next stage of performing classification for
automatic detection of Epilepsy. Unfortunately, my joy was temporary. My father was
admitted to A&E in SGH due to his sudden irregular heartbeat. He was hospitalized for 5
days due to further diagnosis of infection in his stomach. I have to visit him, check with
the doctor about his condition and etc. Hence, I have to stay up late and catch up with my
loss time in meeting my objectives.
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CHAPTER SIX
________________________________________________________________________
REFLECTION
The 1st meeting with Dr Rajendra was discussing how to start off the project.
Though I have no idea what MATLAB programming skills is all about and the
knowledge in Signal processing is limited. I embraced the new learning opportunity. The
initial title proposed was “Computer-Based Detection of Epileptic EEG Signals using
Recurrent Quantification Analysis Parameters”. The discussion was an ambitious attempt
to try different processing techniques such as Discrete Wavelet Transformation,
Recurrent Quantification Analysis, Non-Linear Analysis and Higher Order Spectra
Analysis. So the consent from my supervisor was to submit a new project proposal title
known as “Early Detection of Epilepsy: Comparative Study”.
The progression of the entire project was challenging. From the start to read up a thick
stack of journal papers, book and identifying all the techniques used by the researchers
was demanding. The discipline to following exactly to the Gantt chart schedules and
learning a whole new programming skill called MATLAB was tough. My first task in the
Gantt chart was to gather all the information and literature reviews related to epilepsy,
seizure, EEG and its statistical results. This helps to set my basic fundamentals of the
project. Everything starts off well at phase 1 & 2, until early March when I start to run the
MATLAB program on features extraction and various classifiers at phase 3. The program
needs to be debugged and process was discouraging. Moreover, my dad was diagnosed
with irregular heartbeat due to infection and hospitalized during this period. It was the
most stressful period, as I had to visit him, send him for many post hospitalization
follow-ups and my health was affected. Most of the time is also spent on MATLAB
tutorial practices, reading books on programming codes and online resources. The time
management between family and project progress was demanding. However, I was
fortunate that Dr Rajendra has been constantly encouraging and pushes me throughout
the project development. The debugging of the program was tedious and delays the Gantt
BME499 CAPSTONE PROJECT FINAL REPORT
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schedules due to lack off knowledge in calling some of the functions to perform what
needs to be done. Fortunately Alvin advice me and straightened my thoughts but the joy
was temporary. The extracted features are required to be tested. Hence I need to explore
and select a significant test. ANOVA meant for more than 2 groups of variance was
chosen. The features extracted from the Discrete Wavelet Transform (DWT) technique
were then tested. The p-value was insignificant and result did not turn out well.
Therefore, another processing method known as HOS was selected due to its few
advantages mentioned in a few journals. The MATLAB program then has to be edited
and the process has to be redone. Such inevitable circumstances require me to speed up
my pace and compensate the lost time according to the Gantt chart. Fortunately, majority
of the HOS features tested has shown good results of p-values less than 0.0001 and it is
ready to be classified. The sets of training and testing of the classifiers is also confusing.
However, it is much easier to understand with some help providing by the supervisor.
I am glad and happy to give up my engineering job to pursue my interest in biomedical
field. It is worth to exchange for a four years biomedical education that provided me with
great insights. The opportunity to witness the passion of researchers is motivating. One of
the examples is my supervisor, no matter how busy or tired he is. He has never fails to be
positive and always trying to improve to excel his researches to increase the quality of
life. Through him, I have learnt that building a system through the integration of
mathematics, biology and science can helps in early detection of epilepsy. Though the
disease is not life threatening but it provides awareness for the needy and allows them to
seek early treatments. Only then, I understand and appreciate the purpose of capstone
project. Moreover, the challenges I have encountered along my way, moulds my
perseverance, while the new experiences strengthen my weakness.
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REFERENCE
[1] Anatomical Description of the Human Head
http://alexandria.tue.nl/repository/books/642185.pdf
[2] Ang, K.K., Quek, C. (2006). Stock Trading Using RSPOP: A novel rough set neurofuzzy approach. IEEE Transactions on Neural Networks, in press.
[3] A Teen’s Guide to Epilepsy
http://www.epilepsy.ca/eng/content/teens.html
[4] Babloyantz, A. (1986). Evidence of chaotic dynamics during sleep cycle. In
Dimensions and entropies in Chaotic system, ed. G.Mayer-Kress, Springer-Verlag,
Berlin.
[5] Beatty, Jackson. (2001).The Human Brain: Essential of Behavioral Neuroscience.
Thousand Oaks,California; London: Sage Publications, Inc.
[6] Chua, K. C., Chandra, V., U, Rajendra, Acharya., Lim, C.M. (2009). Analysis of
epileptic EEG signals using higher order spectra. J Med Eng Technology; 33(1):4250.
[7] Complex Partial Seizure
http://en.wikipedia.org/wiki/Complex_partial_seizure
[8] Eric R.Kandel, James H.Schwartz, & Thomas M.Jessell. 4th Edition (2000).
Stamford, Conn.: Appleton & Lange, McGraw-Hill
[9] Gan Eveline (2009). Epilepsy: Everyday is a struggle. p.23. Retrieved from
http://www.channelnewsasia.com/stories/specialreport/news/446692_96/1/.html
[10] Identifying Neurotransmitters
http://en.wikipedia.org/wiki/Neurotransmitter#Identifying_neurotransmitters
[11] Kannathal, N., Acharya, U. R., Joseph, P., and Ng, E. Y.K. (2006). Analysis of EEG
signals with and without reflexology using FFT and auto regressive modelling
techniques. J. Chin. Clin. Med. 1(1):12-20.
[12] Lu, H., Wang, M., and Yu, H. (2005).EEG Model and Location in Brain when
Enjoying Music. Proceedings of the 27th Annual IEEE Engineering in Medicine and
Biology Conference Shanghai: China. 2695-2698.
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[13] Nikias, C.L., Mendel, J.M. (1993). Signal Processing with Higher Order Spectra.
IEEE signal processing magazine. 10-37
[14] Nikias, C.L., Rughuveer, M.R. (1987). Bispectrum estimation: A digital signal
processing framework. Proc. IEEE. 75. 869-890
[15] Ng, G.S., Quek, C., Jiang, H. (2008). FCMAC-EWS: A bank failure early warning
system based on a novel localized pattern learning and semantically associative fuzzy
neural network. Expert Systems with Application, 34(2):989-1003.
[16] Quek, C., Pasquier, M., Lim, B. (2006). POP-TRAFFIC: A Novel Fuzzy Neural
Approach to Road Traffic Analysis and Prediction. IEEE Transactions on Intelligent
Transportation Systems, 7(2), 133-146.
[17] Schater, S.C. (1998).The Brainstorms companion:epilepsy in our view. New York:
Raven Press; 994. activity. Phys Rev Lett, 80. 5019-22.
[18] Singh, Anuradha. (2006). 100 questions & answers about epilepsy. Sudbury, Mass:
Jones and Bartlett Publishers
[19] Specht, Donald. F. (1990), Probabilistic Neural Networks. Pergamon Press pie on
Neural Networks, Vol. 3. 109 118.
[20] Subha, D. Puthankattil., Joseph, Paul. K., U, Rajendra. Acharya., Lim, C. M. (2010).
EEG Signal Analysis: A Survey. J Med System; 34: 195-212. doi:10.1007/s.10916008-9231.z
[21] Swami, A., Mendel, C. M., Nikias C.L. (2000).Higher-Order Spectral Analysis
(HOSA) Toolbox, version 2.0.3.
[22] Tomohiro, Takagi., Michio, Sugeno. (1985). Fuzzy identification of systems and its
applications to modeling and control. IEEE Transactions on systems, man, and
cybernetics, Vol. SMC-15. 116-132.
[23] Types of Seizures
http://www.epilepsyfoundation.org/about/types/types/index.cfm
[24] Vagus Nerve Anatomy
http://en.wikipedia.org/wiki/Vagus_nerve
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APPENDIX A- DATA OF HOS FEATURES
________________________________________________________________________

HOS FEATURES FOR NORMAL EEG SIGNAL
Normal
Parameter 1
Parameter 2
Parameter 3
Parameter 4
Parameter 5
Parameter 6
Parameter 7
Parameter 8
Parameter 9
Parameter 10
Parameter 11
Parameter 12
Parameter 13
Parameter 14
Parameter 15
Parameter 16
Parameter 17
Parameter 18
Parameter 19
Parameter 20
Parameter 21
Parameter 22
Parameter 23
Parameter 24
Parameter 25
Parameter 26
Parameter 27
Parameter 28
Parameter 29
Parameter 30
Parameter 31
Parameter 32
Parameter 33
Parameter 34
Parameter 35
Parameter 36
Parameter 37
Parameter 38
Parameter 39
Parameter 40
Parameter 41
Parameter 42
Parameter 43
Parameter 44
Parameter 45
ent1
0.6653
0.6608
0.6798
0.6678
0.655
0.6935
0.5813
0.6806
0.608
0.6423
0.6404
0.7131
0.6801
0.6341
0.6335
0.7109
0.6505
0.7342
0.616
0.6343
0.6663
0.6425
0.6628
0.6267
0.6462
0.6593
0.6296
0.6969
0.7
0.6629
0.6892
0.7342
0.7503
0.7053
0.6366
0.6839
0.6479
0.6458
0.6409
0.6522
0.6439
0.6979
0.7029
0.6848
0.6065
ent2
0.4215
0.4324
0.4773
0.4299
0.358
0.4313
0.2701
0.4642
0.1831
0.3619
0.3866
0.5178
0.3821
0.3014
0.263
0.5015
0.4146
0.5586
0.4113
0.411
0.4943
0.4108
0.4836
0.3917
0.4117
0.425
0.4242
0.4854
0.5305
0.4711
0.5304
0.5801
0.605
0.4729
0.399
0.4302
0.4092
0.3691
0.3244
0.3514
0.4245
0.4468
0.531
0.4784
0.3391
entPh
3.578
3.5555
3.5725
3.5535
3.5562
3.5683
3.5595
3.5655
3.5077
3.5531
3.4908
3.5695
3.5269
3.4957
3.5241
3.5648
3.5451
3.5688
3.5531
3.5725
3.5576
3.5359
3.5691
3.4238
3.46
3.5737
3.5627
3.5648
3.5781
3.5713
3.5699
3.57
3.5327
3.5608
3.5773
3.573
3.5745
3.5379
3.5752
3.433
3.5725
3.5739
3.571
3.5624
3.5333
BME499 CAPSTONE PROJECT FINAL REPORT
mAmp
4.70E+07
4.03E+07
6.58E+07
1.07E+08
6.19E+07
5.87E+07
1.92E+08
5.12E+07
8.24E+07
2.09E+08
3.15E+08
4.57E+07
6.60E+07
2.85E+08
2.59E+08
3.21E+07
3.02E+08
5.08E+07
1.63E+08
7.95E+07
8.86E+07
1.37E+08
3.93E+07
1.13E+08
9.07E+07
3.63E+07
1.15E+08
4.37E+07
2.63E+07
7.35E+07
3.37E+07
3.16E+07
5.23E+07
9.23E+07
2.31E+08
3.64E+07
2.52E+08
2.74E+08
1.98E+08
7.70E+07
2.51E+08
1.31E+07
2.45E+07
2.27E+07
1.41E+08
wc1
2.8332
21.3827
9.7713
18.4626
17.8593
15.7782
4.8067
17.5401
8.8682
17.9730
15.5959
18.8289
16.8770
16.9071
18.3373
46.0330
17.3862
21.9356
14.8524
13.7613
10.4685
15.0840
16.8269
15.8571
17.5897
14.6409
15.2483
8.3179
15.4465
13.4217
12.6346
16.5157
13.5326
18.1181
17.3412
26.7603
14.5753
16.6411
17.5938
21.1048
15.2316
15.6043
13.5467
17.9208
15.0471
wc2
0.6906
6.0413
6.8316
9.7674
14.2923
5.3716
1.3068
5.6585
2.8000
11.1968
7.5284
14.4457
7.1350
9.1428
13.5913
18.3463
11.1741
13.4507
9.2184
8.1187
7.8722
9.7279
3.7026
6.9554
9.6410
6.1277
8.0083
7.5046
23.1345
9.1778
5.4312
6.6971
3.7490
7.2880
12.1068
23.5293
9.7142
11.1976
14.3618
14.1540
6.3854
9.2320
18.9832
8.0277
8.9085
wc3
15.5231
15.8907
16.2404
16.9162
16.8503
18.0735
12.7004
16.6724
14.2485
17.9727
17.2576
19.0094
19.0857
18.0764
18.3501
18.5916
17.3473
20.8987
15.5548
16.4189
16.2010
16.0266
17.0030
16.6497
16.7875
17.4816
16.6984
17.4584
17.6253
16.3127
16.4169
19.3043
22.1889
18.5981
17.7910
18.0002
17.6628
17.8553
18.5156
19.3273
17.0967
18.5517
18.6249
18.3082
17.1118
wc4
6.2188
6.3208
7.0002
7.0920
7.7740
7.2605
4.7074
6.7162
5.3367
9.0538
8.1111
9.0944
8.5385
9.0066
10.1124
7.7059
9.0373
9.2842
7.8068
7.8939
7.2173
8.2549
7.9221
7.3350
7.7806
8.8639
8.0815
7.3732
7.7188
7.8265
7.3206
8.5446
8.7204
7.7472
8.4365
8.2910
7.9670
8.9386
9.7761
8.7243
8.4090
9.1325
9.1714
7.9682
8.2457
46
Parameter 46
Parameter 47
Parameter 48
Parameter 49
Parameter 50
Parameter 51
Parameter 52
Parameter 53
Parameter 54
Parameter 55
Parameter 56
Parameter 57
Parameter 58
Parameter 59
Parameter 60
Parameter 61
Parameter 62
Parameter 63
Parameter 64
Parameter 65
Parameter 66
Parameter 67
Parameter 68
Parameter 69
Parameter 70
Parameter 71
Parameter 72
Parameter 73
Parameter 74
Parameter 75
Parameter 76
Parameter 77
Parameter 78
Parameter 79
Parameter 80
Parameter 81
Parameter 82
Parameter 83
Parameter 84
Parameter 85
Parameter 86
Parameter 87
Parameter 88
Parameter 89
Parameter 90
Parameter 91
Parameter 92
Parameter 93
Parameter 94
Parameter 95
Parameter 96
Parameter 97
0.6331
0.7301
0.6545
0.6763
0.6775
0.7049
0.6863
0.621
0.5948
0.7033
0.6108
0.674
0.7468
0.7543
0.7357
0.7275
0.7487
0.7399
0.7156
0.7663
0.7877
0.7246
0.7179
0.7248
0.7486
0.7561
0.7373
0.7261
0.7585
0.753
0.7146
0.7413
0.7508
0.7649
0.7266
0.7246
0.7277
0.7628
0.759
0.7388
0.714
0.7519
0.7131
0.7435
0.7382
0.713
0.7073
0.7067
0.6519
0.6488
0.6956
0.6893
0.3886
0.5831
0.4696
0.4481
0.4697
0.484
0.4699
0.378
0.3139
0.5407
0.3324
0.5055
0.5899
0.5793
0.5797
0.569
0.6062
0.592
0.581
0.5504
0.582
0.5852
0.5747
0.5564
0.5931
0.5763
0.4902
0.527
0.6226
0.5877
0.5667
0.5316
0.3458
0.5498
0.5455
0.5251
0.5173
0.6386
0.5869
0.5712
0.5242
0.532
0.5329
0.6071
0.5863
0.5063
0.4943
0.5282
0.3745
0.3403
0.5238
0.4741
3.527
3.5719
3.4952
3.5603
3.5083
3.5747
3.5391
3.5249
3.5618
3.5771
3.5449
3.571
3.5716
3.5756
3.5781
3.5772
3.5763
3.576
3.5757
3.5709
3.5681
3.5766
3.5559
3.5768
3.5771
3.5748
3.5717
3.574
3.5743
3.5725
3.5725
3.5759
3.5711
3.564
3.5791
3.5254
3.5587
3.576
3.5777
3.5734
3.5767
3.5709
3.5677
3.5761
3.578
3.5738
3.5659
3.5704
3.5667
3.5415
3.571
3.5511
BME499 CAPSTONE PROJECT FINAL REPORT
9.65E+07
2.69E+07
7.05E+07
3.26E+07
2.23E+07
1.19E+07
2.12E+07
7.99E+07
9.30E+07
2.30E+07
9.18E+07
2.09E+07
6.17E+07
5.18E+07
1.04E+08
8.18E+07
6.47E+07
9.70E+07
1.75E+08
2.17E+08
9.31E+07
2.69E+08
1.28E+08
8.89E+07
5.56E+07
4.67E+07
1.78E+08
5.93E+07
5.28E+07
5.32E+07
1.33E+08
1.65E+08
9.54E+07
5.61E+07
1.79E+08
9.11E+07
6.71E+07
4.20E+07
3.77E+07
6.30E+07
9.46E+07
1.32E+08
1.53E+08
6.46E+07
4.67E+07
4.91E+07
8.98E+07
1.09E+08
3.50E+08
2.50E+08
6.29E+07
4.35E+07
16.8901
15.2001
13.2438
13.3322
11.6810
17.4159
19.8429
15.8875
17.1906
52.9503
16.2072
20.0572
123.2505
21.2825
17.8876
15.5575
20.3451
20.4632
18.1143
11.7983
20.9500
15.9903
7.5520
30.2541
48.1632
10.4162
17.6112
18.2658
32.6094
32.1391
15.3009
11.4079
12.3254
18.7462
18.4704
51.9833
29.0205
18.6890
41.2413
16.6170
15.0938
15.9059
18.0313
101.4129
20.5337
19.3481
22.7685
11.6882
19.9906
18.0131
18.3512
20.8720
10.2508
6.8565
9.5614
7.2937
7.9839
4.9490
12.9050
11.0295
16.0757
24.4431
11.9075
7.7290
113.8084
15.0838
9.1866
5.9266
9.3544
6.8841
16.5302
7.1260
14.6780
8.1147
6.2182
11.8759
12.1611
3.0299
5.8727
9.1470
20.7528
19.0333
8.7926
23.5874
7.0044
8.6811
11.3416
33.7283
18.7483
8.1883
15.0462
8.2777
4.5339
4.2902
8.5597
27.5777
9.4536
11.3173
4.9090
9.5947
15.4760
12.1881
13.7238
10.7103
16.6757
18.6859
15.8909
16.0869
17.3558
18.3866
18.5634
16.8386
16.6184
17.5016
16.6957
15.6774
20.6100
21.2670
19.7354
19.8498
21.3089
20.4939
19.2937
23.4303
23.9087
19.0209
19.0626
19.9871
20.6921
20.9938
21.3962
19.9919
21.6167
21.4254
19.3047
21.5077
21.6156
21.9082
19.9208
19.1494
20.3784
21.8762
21.2883
20.9196
19.2675
22.0813
19.6130
20.4775
20.6099
18.4354
18.7307
18.5574
18.5025
18.5780
18.6324
18.3159
8.3297
8.0323
7.2533
6.9275
7.5654
7.7502
8.7892
8.2603
8.7446
7.2313
9.0879
6.9945
8.6135
9.0075
8.5923
7.9278
9.1035
9.3660
8.6181
9.4118
9.3903
8.2530
7.6817
8.3800
8.8637
8.5944
8.4496
7.7288
9.3300
8.6747
8.4789
8.9225
8.2344
8.5229
8.7105
7.6931
7.7544
9.2435
8.3482
9.0078
8.3956
8.4734
7.9233
8.2583
7.9244
8.2975
8.5829
8.4351
9.4077
9.6567
9.3883
8.2240
47
Parameter 98
Parameter 99
Parameter100

0.7117
0.7154
0.6564
0.4811
0.5497
0.4054
3.5728
3.5178
3.5373
4.13E+07
9.37E+07
3.36E+08
12.4408
7.9596
16.1264
7.9796
10.3286
8.5258
18.6885
17.9616
17.9045
7.9141
8.0973
8.3778
wc3
9.6499
9.4298
9.1250
8.8416
14.2679
9.6003
8.2421
9.2440
10.3723
9.6047
8.4440
8.9473
7.0209
10.5704
12.1855
8.1321
13.6153
10.3405
7.5340
9.5041
8.6075
9.5085
10.8098
9.6834
8.4580
8.9744
10.1568
8.5045
9.0353
9.3795
8.4761
8.8760
6.9894
5.7782
8.1126
5.4437
10.9318
10.8340
11.2503
7.3092
7.2520
9.3347
9.5161
8.2277
wc4
4.5116
4.1667
4.1383
3.8357
5.4863
4.0207
3.5825
3.8129
4.4213
4.1940
3.6751
4.0438
3.4522
4.6448
4.9298
3.6143
5.5109
4.8755
3.4406
4.3030
3.8850
4.5205
4.1203
4.1053
4.0582
3.8004
4.5242
3.5373
3.8939
3.6847
4.0823
3.9129
3.2755
2.9029
3.7493
3.1117
4.2825
4.1597
4.5529
3.5926
3.1338
3.9465
4.0364
3.6905
HOS FEATURES FOR BACKGROUND SIGNAL
Background
Parameter 1
Parameter 2
Parameter 3
Parameter 4
Parameter 5
Parameter 6
Parameter 7
Parameter 8
Parameter 9
Parameter10
Parameter 11
Parameter 12
Parameter 13
Parameter 14
Parameter 15
Parameter 16
Parameter 17
Parameter 18
Parameter 19
Parameter 20
Parameter 21
Parameter 22
Parameter 23
Parameter 24
Parameter 25
Parameter 26
Parameter 27
Parameter 28
Parameter 29
Parameter 30
Parameter 31
Parameter 32
Parameter 33
Parameter 34
Parameter 35
Parameter 36
Parameter 37
Parameter 38
Parameter 39
Parameter 40
Parameter 41
Parameter 42
Parameter 43
Parameter 44
ent1
0.5468
0.5327
0.5353
0.5096
0.6458
0.5265
0.4723
0.5073
0.5581
0.5344
0.4804
0.5324
0.4398
0.575
0.5951
0.5063
0.6358
0.5709
0.4447
0.5216
0.5064
0.5485
0.5463
0.5388
0.5127
0.5094
0.563
0.4853
0.511
0.5164
0.5123
0.5155
0.4244
0.3842
0.5
0.3891
0.5717
0.546
0.5776
0.4728
0.4099
0.5293
0.5242
0.4933
ent2
0.3743
0.3506
0.2893
0.1799
0.2929
0.2177
0.1524
0.1725
0.3173
0.2843
0.1693
0.3577
0.1768
0.3486
0.2925
0.2505
0.371
0.422
0.1105
0.1598
0.2067
0.3844
0.2809
0.2564
0.3204
0.2774
0.3294
0.2041
0.2839
0.2521
0.3273
0.2586
0.1634
0.183
0.2688
0.2075
0.3368
0.2253
0.295
0.2562
0.1541
0.2397
0.1749
0.2352
entPh
3.4188
3.4136
3.5737
3.5377
3.5711
3.4181
3.5103
3.5776
3.5444
3.5504
3.5577
3.5274
3.5256
3.5109
3.5727
3.4316
3.5771
3.5688
3.5603
3.5548
3.5732
3.5688
3.5772
3.5436
3.5354
3.5596
3.5418
3.5743
3.4908
3.5491
3.4965
3.561
3.4
3.5589
3.5734
3.3999
3.4042
3.5774
3.5765
3.5633
3.5706
3.5773
3.5731
3.56
BME499 CAPSTONE PROJECT FINAL REPORT
mAmp
4.2741E+07
1.1543E+08
4.0812E+07
4.0043E+07
8.1497E+08
6.0707E+07
3.8241E+07
1.8159E+07
9.6380E+07
1.7963E+07
5.4297E+06
9.8529E+07
1.8146E+07
1.7932E+07
1.6481E+08
5.5040E+07
1.4282E+08
1.9169E+07
5.9306E+07
1.7541E+07
6.1440E+07
4.0881E+07
5.4076E+06
1.0093E+08
3.7136E+07
5.0234E+06
7.4553E+06
2.8529E+06
4.6518E+07
4.1450E+07
3.9142E+07
1.8628E+07
9.1594E+07
1.0967E+08
3.5248E+07
9.6484E+07
6.2860E+07
2.8226E+07
7.2284E+07
5.9149E+07
9.3448E+06
1.2938E+08
1.0312E+08
3.1871E+07
wc1
9.2870
5.5961
8.5404
8.3733
7.9379
5.2311
3.2793
5.0610
4.4080
7.3762
4.4826
6.7810
4.2050
8.4827
19.8877
7.4874
9.6089
9.2115
3.2421
2.0207
8.2159
7.6799
3.7535
7.5239
4.9563
3.4712
33.2129
5.2691
13.3349
2.1739
5.4320
5.5852
2.9595
3.8419
9.6766
3.8686
9.0340
5.0920
0.9425
2.5306
2.6609
5.8229
1.2560
2.8303
wc2
4.7006
2.5709
3.7322
3.2784
2.8344
2.1059
1.9365
2.3845
3.0199
3.5285
2.4952
3.0061
3.0094
3.8890
5.9832
3.2471
3.9548
5.4465
2.6294
2.1658
3.6541
4.9664
2.9434
3.2861
2.2445
2.7504
42.2619
2.4990
8.1384
1.3561
2.2192
3.2595
1.4072
1.4462
3.1261
2.4530
3.7041
2.3561
0.9324
2.7932
2.2206
3.1772
1.0661
2.2975
48
Parameter 45
Parameter 46
Parameter 47
Parameter 48
Parameter 49
Parameter 50
Parameter 51
Parameter 52
Parameter 53
Parameter 54
Parameter 55
Parameter 56
Parameter 57
Parameter 58
Parameter 59
Parameter 60
Parameter 61
Parameter 62
Parameter 63
Parameter 64
Parameter 65
Parameter 66
Parameter 67
Parameter 68
Parameter 69
Parameter 70
Parameter 71
Parameter 72
Parameter 73
Parameter 74
Parameter 75
Parameter 76
Parameter 77
Parameter 78
Parameter 79
Parameter 80
Parameter 81
Parameter 82
Parameter 83
Parameter 84
Parameter 85
Parameter 86
Parameter 87
Parameter 88
Parameter 89
Parameter 90
Parameter 91
Parameter 92
Parameter 93
Parameter 94
Parameter 95
0.5363
0.5296
0.552
0.7181
0.6172
0.5708
0.5407
0.7071
0.57
0.5323
0.5087
0.629
0.4918
0.5148
0.5772
0.5933
0.5815
0.5778
0.5485
0.5375
0.5098
0.37
0.5188
0.5647
0.5531
0.566
0.4851
0.5503
0.5364
0.5789
0.5282
0.4757
0.6884
0.5109
0.5591
0.5968
0.5294
0.5403
0.5593
0.5328
0.6778
0.6027
0.5401
0.5856
0.3583
0.5153
0.6691
0.6095
0.4941
0.5603
0.4374
0.3426
0.2823
0.2565
0.4934
0.4345
0.2316
0.3463
0.4383
0.3724
0.2737
0.2397
0.3662
0.1753
0.3323
0.3311
0.3655
0.3932
0.3015
0.3717
0.3616
0.2612
0.1656
0.2317
0.2269
0.3862
0.3821
0.1373
0.3835
0.1931
0.2718
0.2395
0.1485
0.4075
0.1646
0.3408
0.2362
0.292
0.1411
0.2969
0.3844
0.4228
0.4198
0.3078
0.408
0.1961
0.2374
0.4321
0.2971
0.246
0.353
0.1799
3.534
3.5612
3.5664
3.5792
3.5564
3.5764
3.5483
3.5688
3.5528
3.575
3.5258
3.5674
3.5703
3.5452
3.5648
3.5646
3.5683
3.5772
3.5505
3.4923
3.3446
3.5421
3.5572
3.5784
3.5732
3.5514
3.459
3.4111
3.5521
3.5735
3.5596
3.5557
3.5767
3.5425
3.5734
3.5792
3.5591
3.5409
3.5718
3.5661
3.5724
3.5674
3.5488
3.4973
3.5153
3.5772
3.5759
3.5776
3.5776
3.511
3.4222
BME499 CAPSTONE PROJECT FINAL REPORT
2.8410E+07
7.2857E+06
1.0945E+08
6.8257E+07
2.5279E+07
3.2298E+08
1.5242E+08
1.3030E+08
5.1372E+07
6.7940E+07
1.9700E+07
1.3729E+08
3.0609E+06
6.6544E+07
2.0766E+06
1.3938E+08
1.0890E+07
1.2651E+07
1.8421E+07
3.7888E+07
5.6376E+07
1.6022E+08
7.5889E+06
1.9476E+08
2.7990E+07
5.4312E+07
1.6315E+08
3.7898E+07
2.2566E+07
2.2800E+06
3.6587E+07
3.5310E+07
1.0255E+08
1.2393E+08
1.4008E+08
1.6500E+08
7.7775E+06
2.7035E+07
2.4714E+06
3.5777E+07
7.7736E+07
1.3986E+07
8.9422E+06
1.3407E+07
1.1961E+08
1.1448E+07
3.4595E+08
1.5303E+08
5.4246E+06
1.4669E+08
1.1453E+08
6.8207
4.3052
4.3130
5.3274
10.1876
4.6044
7.8051
16.1197
2.6014
10.7410
6.1253
8.7904
4.1694
7.3490
6.6254
11.0569
5.3032
6.8894
5.3966
7.4238
7.3443
4.9733
2.7269
3.9513
14.7574
8.0590
5.0033
5.7309
4.7317
5.3699
16.1172
3.4465
9.5079
3.5688
11.2017
4.7607
4.5290
11.0107
6.4037
6.2674
7.6353
15.3225
8.5089
9.5157
4.9953
3.7474
8.8138
3.3087
4.7212
8.1576
7.2696
4.1557
2.4613
3.4432
3.8498
3.9698
2.7844
2.9172
7.6666
2.0670
3.7373
1.7094
5.2376
2.4997
3.2714
3.2405
3.5105
2.6391
3.6015
2.1383
4.6494
3.1613
2.7159
2.0442
1.7113
3.8225
3.8383
2.2784
4.1541
3.0238
2.3830
7.1657
2.7939
3.5983
2.3458
4.3227
3.0678
2.9149
3.4120
3.5612
2.9988
3.1266
13.5702
0.9529
2.0852
2.5082
2.2110
3.6178
3.2862
3.2081
4.1824
2.9982
9.2060
9.9808
10.1749
17.7535
12.1791
12.3004
9.2666
17.4912
10.7087
9.8253
8.8923
13.6167
8.6586
8.4308
11.4157
11.3208
10.9091
11.0871
9.6277
9.1868
8.2719
5.5298
9.5674
11.1230
9.3111
9.9659
9.7690
9.6731
9.6654
11.7709
9.4028
8.1096
16.6898
10.1015
10.1253
12.4509
9.5519
10.7712
10.6802
8.7105
15.5381
11.2970
9.4912
10.8757
5.1798
8.9847
14.9629
13.2082
8.6135
10.3154
7.0076
4.4801
3.9379
4.5110
7.1832
5.1863
4.4344
4.2259
6.6890
4.3540
3.8648
4.1271
5.2929
3.6267
3.6824
4.4808
4.7714
4.7956
4.5591
4.5381
4.4300
3.6104
3.0495
4.0350
4.5854
4.2587
4.6043
3.8129
4.6188
4.1948
4.5701
4.3418
3.6721
6.3137
3.9642
4.2593
4.7882
4.0195
4.3139
4.3521
4.2156
6.1104
5.1504
4.1982
4.7507
2.8758
3.7612
6.0013
4.8951
3.7181
4.3896
3.2985
49
Parameter 96
Parameter 97
Parameter 98
Parameter 99
Parameter 100

0.6527
0.5045
0.5907
0.3968
0.5075
0.2617
0.2145
0.3331
0.1602
0.2567
3.5679
3.5742
3.5746
3.4409
3.54
1.3882E+08
3.1259E+06
8.2211E+07
1.0365E+08
3.9011E+07
3.8113
4.4524
4.3727
5.4284
4.5391
2.6733
2.4788
2.5653
2.5723
1.9706
14.8203
8.8691
11.9605
6.0998
8.8381
5.7663
3.6874
4.9203
3.0739
3.7910
HOS FEATURES FOR EPILEPSY SIGNAL
Epileptic
Parameter 1
Parameter 2
Parameter 3
Parameter 4
Parameter 5
Parameter 6
Parameter 7
Parameter 8
Parameter 9
Parameter 10
Parameter 11
Parameter 12
Parameter 13
Parameter 14
Parameter 15
Parameter 16
Parameter 17
Parameter 18
Parameter 19
Parameter 20
Parameter 21
Parameter 22
Parameter 23
Parameter 24
Parameter 25
Parameter 26
Parameter 27
Parameter 28
Parameter 29
Parameter 30
Parameter 31
Parameter 32
Parameter 33
Parameter 34
Parameter 35
Parameter 36
Parameter 37
Parameter 38
Parameter 39
Parameter 40
Parameter 41
Parameter 42
ent1
0.6889
0.6868
0.7077
0.5671
0.6209
0.5996
0.7544
0.4388
0.5376
0.7455
0.7337
0.573
0.527
0.6556
0.6274
0.4431
0.6966
0.6973
0.5972
0.5251
0.6396
0.6664
0.6396
0.6668
0.6463
0.5268
0.6935
0.5748
0.6353
0.7099
0.5306
0.6334
0.5676
0.7363
0.7015
0.5011
0.7062
0.6514
0.4805
0.6019
0.6798
0.5401
ent2
0.5125
0.4996
0.5659
0.2754
0.4345
0.3023
0.4478
0.1462
0.1882
0.6076
0.6064
0.1914
0.2239
0.3657
0.3452
0.2007
0.4935
0.4183
0.3546
0.1716
0.3859
0.4646
0.4279
0.5181
0.3654
0.2063
0.5495
0.3329
0.3001
0.5743
0.1916
0.4361
0.4
0.5793
0.5557
0.0789
0.5423
0.5273
0.1894
0.2671
0.4313
0.234
entPh
3.3951
3.5045
3.5716
3.5372
3.4236
3.5118
3.5186
3.4313
3.5597
3.5453
3.4674
3.4118
3.5341
3.4962
3.5297
3.5649
3.5611
3.5423
3.4248
3.5725
3.422
3.5395
3.5376
3.5537
3.5255
3.5471
3.5497
3.5546
3.5713
3.5638
3.5607
3.3024
3.4889
3.4666
3.5677
3.5437
3.4812
3.5172
3.5147
3.495
3.5546
3.5632
BME499 CAPSTONE PROJECT FINAL REPORT
mAmp
7.47E+10
7.11E+10
2.60E+10
2.20E+09
2.05E+10
9.04E+08
1.39E+10
3.17E+10
1.92E+10
8.31E+10
3.54E+10
6.65E+10
1.05E+11
1.45E+09
5.11E+09
7.08E+08
1.14E+11
2.74E+09
6.21E+10
4.81E+09
1.23E+09
4.97E+09
6.62E+09
1.29E+10
3.13E+10
4.69E+09
7.75E+10
5.32E+10
3.35E+09
5.79E+10
1.24E+10
1.03E+09
2.10E+10
3.54E+10
6.64E+10
3.40E+09
1.38E+11
3.23E+09
5.42E+08
3.61E+09
4.61E+10
2.00E+10
wc1
18.4051
18.2409
20.2247
10.3980
14.2566
15.4071
22.4715
11.9494
11.3421
24.2489
20.0806
13.6438
14.3176
13.4513
9.5751
6.2638
16.4640
16.0862
12.0828
13.5263
13.4714
16.4543
16.0565
15.4091
19.4026
13.3589
17.2018
11.2371
10.4039
17.0329
14.2177
12.1986
11.1920
19.8069
19.7371
11.8722
19.6662
14.2136
9.7402
10.7318
18.5041
10.9914
wc2
7.9880
8.0223
5.5538
8.7679
7.9225
9.5429
8.6489
9.9204
8.6340
11.7469
10.7297
8.6879
9.8168
6.0320
0.6876
2.5583
6.7387
4.1400
6.7843
9.4245
6.5954
6.8871
5.9160
6.4961
7.2881
9.7860
8.4269
6.6630
1.6676
10.1500
9.4193
5.4935
7.1078
9.9682
8.9772
8.6215
8.4069
5.7239
9.7831
1.6813
6.3332
9.8200
wc3
17.7792
17.6637
20.4704
13.5586
12.5714
13.0371
24.5414
12.6296
13.6621
23.5485
19.7337
13.0437
15.3347
13.9917
13.2607
7.6510
17.9731
16.8379
12.7658
15.4021
13.8010
16.4844
15.3905
15.0163
14.9749
13.8983
17.8180
11.8563
15.6066
17.9446
14.6897
13.0371
10.7098
19.6615
19.9698
12.4030
19.2655
13.4492
13.1048
14.3621
17.4077
13.4326
50
wc4
7.6611
7.2575
8.0995
8.1458
6.5239
7.2652
8.0314
9.5274
8.7210
11.1083
9.9679
7.6939
10.1238
5.7615
5.2380
3.1914
7.3481
6.5074
7.0596
9.9724
6.6993
7.5559
6.6936
7.6879
6.3790
9.1179
8.1075
6.6956
5.4567
9.0854
9.2032
6.0885
6.3616
9.5529
8.2873
8.2035
8.0911
6.3276
9.1277
4.9942
6.5018
8.9123
Parameter 43
Parameter 44
Parameter 45
Parameter 46
Parameter 47
Parameter 48
Parameter 49
Parameter 50
Parameter 51
Parameter 52
Parameter 53
Parameter 54
Parameter 55
Parameter 56
Parameter 57
Parameter 58
Parameter 59
Parameter 60
Parameter 61
Parameter 62
Parameter 63
Parameter 64
Parameter 65
Parameter 66
Parameter 67
Parameter 68
Parameter 69
Parameter 70
Parameter 71
Parameter 72
Parameter 73
Parameter 74
Parameter 75
Parameter 76
Parameter 77
Parameter 78
Parameter 79
Parameter 80
Parameter 81
Parameter 82
Parameter 83
Parameter 84
Parameter 85
Parameter 86
Parameter 87
Parameter 88
Parameter 89
Parameter 90
Parameter 91
Parameter 92
Parameter 93
0.5902
0.6136
0.5281
0.6172
0.7327
0.5982
0.7388
0.5742
0.622
0.7379
0.6412
0.5205
0.5936
0.6623
0.5228
0.5837
0.6128
0.6404
0.5397
0.6596
0.598
0.665
0.5219
0.6334
0.6406
0.724
0.658
0.4619
0.5739
0.5699
0.5953
0.6806
0.4895
0.7102
0.662
0.6795
0.587
0.4567
0.6275
0.5201
0.653
0.5561
0.7176
0.741
0.569
0.5164
0.5813
0.7653
0.6463
0.6498
0.6613
0.2521
0.3942
0.1973
0.3874
0.58
0.336
0.598
0.2886
0.3775
0.6097
0.3552
0.2306
0.3614
0.5027
0.2388
0.2962
0.4139
0.3933
0.1871
0.4639
0.21
0.4843
0.2609
0.4526
0.4023
0.5682
0.339
0.0907
0.2534
0.2334
0.3985
0.2986
0.0717
0.5744
0.4994
0.4979
0.1841
0.1862
0.2609
0.1875
0.3014
0.2352
0.5701
0.5492
0.4032
0.1052
0.2323
0.6038
0.3461
0.4519
0.4405
3.5756
3.5292
3.5443
3.5241
3.4899
3.5143
3.491
3.4947
3.5481
3.5777
3.5399
3.49
3.5708
3.4506
3.5223
3.5434
3.5545
3.4787
3.571
3.5227
3.5646
3.555
3.4526
3.4145
3.5059
3.4761
3.54
3.5566
3.4804
3.5408
3.5396
3.5238
3.4634
3.5524
3.5529
3.544
3.5001
3.4389
3.5219
3.4785
3.5471
3.5703
3.4645
3.5469
3.4286
3.493
3.5522
3.5769
3.5248
3.5053
3.4742
BME499 CAPSTONE PROJECT FINAL REPORT
5.42E+08
2.47E+10
4.72E+09
1.35E+10
1.36E+11
7.49E+09
8.03E+10
9.92E+09
1.52E+09
8.31E+09
7.12E+09
9.42E+09
1.48E+09
4.48E+09
1.13E+10
1.77E+10
1.49E+11
8.68E+10
1.02E+10
1.44E+10
1.06E+09
1.21E+09
4.50E+10
1.12E+10
7.12E+10
1.19E+11
8.13E+09
2.67E+09
1.88E+09
7.73E+09
1.87E+10
9.37E+08
4.03E+09
6.92E+10
7.88E+08
1.83E+09
9.86E+10
7.53E+10
5.90E+10
3.86E+10
5.23E+08
2.31E+09
6.42E+10
3.45E+09
2.66E+10
3.18E+09
2.83E+09
1.02E+10
3.32E+10
1.01E+10
7.93E+10
11.4396
11.4485
10.1507
13.2830
23.5100
12.0862
20.5829
14.2360
10.1377
20.2915
12.4134
12.9606
13.4948
14.1838
11.2504
10.6791
12.6358
16.0743
10.8864
16.0227
13.7124
13.7101
7.5731
13.6254
15.3635
20.3064
12.5698
11.6756
7.3422
16.0847
11.0928
8.7212
12.3559
20.6035
13.5606
17.3370
15.0829
12.1126
15.3760
13.4131
15.7852
8.7081
21.3796
22.0580
11.1529
12.7138
10.2822
20.1497
14.9294
14.3396
16.2198
6.5512
6.3352
3.5126
7.8843
9.8610
7.5318
11.8694
9.8391
4.8853
8.4972
3.6741
9.4763
6.3024
6.7231
9.8933
6.1308
7.2390
5.0004
8.7653
8.6890
8.0127
6.9404
4.0176
7.5682
8.1318
11.0483
4.1509
9.7108
1.5387
10.0983
5.7026
4.3826
9.0154
8.3960
7.1307
6.9063
9.1232
9.7463
5.9704
9.9195
10.3220
2.3221
9.1028
13.1645
6.7800
9.0693
1.9940
8.0796
7.8464
7.0193
5.3849
12.4006
13.0617
11.1002
14.0045
22.5110
13.0972
19.5919
14.6785
12.5750
21.4595
14.7441
13.9058
11.8645
14.1977
12.5138
12.0220
12.8342
16.0463
16.9592
14.4949
14.0432
14.3804
8.1604
13.0676
14.2272
19.0429
15.9111
12.8970
11.1337
15.7778
11.5649
16.6226
12.6468
20.3966
14.0106
16.1882
15.4518
12.5478
14.8335
14.3052
17.3047
10.5588
20.1763
21.8130
10.7559
13.0759
13.6669
22.4910
15.6304
14.3452
16.5808
51
6.2333
6.8679
4.0372
7.9963
9.7751
7.5598
10.0685
9.5579
5.5262
8.7052
5.3833
9.3484
6.0478
6.6925
8.4726
6.5341
7.0741
5.2939
10.9212
7.5327
8.0964
6.5485
4.1302
6.7260
7.5000
9.8576
5.6165
8.8780
4.6165
9.2261
5.9769
6.2373
8.4663
8.2993
6.5781
7.8800
9.0128
9.2899
6.2218
9.6843
9.2181
4.3076
8.9027
11.2148
6.2562
8.4610
4.6529
9.7291
7.7463
6.9944
5.8283
Parameter 94
Parameter 95
Parameter 96
Parameter 97
Parameter 98
Parameter 99
Parameter100
0.5358
0.528
0.5865
0.4863
0.4946
0.6914
0.6001
0.2399
0.1959
0.1454
0.207
0.1947
0.4516
0.3067
3.5668
3.5458
3.4816
3.5451
3.5131
3.4993
3.5645
BME499 CAPSTONE PROJECT FINAL REPORT
1.56E+10
6.65E+09
5.49E+08
9.41E+10
1.38E+10
4.11E+10
7.88E+09
11.3955
11.8396
11.9796
12.5710
10.7254
18.6015
14.2493
9.9592
9.8752
7.9916
9.6221
9.7255
10.7346
9.4313
13.0602
14.4817
13.7675
12.7911
12.8990
18.2191
14.1297
52
8.7246
9.9132
7.7615
9.0840
9.1270
9.4879
8.2262
APPENDIX B- MEETING LOGS
________________________________________________________________________

Meeting Log 1
Date of Meeting
Venue
Time
: 14th August 2010
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11.30am -12.30pm (1hr)
Discussion Details:
Dr Rajendra explains to me what is expected from my project development. I am required
to understand the anatomy of the brain, what is Epilepsy, how seizures comes about, what
are the current methods that the researcher has done and discuss the area on improvising
early detection of epilepsy. I must also learn and progress my project with the knowledge
of knowing everything by the fingertips in order to produce a good project outcomes. He
also gives me eight journal articles to read and I must understand them.
The 2nd meet up session will take place at the same place on 23rd August 2010, time:
11am.

Meeting Log 2
Date of Meeting
Venue
Time
: 23rd August 2010
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11am – 12pm (1hr)
Discussion Details:
Dr Rajendra gives me a clear overview on the area of research. He asked if I have
completed the eight journal articles and check if I can understand all the methodology
that is being used and what are the types of features being used to obtain an automatic
classification of the three clinical states such as pre-ictal, normal or ictal, as well as the
types of features obtained to be fed into different types of classifiers in order to automatic
detect the mental clinical states.
Literature Review:
I update Dr Raj what I have done over the week. I went to National Libraries for books
on neurology, surfing online for researches on epilepsy and read up related journals to
gather the information that aids in my project. Now I have a better understanding on the
brain anatomy, biochemical states of the neurons, the cause and effect of epilepsy and
seizures. I have also extended my source of information through newspaper articles and
got to know about the population, computational data of epilepsy and appreciate the
importance to have an early detection for epilepsy.
Project Proposal:
BME499 CAPSTONE PROJECT FINAL REPORT
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Dr Raj has given me guidance of what is required to be included in my project proposal
and ask me to come out with a simple process flow diagram to have an idea how I should
carry out my proposal. The literature reviews is my guidelines which I can refer to get my
idea to get new innovative ideas on what others has done and I allows me to explore what
others have yet accomplish.

Meeting Log 3
Date of Meeting
Venue
Time
: 9th September 2010
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11am-11.30am (30mins)
Discussion Details:
Short Meeting is about updating my progress and findings I have gathered from the
literature review. The Matlab tutorial practices outcome and Dr Raj asks me if I have any
questions on regards to the project. However, I am still reading on my journal papers for
details and planning on my project development progress.
The 4th meet up session will take place at the same place on 25th September 2010, time:
11am.

Meeting Log 4
Date of Meeting
Venue
Time
: 25th September 2010
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11am-12pm (1hr)
Discussion Details:
Dr Raj prepared a few set of MATLAB worksheets for enhancing my MATLAB
programming knowledge. He go through with me some of the basic programming
commands, knowing how to plot different types of waveforms in the graph, setting the
x,y axis, changing font size and adjust scaling. I am required to practice on the questions
in the MATLAB worksheets, on top of my own MATLAB tutorials and I have to see him
again.
The 5th meet up session will take place at the same place on 2nd October 2010, time:
11am.

Meeting Log 5
Date of Meeting
Venue
Time
: 2nd October 2010
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11am-12pm (1hr)
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Discussion Details:
Dr Raj taught plotting of the discrete time and frequency signals, advice me on the
programming codes. He also reminded what is the important codes like stem (n,x) when
we want to plot a discrete waveform in the graph and how to compute in a large scales
type of EEG signals when we have many sets of EEG data. I need to have more
MATLAB tutorial practices.
Discussion also covers drafting the interim report for the coming submission in 8th Nov
2010. I have also informed Dr Raj that I be setting aside 10 for exam revision and will
continue with literature review for Discrete Wavelet Transform (DWT) after exam.
Should the literature review be done as per schedule, I will proceed with DWT
programming and let Dr Raj review the results.

Meeting Log 6
Date of Meeting
Venue
Time
: 11th February 2011
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11am -12pm (1hr)
Discussion Details:
Dr Raj reviewed on my 200 normal, 200 background and 100 epilepsy EEG data
processed by the discrete wavelet transformation method through email. However, after
performing the ANOVA analysis on all the data, the data is not significant. Hence, I am
encouraged to try on Higher Order Spectra analysis (HOS). As I have too much
information on DWT, I need to revise all the journal reading related to HOS and collect
more information for HOS programming in extracting the HOS features.
I am required to prepare another 3 sets (normal, background and epilepsy) of 100 EEG
signals from Bonn University database and run them all to get the data for the various
features. Will email Dr Raj the results and check if it is ok to proceed on for
classifications.

Meeting Log 7
Date of Meeting
Venue
Time
: 26th February 2011
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11am -12pm (1hr)
Discussion Details:
The data of the HOS features were emailed to Dr Raj and reviewed. The ANOVA test
has shown good significant p-values of less than 0.0001. Hence, I need to start the
preparation of MATLAB programming for each of the classifiers. This is to process all
BME499 CAPSTONE PROJECT FINAL REPORT
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the good HOS features and perform an automatic classification of epilepsy. The
comparative study will be done based on the results of the various classifiers. Meanwhile,
I also start to tabulate the tables to display the results in my Final Year report.

Meeting Log 8
Date of Meeting
Venue
Time
: 22nd March 2011
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11.30am -12pm (30 mins)
Dr Raj reviewed the results of the various classifications. The result is ok. I may start to
write the first chapter for the Final Year report and show him the content of each chapter.
I am required to write about the anatomy, epilepsy, seizures, EEG and etc as informative
as possible. Chapter one will be on the fundamentals from Literature review. Chapter two
will be on project methodology. Chapter three is all about results and discussion on the
HOS extracted features, classifier performances and outcome. Chapter four will cover the
summary, conclusion and recommendation. Chapter five and six is about project
development and reflection respectively.
He also reminded me about plagiarism when submitting the final year report and all
references are to be cited accordingly.

Meeting Log 9
Date of Meeting
Venue
Time
: 11th April 2011
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11.30am -12pm (30 mins)
Chapter One on all literature review is done and reviewed by Dr Raj. However, I need to
write more in Chapter Two regarding on the methodology, I need to improve on the
numbers of tables that is used to describe my work. Need to show the distinct between
the good and less significant data.

Meeting Log 10
Date of Meeting
Venue
Time
: 14th May 2011
: Ngee Ann Polytechnic, Block 8, Level 3, Room 9A
: 11.30am -12pm (30 mins)
Dr Raj reviewed the Final Year Report. It is ready for submission. The coming Poster
Presentations and the information required for Poster are discussed. I need to refer to the
Capstone
handbook
for
more
information
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get
the
template
ready.
56
APPENDIX C- PROGRAMMING CODES
________________________________________________________________________
1. Main Program: Features extraction for three classes of EEG data.
% HOSoneD.mat, a main program for the extraction of HOS features.
clear all
clc
tmp = [];
starttime=0;
%Temporary space for data
%maxFrm= 2048 (Length of Data)
t = []; t1 =[]; t2 = []; t3=[]; t4=[];t5=[]; t6=[]; tH=[]; tWcob =[]; %Storing HOS features data
nfft=256; shift=128;
%nfft=128; shift=64;
nv=load('O100.txt');
m = length(nv);
nFrm = floor((m-nfft)/shift)+1;
% Loading the EEG signal
% Return the largest dimension of the EEG data
% Perform FFT algoritm and rounding the contents in nFrm to
the nearest integers
if nFrm > 1
%Features Extraction perform by sub function
[ent1,ent2,ent3,entPh,mAmp, H, WC]= bispFeaturesWSWcob(nv,nfft,shift);
% Directory of containing the HOS extracted features
t1 =[t1 ent1];
% Storing data for normalized bispectral entropy
t2 =[t2 ent2];
% Storing data for normalized bispectral squared entropy
t3 =[t3 ent3];
% Storing data for normalized bispectral cubed entropy
t5 = [t5 mAmp];
% Storing data for mean bispectrum magnitude
t6 = [t6 entPh];
% Storing data for bispectrum phase entropy
tH = [tH; H];
% Storing data for moments of bispectrum
tWcob =[tWcob; WC];
% Storing data for weighted center of bispectrum
end
ent1 = t1';
ent2 = t2';
ent3 = t3';
mAmp = t5';
entPh = t6';
Wcob = tWcob;
H = tH;
%Display result for normalized bispectral entropy
%Display result for bispectral squared entropy
%Display result for normalized bispectral cubed entropy
%Display result for mean bispectrum magnitude
%Display result for bispectrum Phase entropy
%Display result for weighted center of bispectrum
%Display result for moments of bispectrum
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2. Sub Function for Main Program on Features extraction
% Sub function of HOSoneD.mat
% bispectral features with shift
% bispectrum plotting
% function of bisp_plot(input,Xsize)
function [ent1,ent2,ent3,entPh,mAmp,H,wcob ]= bispFeaturesWSWcob(nv,Xsize,shift)
m = length(nv);
nFrm = floor((m-Xsize)/shift)+1;
B_sum = zeros(Xsize/2, Xsize/4);
sPt = 1; ePt = Xsize;
for i = 1:nFrm
input = nv(sPt:ePt);
Fx_input = fft(input,Xsize);
% Convert time domain to frequency domain by applying Fast
Fourier Transform algorithm to the “input”
Fx_input(1) = 0 + i*0;
Fx_input(Xsize/2+2) = 0 + i*0;
%set dc = 0 else interpolation will cause dc dependence
%set = 0 else interpolation will cause problem
% 3rd Cumulant Function of Bispectrum
B_input = zeros(Xsize/2, Xsize/4); % Form the triangle at the Non-Redundant Region
for k1 = 1:Xsize/2;
for k2 = 1:min(k1,Xsize/2-k1) % Return the same size of array that is same as k1 and Xsize/2-k1with
the smallest elements taken from Xsize/2-k1
B_input(k1,k2) = Fx_input(k1) * Fx_input(k2) * conj(Fx_input(k1+k2-1)); % The complex
conjugate: real to imaginary.
end
end
B_sum = B_sum+B_input;
sPt = sPt+shift; ePt=ePt+shift;
end;
B_input = B_sum/nFrm;
%x = [1 0 0 0; 1 2 0 0; 1 2 3 0; 1 2 3 4];
sxw = 0; sb = 0; syw = 0;
asxw = 0; asb = 0; asyw = 0;
for k1 = 1:Xsize/2;
for k2 = 1:min(k1,Xsize/2-k1)
sxw = sxw + k1*B_input(k1,k2); syw = syw + k2*B_input(k1,k2);
asxw = asxw + k1*abs(B_input(k1,k2)); asyw = asyw + k2*abs(B_input(k1,k2));
sb = sb + B_input(k1,k2);
asb = asb + abs(B_input(k1,k2));
end;
end;
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%sxw
%sb
%syw
wcobx = abs(sxw)/abs(sb);
wcoby = abs(syw)/abs(sb);
awcobx = abs(asxw)/abs(asb);
awcoby = abs(asyw)/abs(asb);
wcob = [ wcobx wcoby awcobx awcoby];
B_ent2 = B_input(find(B_input)).*conj(B_input(find(B_input))); % Return a non-zero value.
B_ent1 = sqrt(B_ent2);
B_ent3 =(B_ent1.^3)./sum(B_ent1.^3);
B_ent2 = B_ent2./sum(B_ent2); % Probability Computation
B_ent1 = B_ent1./sum(B_ent1);
[m,n] = size(B_ent2);
ent2 = -sum(B_ent2.*log(eps+B_ent2))/log(m); %Log(m): An average of obtaining a relative bispectrum
estimation
ent1 = -sum(B_ent1.*log(eps+B_ent1))/log(m);
ent3 = -sum(B_ent3.*log(eps+B_ent3))/log(m);
% Defining the Bispectra phase entropy & Mean bispectra amplitude (mAmp) Equation for the features to
be extracted
B_phi = angle(B_input(find(B_input)));
N = 36;
p = zeros(1,N);
theta=-pi;
delTheta = 2*pi/N;
for k3=1:N-1
tt = find(B_phi>=theta&B_phi<(theta+delTheta));
[m,n] = size(tt);
p(k3) = m;
theta = theta+delTheta;
end;
tt = find(B_phi>=theta&B_phi<=pi);
[m,n] = size(tt);
p(N) = m;
%Probability Computation
[m,n] = size(B_phi);
p=p/m;
entPh = -sum(p.*log(eps+p));
mAmp = sum(abs(B_input(find(B_input))))/m;
%computing the Moments of Bispectra (diagonal 1st and 2nd moment)
%the values of the H features extracted for diagonal components could be very small
H1 = sum(log10(abs(B_input(find(B_input)))));
H2 = 0;H3=0;
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for i=2:Xsize/4
if abs(B_input(i,i))> eps
H2=H2+log10(abs(B_input(i,i)));
H3=H3+i*log10(abs(B_input(i,i)));
end
end;
H4=0;
for i=2:Xsize/4
if abs(B_input(i,i))> eps
H4=H4+(i-H3)*(i-H3)*log10(abs(B_input(i,i)));
end;
end;
H5 = 0;
for k1 = 1:Xsize/2;
for k2 = 1:min(k1,Xsize/2-k1)
H5 = H5 + sqrt(k1^2+k2^2)*abs(B_input(k1,k2));
end;
end;
%H5 = log10(H5);
H = [H1 H2 H3 H4 H5];
%B_input_imag = imag(B_input);
%B_input_real = real(B_input);
%B_inputimag_log = log10(abs(flipud(B_input_imag')+eps)); %avoid log(0)
%figure(1), subplot(2,1,1, 'align'), imagesc(B_inputimag_log); colorbar;
%B_inputreal_log = log10(abs(flipud(B_input_real')+eps));
%figure(1), subplot(2,1,2, 'align'), imagesc(B_inputreal_log); colorbar;
%end
3. Various Classifiers
Fuzzy Sugeno Classifier
clear all;
clc;
close all;
%Load the Testing and Training sets of data
for pp=1:3
filename1=['testing',num2str(pp)]
filename2=['training',num2str(pp)]
load(filename1)
load(filename2)
load class;
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%Training various HOS features
p1 = training(:,1);
p2 = training(:,2);
p3 = training(:,3);
p4 = training(:,4);
p5 = training(:,5);
p6 = training(:,6);
p7 = training(:,7);
p8 = training(:,8);
%Define the three classes of Data
t1 = class(:,1);
t2 = class(:,2);
t3 = class(:,3);
p = [p1';p2';p3';p4';p5';p6';p7';p8'];
t = [t1';t2';t3'];
%Testing various HOS features
test1 = testing(:,1);
test2 = testing(:,2);
test3 = testing(:,3);
test4 = testing(:,4);
test5 = testing(:,5);
test6 = testing(:,6);
test7 = testing(:,7);
test8 = testing(:,8);
test = [test1';test2';test3';test4';test5';test6';test7';test8'];
ptest=test';
%18 0 and 18 1
fismat = genfis2(p',t',0.5,[0 0 0 0 0 0 0 0 0 0 0;1 1 1 1 1 1 1 1 1 1 1]);
y=evalfis(ptest,fismat);
outres=y';
yj=outres';
for i=1:length(yj)
a=yj(i,:);
maxi=max(a);
%8 classes
for j=1:3
if yj(i,j)==maxi
yj(i,j)=1;
else
yj(i,j)=0;
end
end
end
yj
result1=0;
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result2=0;
result3=0;
for i=1:90
if(yj(i,1)==1)
result1(i,1)=0.1;
else
result1(i,1)=0;
end
end
for i=1:90
if(yj(i,2)==1)
result2(i,1)=0.01;
else
result2(i,1)=0;
end
end
for i=1:90
if(yj(i,3)==1)
result3(i,1)=0.001;
else
result3(i,1)=0;
end
end
result=result1+result2+result3;
output=0;
j=1;
for i=1:90
if(result(i,1)==0.001)
output(j,1)=1;
else if(result(i,1)==0.01)
output(j,1)=2;
else
output(j,1)=3;
end
end
j=j+1;
end
y=output';
normwrong=0;
normright=0;
duringseizurewrong=0;
duringseizureright=0;
beforeseizurewrong=0;
beforeseizureright=0;
for i=1:30
if y(1,i)==1
duringseizureright=duringseizureright+1;
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else
duringseizurewrong=duringseizurewrong+1;
end
end
for i=31:60
if y(1,i)==2
beforeseizureright=beforeseizureright+1;
else
beforeseizurewrong=beforeseizurewrong+1;
end
end
for i=61:90
if y(1,i)==3
normright=normright+1;
else
normwrong=normwrong+1;
end
end
ans=(beforeseizureright+duringseizureright+normright)/90*100;
TN=normright;
FP=normwrong;
e=0
for i=1:60
if(y(i)==3)
e=e+1
end
end
FN=e;
TP=60-FN;
sensi=(TP/(TP+FN))*100;
speci=(TN/(TN+FP))*100;
ppv=(TP/(TP+FP))*100;
final(pp,:)=[TN;FN;TP;FP;ans;ppv;sensi;speci]'
TN=num2str(TN)
FN=num2str(FN)
TP=num2str(TP)
FP=num2str(FP)
ppv=num2str(round(ppv))
sensi=num2str(round(sensi))
speci=num2str(round(speci))
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end
final(4,1)=round(mean(final(1:3,1)));
final(4,2)=round(mean(final(1:3,2)));
final(4,3)=round(mean(final(1:3,3)));
final(4,4)=round(mean(final(1:3,4)));
final(4,5)=mean(final(1:3,5));
a=([num2str(final(4,5),'%.1f')]);
final(4,5)=str2num(a);
final(4,6)=mean(final(1:3,6));
b=([num2str(final(4,6),'%.1f')]);
final(4,6)=str2num(b);
final(4,7)=mean(final(1:3,7));
c=([num2str(final(4,7),'%.1f')]);
final(4,7)=str2num(c);
final(4,8)=mean(final(1:3,8));
d=([num2str(final(4,8),'%.1f')]);
final(4,8)=str2num(d);
Probabilistic Neural Network Classifier (PNN)
%Training the set of HOS features
clc;
close all;
clear all;
for pp=1:3
filename1=['training',num2str(pp)];
filename2=['net',num2str(pp)];
load(filename1)
P = training(:,1:8);
P=P';
class=training(:,9)';
T = ind2vec(class);
net = newpnn(P,T,0.015);
save(filename2,'net');
end
figure
clc
close all;
clear all;
for pp=1:3
filename1=['testing',num2str(pp)];
filename2=['net',num2str(pp)];
load(filename1)
load(filename2)
% Testing the set of HOS features
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P = testing(:,1:8);
P=P'
Y = sim(net,P);
y = vec2ind(Y)
normwrong=0;
normright=0;
duringseizurewrong=0;
duringseizureright=0;
beforeseizurewrong=0;
beforeseizureright=0;
for i=1:30
if y(1,i)==1
duringseizureright=duringseizureright+1;
else
duringseizurewrong=duringseizurewrong+1;
end
end
for i=31:60
if y(1,i)==2
beforeseizureright=beforeseizureright+1;
else
beforeseizurewrong=beforeseizurewrong+1;
end
end
for i=61:90
if y(1,i)==3
normright=normright+1;
else
normwrong=normwrong+1;
end
end
ans=(beforeseizureright+duringseizureright+normright)/90*100;
TN=normright;
FP=normwrong;
e=0
for i=1:60
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if(y(i)==3)
e=e+1
end
end
FN=e;
TP=60-FN;
sensi=(TP/(TP+FN))*100;
speci=(TN/(TN+FP))*100;
ppv=(TP/(TP+FP))*100;
final(pp,:)=[TN;FN;TP;FP;ans;ppv;sensi;speci]'
TN=num2str(TN)
FN=num2str(FN)
TP=num2str(TP)
FP=num2str(FP)
ppv=num2str(round(ppv))
sensi=num2str(round(sensi))
speci=num2str(round(speci))
end
final(4,1)=round(mean(final(1:3,1)));
final(4,2)=round(mean(final(1:3,2)));
final(4,3)=round(mean(final(1:3,3)));
final(4,4)=round(mean(final(1:3,4)));
final(4,5)=mean(final(1:3,5));
a=([num2str(final(4,5),'%.1f')]);
final(4,5)=str2num(a);
final(4,6)=mean(final(1:3,6));
b=([num2str(final(4,6),'%.1f')]);
final(4,6)=str2num(b);
final(4,7)=mean(final(1:3,7));
c=([num2str(final(4,7),'%.1f')]);
final(4,7)=str2num(c);
final(4,8)=mean(final(1:3,8));
d=([num2str(final(4,8),'%.1f')]);
final(4,8)=str2num(d);
K-Nearest Neighbor Classifier (KNN)
clc;
clear all;
close all;
%Loading training and testing sets of data
for pp=1:3
filename1=['training',num2str(pp)];
filename2=['testing',num2str(pp)];
load(filename1)
load(filename2)
%load training80;
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%load testing80;
% classify the sample using the nearest neighbor classification
y = knnclassify(testing(:,1:8), training(:,1:8), training(:,9));
y=y';
normwrong=0;
normright=0;
duringseizurewrong=0;
duringseizureright=0;
beforeseizurewrong=0;
beforeseizureright=0;
for i=1:30
if y(1,i)==1
duringseizureright=duringseizureright+1;
else
duringseizurewrong=duringseizurewrong+1;
end
end
for i=31:60
if y(1,i)==2
beforeseizureright=beforeseizureright+1;
else
beforeseizurewrong=beforeseizurewrong+1;
end
end
for i=61:90
if y(1,i)==3
normright=normright+1;
else
normwrong=normwrong+1;
end
end
ans=(beforeseizureright+duringseizureright+normright)/90*100;
TN=normright;
FP=normwrong;
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e=0
for i=1:60
if(y(i)==3)
e=e+1
end
end
FN=e;
TP=60-FN;
sensi=(TP/(TP+FN))*100;
speci=(TN/(TN+FP))*100;
ppv=(TP/(TP+FP))*100;
final(pp,:)=[TN;FN;TP;FP;ans;ppv;sensi;speci]'
TN=num2str(TN)
FN=num2str(FN)
TP=num2str(TP)
FP=num2str(FP)
ppv=num2str(round(ppv))
sensi=num2str(round(sensi))
speci=num2str(round(speci))
end
final(4,1)=round(mean(final(1:3,1)));
final(4,2)=round(mean(final(1:3,2)));
final(4,3)=round(mean(final(1:3,3)));
final(4,4)=round(mean(final(1:3,4)));
final(4,5)=mean(final(1:3,5));
a=([num2str(final(4,5),'%.1f')]);
final(4,5)=str2num(a);
final(4,6)=mean(final(1:3,6));
b=([num2str(final(4,6),'%.1f')]);
final(4,6)=str2num(b);
final(4,7)=mean(final(1:3,7));
c=([num2str(final(4,7),'%.1f')]);
final(4,7)=str2num(c);
final(4,8)=mean(final(1:3,8));
d=([num2str(final(4,8),'%.1f')]);
final(4,8)=str2num(d);
Decision Tree (DT)
clear all;
clc;
close all;
% Loading the sets of Testing and Training data
for pp=1:3
filename1=['testing',num2str(pp)]
filename2=['training',num2str(pp)]
load(filename1)
load(filename2)
BME499 CAPSTONE PROJECT FINAL REPORT
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X=training(:,1:8);
size(X);
Y=training(:,9);
size(Y);
% Build Decision Tree
BuiltTree = classregtree(X,Y,'method', 'classification','names',{'a1','a2','a3','a4','a5','a6','a7','a8'});
% Evaluate the establised tree using the test data
yfit = eval(BuiltTree,testing(:,1:8));
% Determine the accuracy.
pct = mean(strcmp(yfit,num2str(testing(:,9))));
output=cell2mat(yfit);
y=str2num(output);
y=y';
% Tabulate Accuracy
fprintf('%s %6.2f\n', 'Decision tree accuracy for the build dataset:', pct*100);
normwrong=0;
normright=0;
duringseizurewrong=0;
duringseizureright=0;
beforeseizurewrong=0;
beforeseizureright=0;
for i=1:30
if y(1,i)==1
duringseizureright=duringseizureright+1;
else
duringseizurewrong=duringseizurewrong+1;
end
end
for i=31:60
if y(1,i)==2
beforeseizureright=beforeseizureright+1;
else
beforeseizurewrong=beforeseizurewrong+1;
end
end
for i=61:90
if y(1,i)==3
normright=normright+1;
BME499 CAPSTONE PROJECT FINAL REPORT
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else
normwrong=normwrong+1;
end
end
ans=(beforeseizureright+duringseizureright+normright)/90*100;
%ans=round(ans);
str11=([num2str(ans,'%.1f')]);
TN=normright;
FP=normwrong;
e=0
for i=1:60
if(y(i)==3)
e=e+1
end
end
FN=e;
TP=60-FN;
sensi=(TP/(TP+FN))*100
speci=(TN/(TN+FP))*100
ppv=(TP/(TP+FP))*100
final(pp,:)=[TN;FN;TP;FP;ans;ppv;sensi;speci]'
TN=num2str(TN)
FN=num2str(FN)
TP=num2str(TP)
FP=num2str(FP)
ppv=num2str(round(ppv))
sensi=num2str(round(sensi))
speci=num2str(round(speci))
end
final(4,1)=round(mean(final(1:3,1)));
final(4,2)=round(mean(final(1:3,2)));
final(4,3)=round(mean(final(1:3,3)));
final(4,4)=round(mean(final(1:3,4)));
final(4,5)=mean(final(1:3,5));
a=([num2str(final(4,5),'%.1f')]);
final(4,5)=str2num(a);
final(4,6)=mean(final(1:3,6));
b=([num2str(final(4,6),'%.1f')]);
final(4,6)=str2num(b);
final(4,7)=mean(final(1:3,7));
c=([num2str(final(4,7),'%.1f')]);
final(4,7)=str2num(c);
final(4,8)=mean(final(1:3,8));
d=([num2str(final(4,8),'%.1f')]);
final(4,8)=str2num(d);
BME499 CAPSTONE PROJECT FINAL REPORT
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