Analyzing EEG

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Letters

Epileptic seizure detection by analyzing EEG signals using

different transformation techniques

Mohammad Zavid Parvez n Manoranjan Paul

Centre for Research in Complex Systems School of Computing amp Mathematics Charles Sturt University Bathurst NSW 2795 Australia

a r t i c l e i n f o

Article history

Received 13 August 2013

Received in revised form4 May 2014

Accepted 24 May 2014

Communicated by Bo ShenAvailable online 2 June 2014

Keywords

EEG

Epilepsy and seizure

Ictal and interictal

EMD

LS-SVM

a b s t r a c t

Feature extraction and classi1047297cation are still challenging tasks to detect ictal (ie seizure period) and

interictal (ie period between seizures) EEG signals for the treatment and precaution of the epileptic

seizure patient due to different stimuli and brain locations Existing seizure and non-seizure featureextraction and classi1047297cation techniques are not good enough for the classi1047297cation of ictal and interictal

EEG signals considering for their non-abruptness phenomena inconsistency in different brain locations

type (generalpartial) of seizures and hospital settings In this paper we present generic seizure

detection approaches for feature extraction of ictal and interictal signals using various established

transformations and decompositions We extract a number of statistical features using novel ways from

high frequency coef 1047297cients of the transformeddecomposed signals The least square support vector

machine is applied on the features for classi1047297cations Results demonstrate that the proposed methods

outperform the existing state-of-the-art methods in terms of classi1047297cation accuracy sensitivity and

speci1047297city with greater consistence for the large size benchmark dataset in different brain locations

amp 2014 Elsevier BV All rights reserved

1 Introduction

The human brain processes sensory information which is

received by externalinternal stimuli Human brain is an organic

electrochemical computer as neurons exploit chemical reaction to

generate electricity [1] Electroencephalogram (EEG) is a graphical

record of ongoing electrical activity which measures the changes

of the electrical activity in term of voltage 1047298uctuations of the brain

through multiple electrodes placed in the different location of the

brain [2] In the clinical contexts the main diagnosis of EEG is to

discover abnormalities of brain activities referred to the epileptic

seizure A seizure occurs when the neurons generate uncoordi-

nated electrical discharges that spread throughout the brain and

epilepsy is a recurrent seizure disorder caused by abnormal

electrical discharges from brain cells often in the cerebral cortex

[3] Another clinical use of EEG is in diagnosis of coma brain deathencephalopathies sleep disorder etc Moreover EEG can be used

in many applications such as emotion recognition [4] video

quality assessment [5] alcoholic consumption measurement [6]

sleep stage detection [7] change the brainwaves by smoking [8]

mobile phone usages [9] etc

People may experience abnormal activities in sensation move-

ment awareness and behavior during seizure as a result they

cannot perform normal task Ictal and interictal both are medical

condition of seizure where ictal represents the period of seizureand interictal represents the intermediate period between two

seizures Note that interictal is signi1047297cantly different from normal

non-seizure (mainly for healthy people) signal in terms of signal

characteristics A prediction of seizures (ie ictal) from interictal

could aware a patient to put away from the next seizure and also

can make sure better treatment and precaution A number of

existing EEG features extraction and classi1047297cation techniques

[11ndash17] are able to classify seizure and non-seizure EEG signals

for a small size dataset almost perfectly However those methods

are struggling to provide acceptable classi1047297cation accuracy for ictal

(ie seizure period) and interictal (ie period between seizures)

EEG signals due to the non-abruptness and inconsistence phenom-

ena [10] of the signals in different brain locations Moreover it is

more challenging to get acceptable label of accuracy by a particularmethod if we do not have any domain knowledge of the available

EEG signals due to different types of seizures (ie generalized or

partial) patients hospital setting brain location and artifact Note

that ictal signals are considered the EEG signals during the seizure

period when a patient shows abnormal activities whereas inter-

ictal signals are considered as non-ictal signals between two

seizure periods of an epileptic patient Thus we can consider the

characteristics of interictal signal as a middle stage of non-seizure

and ictal signals (although a patient may show normal brain

activities similar to the non-seizure signals during the interictal

period)

Contents lists available at ScienceDirect

journal homepage wwwelseviercomlocateneucom

Neurocomputing

httpdxdoiorg101016jneucom201405044

0925-2312amp 2014 Elsevier BV All rights reserved

n Corresponding author

E-mail address mparvezcsueduau (MZ Parvez)

Neurocomputing 145 (2014) 190ndash200



Existing methods [11ndash18] used small epilepsy dataset [19] (for

detailed information of the dataset see Section 31) The small

dataset with duration 236 s has seizure (ie ictal) and non-seizure

signals which can be distinguished by their visual phenomena

such as magnitude of amplitude and changing rate of frequency

(see in Fig 1(a)) For the non-seizure signal the amplitude is low

and the frequency is high while the nature of seizure signal is

totally opposite (see Fig 1(a)) Fig 1(b) and (c) shows the ictal and

interictal EEG signals from a large dataset [22] The 1047297gure

demonstrates the non-abrupt phenomena (ie not easily distin-

guishable between ictal and interictal based on amplitude and

frequency) of the ictal and interictal signal for both cases of Frontal

and Temporal lobes compared to the dataset in [19] Moreover

EEG signals from different locations exhibit different phenomenal

activities for an ictal and interictal period Thus the classi1047297cation

of ictal and interictal EEG signals is challenging compared to that

of seizure (ie ictal) and non-seizure EEG signals It also demon-

strates that the characteristics of EEG signals are varied fromdataset to dataset

Birvinskas et al [17] used DCT (discrete cosine transformation) to

reduce the EEG signals and then extracted features from the

reduced signals to classify EEG signals They achieved good

classi1047297cation accuracy by keeping low frequency DCT coef 1047297cient

for feature extractions Worrell et al [33] claimed that the seizure

onset frequencies are in the range of gamma frequency ie high

frequency band (typically between 30 to 100 Hz) They explicitly

mentioned that seizure onset frequencies are on the range of

frequency 74376 137 and 23776 28 Hz for Frontal and

Temporal lobes respectively They also observed that dominant

spectral peaks are observed in the high frequency spectral com-

ponents for the Frontal lobe and in the lower gamma frequency for

the Temporal lobe during the seizure period Other literature such

as Panda et al [11] Ocak et al [13] and Ghosh-Dastidar et al [12]

used other frequency bands such as delta theta alpha and beta

with gamma band to extract features for EEG signal classi1047297cation

From the above 1047297ndings and the characteristics of the different

datasets we 1047297nd that the frequency range of seizure onset can be

varied based on the brain location seizure type (generalpartial)

patient and hospital setting (eg capturing machine quality

artifacts etc) However we observe that the signal has larger

spikes ie high amplitude in the ictal (ie seizure) period

compared to the interictal period Thus based on the temporal

and spatial features of raw EEG signals and the above 1047297ndings of

the recent literatures our hypothesis is that the distinguishing

features between ictal and interictal signals would be in the high

frequency spectral area irrespective of the brain location patient

type of seizure and hospital setting

To verify our hypothesis we perform an experiment to 1047297nd the

gamma frequency of signals which provides the maximum ampli-

tude using ictal and interictal signals in different brain locationsThe results for 200 EEG signals from ictal and interictal are shown

in Fig 2 The 1047297gure shows that relatively high gamma frequencies

ie high frequencies in the signals provide the maximum ampli-

tude of ictal compared to the interictal signals for both Frontal and

Temporal lobes from different patients We use the maximum

amplitude criteria for the gamma band selection in this analysis as

our observation and Worrell et als observation indicate that

dominant spectral peaks for the seizure onset are in the high

frequency (ie gamma band) Thus the features extracted from

gamma frequencies ie the high frequencies should provide better

classi1047297cation accuracy between the ictal and interictal irrespective

of brain location patients type of seizure and hospital setting

Based on the above mentioned observation in this paper we

proposed generic seizure detection techniques which use different

Fig 1 Ictal and interictal EEG signals from different datasets and different brain locations (a) Seizure and non-seizure EEG signals from the small dataset [19] the 1047297rst row

and remaining four rows indicated the seizure and non-seizure signals respectively [19] (b) Ictal (patient one 7th datablock channel one) and interictal (patient one 95th

datablock channel one) of Frontal lobe signals [22] and (c) Ictal (patient 1215th datablock and channel one) and interictal (patient 12 35th datablock and channel one) of

Temporal lobe signals [22]

MZ Parvez M Paul Neurocomputing 145 (2014) 190ndash 200 191



established transformations and decompositions to get different

statistical features from the high frequencies component of the

signals to classify the ictal and interictal signals using the least

square support vector machine (LS-SVM) [23] Although we use

a number of established transformation and decompositions we

use weighted high frequency coef 1047297cients to extract the features In

the experiment we investigate the strength of each transformation

and decomposition for the capability of classi1047297cation accuracy as

well as the combined strength through different feature extrac-

tions We use a large dataset [22] consists of raw ictal and

interictal signals (no explicit artifact removal technique is applied)

from different brain locations different seizures and patients to

verify the performance of the proposed methods against two

state-of-the-art methods The experimental results demonstrate

that the proposed generic methods outperform the existing

methods in terms of accuracy (ie overall) sensitivity (ie ictal

or positive) and speci1047297city (ie interictal or negative) with greater

consistence for the challenging ictal and interictal EEG signals

classi1047297cation

The contributions and novelties of the proposed methods are in

the capacity to (i) classify challenging ictal and interictal signals

irrespective of brain location patient type of seizure and hospital

setting with a certain range of artifact tolerance (ii) identify that

high frequency component of EEG signals has the distinguishing

features to classify ictal and interictal EEG signals (iii) formulate

a weighing vector for high frequency coef 1047297cients to provide

individual importance of different coef 1047297cients (iv) provide the

rationality and way of extracting high frequency components from

each transformationdecomposition technique (v) to analysis EEG

signals for seizure detection best of our knowledge we are the 1047297rst

one to use two dimensional SVD (vi) identify the distinguishing

features (provided pictorial comparison) for ictal and interictal

signals for classi1047297cation and (vii) provide comprehensive result

analysis using receiver operating characteristics (ROC) curve Whis-ker-box graphical view of classi1047297cation outcome and classi1047297ca-

tion performance in terms of sensitivity speci1047297city and accuracy

2 Existing methods for seizure detection

Existing feature extraction and classi1047297cation methods based on

wavelet [11ndash13] and Fourier transformation [14] were employed

for the detection of seizure in EEG signals Panda et al [11]

computed various features such as energy entropy and standard

deviation (STD) using discrete wavelet transformation (DWT) and

used support vector machine (SVM) as a classi1047297er Dastidar et al

[12] applied wavelet transformation to decompose the EEG signals

into different range of frequencies and then extracted three

features such as STD correlation dimension and the largest

Lyapunov exponent (quantifying the non-linear chaotic dynamics

of the signals) and applied different methods for classi1047297cation

Ocak [13] proposed a fourth level wavelet packet decomposition

method to decompose the normal and epileptic EEG epochs to

various frequency-bands and then used genetic algorithm to 1047297nd

optimal feature subsets which maximize the classi1047297cation perfor-

mance Polat et al [14] extracted feature using fast Fourier trans-

form (FFT) and then classi1047297ed using decision making classi1047297er The

techniques [111214] have used small dataset [19] (for detailed

information of the dataset see Section 21) Sample examples of

the dataset are shown in Fig 1(a) The classi1047297cation accuracies

were 9120 [11] 9670 [12] and 9872 [14] respectively Ocaks

[13] experimental dataset was of the same pattern with the

dataset in [19] and the classi1047297cation accuracy was 98

Other feature extraction and classi1047297cation methods [15ndash18]

based on empirical mode decomposition (EMD) principle component

analysis (PCA) and genetic algorithm (GA) have been proposed

Liang et al [15] extracted EEG features where the approximate

entropy(ApEn) analysis was evaluated for its ability to analyze

multiclass EEGs and the performance of ApEn analysis was

enhanced by incorporating the power of EEG sub-bands or auto-

regressive models PCA and GA were compared for their ability to

select features by applying various linear and nonlinear methods

for classi1047297cation The maximum accuracy was 9867 Pachori [16]

decomposed EEG signals into intrinsic mode function (IMF) using

EMD and then computed mean frequency for each IMF by Fourierndash

Bessel expansion [20] to differentiate seizure and non-seizure EEG

signals Zhang et al [21] proposed an approach for the pattern

recognition of four complicated hand activities such as grasping

a football a small bar a cylinder and a hard paper based on EEG

signal in which each piece of raw data sequence for EEG signal is

decomposed by wavelet transform After forming a matrix they

applied singular value decomposition (SVD) on the matrix to 1047297ndthe singular value feature vectors These features are used as input

to arti1047297cial neural network to discriminate four hand activities and

they got 8900 classi1047297cation accuracy

Recently Bajaj et al [18] extracted amplitude modulation and

frequency modulation bandwidth as features from IMF using EMD

Among the existing contemporary techniques Bajaj et als tech-

nique is the latest and the best in terms of performance They used

the LS-SVM [23] technique for the classi1047297cation of seizure and

non-seizure EEG signals using the small dataset [19] They

obtained 980 to 995 accuracy using radial basis function (RBF)

kernel and also obtained 995 to 100 accuracy using Morlet

kernel

We have observed that the existing methods [11ndash18] exhibit

almost perfect classi1047297cation accuracy for the small dataset of

Fig 2 Gamma frequencies which provide the maximum amplitude for ictal and interictal signals (from a standard dataset [22])

MZ Parvez M Paul Neurocomputing 145 (2014) 190ndash 200192



seizure and non-seizure EEG signals However their performance

is below the expected results using the large dataset [22] for ictal

and interictal EEG signal classi1047297cations For example when we

applied the Bajaj et als [18] feature extraction and classi1047297cation

method to differentiate ictal and interictal EEG signals for the 1047297rst

IMF in the large dataset from Epilepsy Center of the University

Hospital of Freiburg [22] the classi1047297cation accuracy is 7790 for

Frontal lobe and 8020 for Temporal lobe which is far below the

accuracy 9850 obtained using the small dataset [19] of seizureand non-seizure EEG signals

The paper is organized as follows the details of the proposed

four methods with dataset feature extractions classi1047297cation

techniques and computational time are described in Section 3

the detail experimental results are explained in Section 4 while

Section 5 concludes the paper

3 Proposed methods

In this paper we propose four methods based on the various new

features extracted from DCT DCTndashDWT SVD and IMF We use the

transformation and decompositions to separate high frequency

components and then apply innovative ways to extract different

statistical features When we extract features from the high fre-quency component (ie IMF and detail signals respective) using EMD

and DWT we take total length of the high frequency components

and we do not use any weighting for them as all the coef 1047297cients

represent the high frequency components As SVD and DCT provide

coef 1047297cients of all frequencies we need to select a subset of the

coef 1047297cients which can represent high frequency components We

investigate the strength of each transformation and decomposition

for the capability of classi1047297cation accuracy as well as the combined

strength through different feature extractions The dataset individual

method their corresponding rationality and detailed methodology of

feature extractions are described in the following sub-sections

31 Dataset

Small Dataset [19] The existing seizure and non-seizure feature

extraction and classi1047297cation schemes [11ndash18] except [13] used

small dataset [19] The dataset consists of 1047297ve subsets each subset

contains 100 single-channels recoding and each recoding has

236 s in duration captured by the international 10ndash20 electrode

placement scheme ie 32 electrodes [14] Among them two

subsets are taken from healthy volunteers two subsets are taken

from seizure free intervals and another subset is also taken during

seizure period [18] (see sample examples in Fig 1(a)) Ocak [13]

used different datasets with the same pattern of small dataset

where 300 were normal and 100 were epileptic patients

Large Dataset [22] Seizure and brain discharges are much more

complicated than what can be seen in the scalp Thus in the paper we

have used intracranial EEG dataset using grid strip and depth electrodes

rather than scalp EEG The dataset used in the paper was recorded at

Epilepsy Centre of the University Hospital of Freiburg Germany [22] The

database contains invasive EEG recordings of 21 patients suffering frommedically intractable focal epilepsy The data obtained by Neuro1047297le NT

digital video EEG system with 128 channels 256 Hz sampling rate and

16 bit analog-to-digital converter According to the dataset [22] epileptic

EEG signals can be classi1047297ed into (i) ictal (ie seizure) (ii) preictal (a

certain period before seizure onset) (iii) interictal (period between

seizures) and (iv) non-seizure (period without any seizure activity after

a certain time of interictal) Normally duration of ictal is varied from few

seconds to 2 min The ictal records contain epileptic seizures with at

least 50 min of preictal data preceding each seizure The median time

period between the last seizure and the interictal data set is 5 h and

18 min and the median time period between the interictal data set and

the 1047297rst following seizure is 9 h and 36 min [29] As suggested in the

dataset we have treated preictal and ictal period as an ictal signals in

our experiments We do not consider any non-seizure signals into the

interictal period as we carefully select the same duration of interictal

signal (with the ictal signal) from the above duration of interictal signal

32 Feature extraction using DCT

DCT is a transformation method for converting a time series

signal into basic frequency in such a way that the DCT coef 1047297cients

are arranged from low frequency to high frequency components

Low frequency components represent the coarse signals and high

frequency components represent the detail signals DCT is widely

used in the image processing and video coding areas to compress

image signals based on their frequencies [34] As the ictal and

interictal EEG signals have different amplitudes and frequencies

(not visually separable) thus the most distinguishable featuresshould be located in the high frequency components of DCT

coef 1047297cients see in Fig 3(a) As the EEG signal is non-linear and

non-stationary in nature thus for real time processing of EEG

signals DCT may not be correct to directly correspond to the

frequency analysis however if we segment the EEG signals in time

window and apply DCT on them to 1047297nd DCT coef 1047297cients we can

avoid non-linear and non-stationary nature of the signals In our

experiment we apply DCT on the benchmark dataset [22] and take

Fig 3 (a)High frequency DCT coef 1047297cients and their energy and entropy for the signals of Temporal lobe the 1047297rst two rows show the high frequency DCT coef 1047297cients (ie

25) of ictal and interictal signals while the last row shows the energy and entropy of using the high frequency DCT coef 1047297cients (ie 25) (b) High frequency DWT

coef 1047297cients and STDs of high frequency DCT and DWT coef 1047297cients for the EEG signals of Temporal lobe the 1047297rst two rows show that the high frequency DWT coef 1047297cients of

ictal and interictal signals while the last row shows the STD of the high frequency DCT and DWT coef 1047297cients of ictal and interictal EEG signals




the higher frequency component (ie the last quartile (Q4) ie last

25 of DCT coef 1047297cient) Then we extract statistical features such as

entropy and energy of the weighted higher frequency coef 1047297cients to

classify ictal and interictal signals

The extraction of the statistical features from the Q4 is not

straightforward We use heuristics to provide different weight to

the different coef 1047297cients and investigate the classi1047297cation accu-

racy Although we try different type of weighting approximations

the experimental results show that exponential weight performswell The weight w i for the i-th position coef 1047297cient is selected as

wi frac14 β e β xi where xi x j frac14 1

Number of Selected Coeff icients We also try

different β and test for the classi1047297cation results We 1047297nd that

β frac142 provides the best results for the dataset Fig 4 shows an

example of weights According to the proposed weight we provide

more weight to the relatively low frequency coef 1047297cients and less

weight to the relatively high frequency coef 1047297cients within the

higher frequency area of the overall signal as sometimes the high

frequency coef 1047297cients in the tail would be very small

We de1047297ne weighted entropy and energy as follows

Energy frac14 sumn

i frac14 1

W i X ietht THORN

2and Entropy frac14 sum

n

i frac14 1

W iY 2i etht THORNlnethY 2i etht THORNTHORN eth1THORN

where n is the length of signal X (t ) is the higher frequency DCTcoef 1047297cients and Y (t ) is the normalized value of X (t )

The representation of ictal and interictal data for different higher

frequency DCT coef 1047297cients and their entropy and energy are given in

Fig 3(a) It can be observed that the changing rate of frequencies is

higher for interictal signal than ictal signal from Temporal lobe It is

also interesting to observe that the amplitude of the higher frequency

DCT coef 1047297cients for ictal signals is larger compared to that of interictal

signals Note that although the original ictal and interictal EEG signals

are not visually separable the high frequency DCT coef 1047297cients of ictal

and interictal signals are visually separable The third row of Fig 3

(a) also demonstrates that the weighted energy and entropy extracted

from the higher frequency DCT components have different values for

ictal and interictal signals Thus weighted entropy and energy would

be effective features for ictal and interictal signals classi1047297

cation

33 Feature extraction using DCT ndashDWT

DWT is a transformation and widely used in the image and signal

processing research to decompose the imagesignals into different

frequency and bands DWT can be speci1047297ed by low pass (LP) and high

pass (HP) 1047297lters named as standard quadrature mirror 1047297lters [28] The

cut-off frequency of these 1047297lters is one-fourth of the sampling

frequency The bandwidth of the 1047297lter outputs is half the bandwidth

of the original signal which generates downsampled output signals

without losing any information according to the Nyquist theorem [13]

The down-sampled signals from LP and HP 1047297lters refer to 1047297rst-level

approximation and details of the original signal respectively [24] DWT

is sometimes more effective compared to DCT to separate signals into

low and high frequency components when the signal is longer Thus

we also investigate the effectiveness of the DWT to extract some other

statistical features (such as STD) to classify ictal and interictal signals

In our second approach we apply single-level one dimensional DWT

on the EEG signal to 1047297nd the high frequency components from the

signal Then we calculate the STD from the detail frequency (ie highfrequency) components of the DWT coef 1047297cients and use as a feature

We use the whole high frequency component for feature extraction as

DWT separates the signals into low and high frequency components

Another feature is extracted using the STD of the weighted high

frequency DCT coef 1047297cients (similar to Fig 4) Two STD features from

DCT and DWT higher frequency components are then used for the

classi1047297cation purpose Note that we use different primary functions of

the wavelet transformation including Daubechies (db) series We 1047297nd

that db16 is the best for our experiment

In the experiment we take only the high frequency DWT compo-

nents The 1047297rst two rows in Fig 3(b) show the higher change of

frequency and lower amplitude for interictal signal compared to ictal

signal from Temporal lobe The third row of Fig 3(b) represents the

STDs generated from the high frequency DCT coef 1047297cients (weighted)

and DWT for ictal which are higher than that of interictal signal Thus

the STDs extracted from the high frequency DCT (weighted) and DWT

coef 1047297cients can be good features for classi1047297cation

34 Feature extraction using SVD

SVD decomposes two dimensional signals into three matrixes in

such a way that the middle matrix (ie singular value) has unique

properties to separate signals The middle matrix containing the

square root of eigenvalues and diagonalized contains much less non-

zero coef 1047297cients compared with the coef 1047297cient matrices of other

transformations [35] The values of diagonal matrix are with des-

cending order from top-left to bottom-right As the original ictal and

interictal signals are almost of the same nature we do not expect asigni1047297cant different values of other two matrixes after SVD however

we expect some distinguishable values on the extreme part (ie

bottom-right) of diagonal matrix Thus any feature extracted from

the bottom-right singular values could be used to classify ictal and

interictal signals To the best of our knowledge we are the 1047297rst to use

two dimensional SVD to analyze EEG signals for seizure detection

To extract the features using SVD we reshape the EEG signals

into a two dimensional matrix The column vector of EEG signal is

rearranged to square matrix for the computation of singular value

components using SVD From the singular values we take the

weighted Q4 (weight is similar to Fig 4 but the number of selected

coef 1047297cients are different) of the non-zero diagonal values to

calculate the STDs and energy and treat as the two features to

classify ictal and interictal signals SVD allows transforming any

given matrix AAC mxn to diagonal form using unitary matrices ie

A frac14 U Σ V H where Amn is a matrix U mm and V nnare orthogonal

matrix and Σ mn matrix with non-negative diagonal entries

The columns of U are called left singular vectors and the rows

of V H are called right singular vectors If A is a rectangular m n

matrix of rank K then U will be m m and Σ will be

Σ frac14

λ1 0 ⋯ 0

0 λ2 ⋯ 0

⋮ ⋮ ⋱ ⋮

0 0 ⋯ λn

⋮ ⋮ ⋮

0 0 ⋯ 0

2666666664

3777777775

eth2THORN

where λ1Z λ 2Zhellip λn-1 Z λnFig 4 Weight for different coef 1047297cients of selected high frequency coef 1047297cients




The data distribution of non-zero singular values of the ictal

and interictal signals from the Frontal lobe is shown in Fig 5(a)

The top part of the 1047297gure clearly shows that the Q4 singular values

of interictal signals are larger than that of the ictal signals The

bottom part of Fig 5(a) shows the STD and energy extracted from

the weighted Q4 of non-zero singular values from ictal and

interictal signals respectively The 1047297gure exposes that the STDs

extracted from the ictal and interictal signals have distinguishable

values thus they can be used for ictal and interictal signal

classi1047297cation

35 Feature extraction of IMF using EMD

The main strength of EMD is its ability to analyze nonlinear and

non-stationary signals like EEG signals When we apply EMD on

a signal the signal is decomposed into a 1047297nite number of signals

into IMFs which are ordered from higher frequency component to

lower frequency component The number of IMFs in a signal

depends on the local characteristics of the signal rather than

pre-de1047297ned number of IMFs This feature makes each IMF crucial

to identify some characteristics of the original signal Since the

IMFs are the representations of different frequenciesamplitudes

of the original signal and the distinguishing features between ictal

and interictal EEG signals lie on the frequencyamplitude our

hypothesis is that any feature extracted from IMFs will be an

effective feature to classify ictal and interictal successfully We also

observe that the ictal and interictal signals are deferred in higherorder frequency components thus our other hypothesis is that

features extracted from the 1047297rst few IMFs should be enough to

classify ictal and interictal signals successfully Through the

decomposition we keep only few mode functions of the original

signals for the 1047297nal feature extraction however we are able to

keep the important mode functions for the classi1047297cation purpose

Experimental results reveal that our both hypothesis are correct as

extracted features from the 1047297rst IMF successfully classify ictal and

interictal signals (see Section 4) Thus IMFs can be used in

classi1047297cation applications where the original signals are non-

linear and non-stationary and distinguishing features exist on

the different frequenciesamplitudes

Each IMF satis1047297es two following conditions (i) the number of

extrema and the number of zero crossing are identical or differ at

most by one and (ii) the mean value between the upper and the

lower envelope is equal to zero at any time

The EMD algorithm can be summarized as follows

1 Extract the extrema (minima and maxima) of the signal xetht THORN

2 Interpolate between minima and maxima to obtain

ℓminetht THORN and ℓmaxetht THORN

3 Calculate local mean metht THORN frac14 ℓminetht THORN thornℓmaxetht THORNfrac12 =2

4 Extract the detail detht THORN frac14 xetht THORN metht THORN

5 Check detht THORN is an IMF according to the conditions which are

mentioned above If yes repeat the procedure from the step1 on the residual signal r etht THORN frac14 xetht THORN metht THORN If no replace xetht THORN with

detht THORN and repeat the procedure from step 1

The pictorial scenario of generating IMFs is shown in Fig 6 The

signal xetht THORNcan be represented as a combination of IMFs and

residual component

xetht THORN frac14 sumL

i frac14 1

dietht THORN thornr Letht THORN eth3THORN

where di and r L are the i-th IMF and L-th is residual signal

We observe that features extracted from the 1047297rst two IMFs

provide the best classi1047297cation results To reduce computational

time we consider only the 1047297rst IMF from the EMD and then

calculate STD of IMF to capture the variation of the signal Byignoring other IMFs we do not sacri1047297ce any signi1047297cant classi1047297cation

accuracy The strength of a signal is distributed in the frequency

domain relative to the strengths of other ambient signals is central

to the design of any 1047297lter intended to extract or suppress the signal

To 1047297nd the strength of the signal we apply power spectral density

(PSD) on the 1047297rst IMF Then determine the STD feature from

normalized PSD The calculation of PSD is as follows

P ethwiTHORN frac14 1

N DethwiTHORN 2

eth4THORN

where DethwiTHORNis the discrete Fourier transform of IMFdietht THORN Normalized

PSD can be de1047297ned as

pi frac14 P ethwiTHORN=sum

i

P ethwiTHORN eth5THORN

Fig 5 (a) Non-zero singular value of SVD for Frontal lobe The 1047297rst row shows that the non-zero singular value distribution for ictal and interictal signals from Frontal lobe

and the last row shows the STD and energy of the last 25 non-zero singular values of ictal and interictal EEG signals (b) The 1047297rst IMFs of ictal and interictal signals with the

extracted features the top two rows represent the 1047297rst IMF from the ictal and interictal signals of Temporal lobe bottom row represents STD of the 1047297rst IMF and STD of the

normalized PSD (power spectral density) of the same IMF of ictal and interictal EEG signals




In the experiment we extract two features such as STD of the

1047297rst IMF and STD of normalized PSD of the 1047297rst IMF From the 1047297rst

two rows in Fig 5(b) it can be observed that the 1047297rst IMF of ictal

signal carries the higher amplitude compared to the interictal

signal

The third row of the 1047297gure represents the STD of the 1047297rst IMF

and STD of PSD of the same IMF from using ictal and interictal

signals The STDs for both cases of interictal signal are signi1047297cantly

larger than that of ictal signal Thus the two new features

extracted by EMD method can be good features for classi1047297cation

Table 1 presents the summary of all corresponding features use

for ictal and interictal classi1047297cation by each method

36 Classi 1047297er

We extract a number of features from different transforma-

tionsdecompositions To classify ictal and interictal signals we

need to use a classi1047297er The goal of a classi1047297er is to 1047297nd patients

states such as ictal (class 1) and interictal (class 2) using machine

learning approaches with cross-validation The challenge is to 1047297nd

the mapping that generalizes from training sets to unseen test

sets For the cross-validation data are partitioned into training set

and test set In our experiment we use 4-fold cross validation

where each dataset was randomly divided into 4 splits where

three of them were used for training and the fourth one used for

testing

To classify the ictal and interictal signals we extract various

features from DCT DWT SVD and IMF of EMD For classi1047297cation we

use SVM-based classi1047297er as the SVM is one of the best classi1047297ers in

the EEG signal analysis SVM is a potential methodology for solving

problem in linear and nonlinear classi1047297cation function estimation

and kernel based learning methods [25] It can minimize the

operational error and maximize the margin hyperplane as a result

it will maximize the classi1047297cation performance [25] LS-SVM is the

extended version of SVM and it is closely related to regularization

networks and Gaussian process and it also has primal-dual inter-

pretations [23] The major drawback of SVM is in its higher

computational burden of the constrained optimization program-

ming However LS-SVM can solve this problem [26]

The classi1047297er is a LS-SVM which learns nonlinear mapping

from the training set features xifrac141hellipnT where nT is the number

of training features into the patients state ictal ( thorn1) and interictal( 1) For unbiased classi1047297cation results [27] we divide the whole

1000 trials into four subsets in each subset we randomly select

75 samples for the validation training set and the remaining 25

for the validation testing set Let f yigi frac14 C 12 designate the LS-SVM

validation test outputs mapping to class 1 or class 2 The equation

of LS-SVM can be de1047297ned in [18] as

f eth xTHORN frac14 sign sumN

i 1

α i yiK eth x xiTHORN thornc

eth6THORN

where K ( x xi) is a kernel function α i are the Lagrange multipliers c

is the bias term xi is the training input and yi is the training

output pairs Linear RBF and Morlet kernel are used in our

experiments and RBF and Morlet functions can be de1047297ned in [18]

Fig 6 Extracted IMFs by EMD from Temporal lobe (Patient 12 channel one 15th datablock for ictal and 35th datablock for interictal)

Table 1Extracted features of proposed and existing methods

Proposed methods Feature-1 Feature-2

Q4WHF_SVD (Quartile four weighted high frequency component using SVD ) STD on 25 weighted high frequency

singular values

Energy on 25 weighted high frequency

singular values

Q4WHF_DCT (Quartile four weighted high frequency component using DCT ) Energy on 25 weighted high

frequency DCT coef 1047297cients

Entropy on 25 weighted high frequency DCT

coef 1047297cients

FIMF_EMD (First IMF using EMD ) STD on the 1047297rst IMF (ie high

frequency components)

STD on normalized PSD

Q4WHF_DCT ndash HF_DWT (Quartile four weighted high frequency component

using DCT and high frequency from DWT)

STD on 25 weighted high frequency

DCT coef 1047297cients

STD on detail signals (ie high frequency

components) after apply DWT

Combined Combined all above features

Existing methods Feature-1 Feature-2

EMD[18] Amplitude modulation bandwidth Frequency modulation bandwidth

DCT [17] Features from 6 of quantized DCT coef 1047297cients at lower frequency




as

keth x xiTHORN frac14 expeth x xi2=2s

2THORN eth7THORN

where s controls the width of the RBF function

keth x xiTHORN frac14 prodd

k frac14 1

cos ω0etheth xk xki THORN=aTHORN

h iexpeth xk xk

i =2a2THORN eth8THORN

where d is the dimension a is the 1047298exible coef 1047297cient and xki is the

k-th component of i-th training data Matlab codes for LS-SVMclassi1047297er are available at httpwwwesatkuleuvenacbesista

lssvmlab

37 Computational time

Feature extractions using IMF through EMD take longer time

due to two-fold iterations to 1047297nd all IMFs In the 1047297rst iteration

EMD decomposes a signal and checks whether the signal makes an

IMF and in the second iteration it subtracts the current IMF from

the signal and continues for the next IMF Even if we stop the

iteration for the 1047297rst IMF the EMD-based technique takes a full

1047297rst iteration for calculating the IMF The experimental data

reveals that the proposed technique using DCT DWT and SVD

takes less computational time compared to the EMD-based tech-nique as DCT DWT and SVD transformation is faster compared to

the EMD decomposition Thus the proposed methods (except

FIMF_EMD method ie First IMF using EMD) take insigni1047297cant

time compared to the existing method [18]

4 Experimental results

Sometimes EEG signals have line noise and other kinds of

artifacts due to muscle and body movements Notch 1047297lter and

independent component analysis (ICA)-based method are recom-

mended to remove the line noise and artifacts respectively for

suf 1047297cient amount of EEG signals [30ndash32] However in this experi-

ment we do not use any explicit noiseartifacts removal technique

to see the tolerance of the proposed method against noise

artifacts

We propose four methods and the experimental results of the

proposed methods are compared with existing methods [1718]

and corresponding features are presented in Table 1Our ultimate

target is to classify ictal and interictal EEG signals using LS-SVM

with different kernels such as RBF and Morlet where the values of regularization parameter and kernel parameter are 10 and 6

respectively for both kernels We also use linear kernel In this

paper four methods are proposed based on the new features of

ictal and interictal EEG signals captured from Frontal and Temporal

lobes using DCT DWT SVD and IMF of EMD to see the strength of

individual transformationdecomposition techniques for seizure

detection Then we also test the classi1047297cation accuracy by combin-

ing all features In our experiment we use 200 signals from ictal

and 800 signals from interictal EEG signals After training and

testing of all features we calculate average value from the four

subsets to compute sensitivity speci1047297city and accuracy [18] The

sensitivity speci1047297city and accuracy are de1047297ned as

Sensitivity frac14 ethTP =ethTP thornFN THORNTHORN 100 eth9THORN

Specificity frac14 ethTN =ethTN thornFP THORNTHORN 100 eth10THORN

Accuracy frac14 ethethTP thornTN THORN=ethTP thornTN thornFP thorn FN THORNTHORN 100 eth11THORN

where TP and TN represent the total number of detected true

positive events and true negative events respectively The FP and

FN represent false positive and false negative respectively

The technique in [18] claims that the second IMF provides

better classi1047297cation results while they use frequency and ampli-

tude modulation bandwidth features for the dataset [19] We

conduct experiments using 1047297rst four IMFs however we provide

results using only the 1047297rst IMF presented in Table 2 as it carries the

Table 2

Sensitivity speci1047297city and accuracy for different features of ictal and interictal EEG signals from Frontal and Temporal lobes

Classi1047297er Brain location Criteria Existing method Proposed method

Kernel Lobe EMD[18] DCT[17] Q4WHF_SVD Q4WHF_DCT FIMF_EMD Q4WHF_DCT ndashHF_DWT Combined

RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385

Morlet Frontal SEN 3382 000 6383 8889 9333 6000 2442

SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200

Linear Frontal SEN 2895 2728 8095 100 100 100 8750

SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415




best result compared to other IMFs For the RBF kernel function

the proposed methods outperform the state-of-the-art method

consistently and the average sensitivity using the proposed meth-

ods such as Quartile four weighted high frequency component using

SVD (Q4WHF_SVD) Quartile four weighted high frequency compo-

nent using DCT (Q4WHF_DCT) First IMF using EMD (FIMF_EMD)

and Quartile four weighted high frequency component using DCT and

high frequency from DWT (Q4WHF_DCTndashHF_DWT) are 8333

9912 9647 and 8713 for RBF kernel respectively whereas

the average sensitivity using the state-of-art-methods [17] and

[18] are 635 and 4804 respectively Using Morlet kernel the

average sensitivity of the proposed methods is higher compared to

the existing methods [17] and [18] Moreover we justify our result

applying linear kernel Table 2 also presents that in most of the

cases the proposed methods with linear kernel provide better

results compared to the proposed methods with Morlet and RBF

kernels in terms of sensitivity Table 2 also presents that the

proposed Q4WHF_DCT method is the best among all proposed

methods in terms of sensitivity It is also interesting to note that

the accuracy of the proposed Q4WHF_DCTndashHF_DWT method is

reasonably consistence in terms of three criteria for RBF and

Morlet kernel According to Table 2 all proposed methods always

outperform the existing methods [1718] in terms of sensitivity (i

e seizure and ictal detection rate) however in some cases the

existing methods [1718] outperform one or more proposed

methods in terms of speci1047297city (interictal detection rate) For

seizure detection sensitivity is more important compared to the

speci1047297city detection as the cost of failure detecting ictal is heavier

compared to interictal In overall classi1047297cation the proposed

methods are also better compared to the existing methods We

combine all features to classify ictal and interictal EEG signalsHowever combining is not good because LS-SVM may not prop-

erly combine all available features to make a good prediction

model The average sensitivity of the proposed methods is 9136

(average result of all kernel and lobe) whereas the sensitivities of

the state-of-the-arts methods are 5461 [18] and 799 [17]

respectively

The performance of the LS-SVM is evaluated by (ROC) plot

shown in Fig 7(a) ROC illustrates the performance of a binary

classi1047297er system where it is created by plotting the fraction of true

positives from the positives ie true positive rate (TPR) vs the

fraction of false positives from negatives ie false positive rate

(FPR) with various threshold settings TPR is known as sensitivity

and FPR is one minus the speci1047297city or true negative rate Fig 7(a)

shows that the proposed four methods are carrying good

classi1047297cation results than that of the state-of-the-art method

[18] using dataset from Frontal lobe of the third subset of training

and testing EEG signals

Fig 7(b) shows the distribution of normalized feature values

(ie energy amp entropy in the Q4WHF_DCT method STD in the

Q4WHF_DCTndashHF_DWT method STD amp energy in the Q4WHF_SVD

method and STD in the FIMF EMD method) of ictal and interictal

from the Frontal lobe dataset [22] The 1047297gure shows that the range

of feature values for ictal is different from that of the interictal

signals This phenomenon is the other evidence why we use those

features to classify ictal and interictal EEG signals in the proposed

methods

First column of Fig 8 shows classi1047297cation comparison using the

proposed methods (ie FIMF EMD and Q4WHF_DCTndashHF_DWT)

and the state-of-the-art method [18] Fig 8(b) and (c) shows the

LS-SVM classi1047297cation results using the proposed FIMF EMD and

Q4WHF_DCTndashHF_DWT against the state-of-the-art method (ie

EMD Fig 8(a)) We generate 1047297rst column of Fig 8 from Frontal lobe

for the second subset of training and testing and obtain the

classi1047297cation accuracy 9120 for the Q4WHF_DCTndashHF_DWT

method 8720 for the FIMF_EMD method and 8080 for the

EMD method It can be concluded from these 1047297gures that the

proposed method based on the DCTndashDWT and the IMF outperform

the state-of-the-art method (ie EMD) The same classi1047297cation

comparisons are also provided in second column of Fig 8 using the

state-of-the-art method based on the EMD and the proposed

method based on the SVD and DCT respectively We consider

Temporal lobe for the fourth subset of training and testing obtain

classi1047297cation accuracy 9120 for Q4WHF_SVD 8960 for

Q4WHF_DCT and 8520 for EMD [18]Thus it is concluded that

the proposed methods based on DCT and SVD also outperform theEMD method [18]

5 Conclusion

Unlike seizure and non-seizure EEG signals classi1047297cation of

ictal and interictal EEG signals is a challenging task due to non

abrupt phenomena (ie visually non-separable) between ictal and

interictal EEG signals Moreover the features of ictal and interictal

signals are not consistence in different brain locations patient

types of seizure and hospital setting during epileptic period of a

patient Thus the classi1047297cation performance for ictal and interictal

EEG signals using the existing features extraction and classi1047297cation

techniques is not at expected level Our initial observation

Fig 7 (a) The receiver operating characteristics (ROC) curves of the third subset of training and testing EEG signals by three proposed methods ( Q4WHF_SVD FIMF_EMD

and Q4WHF_DCTndashHF_DWT) against the EMD method using LS-SVM with RBF kernel from Frontal lobe and (b) Normalized distribution of feature values extracted from ictal

and interictal EEG signals using DCT DWT SVD and IMF based method where 200 samples are taken from each of ictal and interictal categories in Frontal lobe




indicates that high frequency components of EEG signals have

distinguished characteristics To improve the accuracy in this

paper we propose novel feature extractions strategies from

the high frequency components based on a number of estab-

lished transformation and decompositions The average sensi-

tivity (which is the measure for seizure) of the proposed

methods is 9136 (average result of all kernel and lobe)

whereas the sensitivity of the state-of-the-arts methods are

5461 [18] and 799 [17] respectively The experimental

results show that the proposed methods outperform the state-

of-the-art methods in terms of accuracy sensitivity and speci-

1047297city for the ictal and interictal EEG signals with greater

consistence from the large benchmark dataset in two different

brain locations

Fig 8 First column represents the classi1047297cation of ictal and interictal EEG signals from Frontal lobe for the second subset of training and testing using (a) the state-of-the-art

method against (b) the proposed IMF and (c) the DCT-DWT method Second column represents the classi1047297cation of ictal and interictal EEG signals from Temporal lobe for the

fourth subset of training and testing using (d) the state-of-the-art method against (e) the proposed SVD and (f) the DCT methods




References

[1] Neurology Applied How Science is Bringing Music Instruction Back toExpressive Development URL langhttpwwwthomasjwestmusiccomneurolo

gyappliedhtmrang (accessed 270812)[2] EEG Signal The science behind Bo-Tau URL langhttpwwwbo-taucoukindex

phpaction=the_science_behind_bo_tau rang (accessed 270812)[3] Epilepsy-SeizuresURL langhttpwwwhealthcommunitiescomepilepsy-sei

zuresabout-seizuresshtml rang (accessed 270812)[4] M Soleymani T Pun M Pantic Multi-modal emotion recognition in response

to videos IEEE Trans Affect Comput 3 (2) (2012) 211ndash223[5] S Scholler S Bosse MS Treder B Blankertz G Curio K-R Muumlller

T Wiegand Toward a direct measure of video quality perception using EEGIEEE Trans Image Process 21 (5) (2012) 2619ndash2629

[6] W Di C Zhihua F Ruifang L Guangyu and L Tian Study on human brainafter consuming alcohol based on EEG signal in Proceedings of the Interna-tional Conference on Computer Science and Information Technology (ICCSIT)5 406ndash409 2010

[7] E Estrada H Nazeran F Ebrahimi and M Mikaeili EEG signal features forcomputer-aided sleep stage detection in Proceedings of the IEEE EMBSConference on Neural Engineering 669ndash672 2009

[8] ZM Hana1047297ah KFM Yunos ZH Murat MN Taib S Lias EEG brainwavepattern for smoking behaviour after horizontal rotation treatment in Pro-ceedings of the IEEE Student Conference on Research and Development 2009

[9] ZH Murat R Shilawani SA Kadir RM Isa and MN Taib The effects of

mobile phone usage on human braingwave using EEG in Proceedings of the13th International Conference on Modelling and Simulation 2011

[10] K Lehnertz RG Andrzejak J Arnhold T Kreuz F Mormann C Rieke

G Widman CE Elger Nonliner EEG analysis in Epilepsy its possible use forinterictal focus localization seizure ancipation and prevention J ClinNeurophsysiol 18 (3) (2001) 209ndash222

[11] R Panda PS Khobragade PD Jambhule SN Jengthe PR Pal and TK GandhiClassi1047297cation of EEG signal using wavelet transform and support vectormachine for epileptic seizure diction in Proceedings of the InternationalConference on Systems in Medicine and Biology (ICSMB) 405ndash408 2010

[12] SG Dastidar H Adeli N Dadmehr Mixed-band wavelet chaos-neural net-

work methodology for epilepsy and epileptic seizure detection IEEE TransBiomeical Eng 54 (9) (2007) 1545ndash1551

[13] H Ocak Optimal classi1047297cation of epileptic seizures in EEG using waveletanalysis and genetic algorithm Signal Process 88 (2008) 1858ndash1867

[14] K Polat S Gunes Classi1047297cation of epileptiform EEG using a hybrid systembased on decision tree classi1047297er and fast Fourier transform Appl Math

Comput 187 (2) (2007) 1017ndash1026[15] SF Liang HC Wang WL Chang Combination of EEG complexity and spectral

analysis for epilepsy diagnosis and seizure detection EURASIP J Adv SignalProcess 853434 (2010) 1ndash15 (Article ID)

[16] RB Pachori Discrimination between ictal and seizure-free EEG signals usingempirical mode decomposition Res Lett Signal Process 293056 (2008) 1ndash5(Article ID)

[17] D Birvinskas V Jusas I Martisius and R Damasevicius EEG dataset reductionand feature extraction using discrete cosine transform in Proceedings of theEuropean symposium on computer modeling and simulation (EMS) 199-2042012

[18] V Bajaj RB Pachori Classi1047297cation of seizure and non-seizure EEG signalsusing empirical mode decomposition IEEE Trans Inf Technol Biomed 16 (6)(2012) 1135ndash1142

[19] Small dataset Epilepsy dataset (text format) langhttpepileptologie-bonndecmsfront_contentphpidcat=193amplang=3ampchangelang=3rang (accessed 280412)

[20] J Harrison Fast and accurate bessel function computation in Proceedings of the19th IEEE international symposium on computer arithmetic 104ndash113

2009[21] X Zhang W Diao Z Cheng Wavelet transform and singular value decom-

position of EEG signal for pattern recognition of complicated hand activitiesLect Notes Comput Sci Digit Hum Model 4561 (2007) 294ndash303

[22] Large dataset EEG Data set from Epilepsy Center of the University Hospital of

Freiburg langhttpepilepsyuni-freiburgdefreiburg-seizure-prediction-projecteeg-databaserang (accessed 100612)

[23] KD Brabanter P Karsmakers F Ojeda C Alzate and JD Brabanter KPelckmans B De Moor J Vandewalle JAK Suykens LS-SVMlab ToolboxUsers Guide version 18 Katholieke Universiteit Leuven 2011

[24] S Sanei JA Chambers EEG Signal Processing John Wiley amp Sons Ltd UK

2007 (Centre of Digital Signal Processing Cardiff University)[25] S Abe Support Vector Machine for Pattern Classi1047297cation Second Edition

Springer London Dordrecht Heidelberg New York 2010

[26] H Wang and D Hu Comparison of SVM and LS-SVM for regression inProceedings of the international conference on neural networks and brain 1279ndash283 2005

[27] H Xing M Ha B Hu D Tian Linear feature-weighted support vectormachine Fuzzy Inf Eng 1 (3) (2009) 289ndash305

[28] A Subasi EEG signal classi1047297cation using wavelet feature extraction and amixture of expert model Expert Syst Appl 32 (4) (2007) 1084ndash1093(Elsevier)

[29] JR Williamson DW Bliss DW Browne JT Narayanan Seizure predictionusing EEG spatiotemporal correlation structure Epilepsy Behav 25 (2) (2012)230ndash238

[30] TP Jung S Makeig C Humphries et al Removing electroencephalographicsartifacts by blind source separation Psychophysiology 37 (2000) 163ndash178

[31] W De Clercq A Vergult B Vanrumste WV Paesschen SV Huffel Canonicalcorrelation analysis applied to remove muscle artifacts from the electroence-phalogram IEEE Trans Biomeical Eng 53 (12) (2006) 2583ndash2587

[32] J Ma P Tao S Bayram V Svetnik Muscle artifacts in multichannel EEGcharacteristics and reduction ClinNeurophysiol 123 (8) (2012) 1676ndash1686

[33] GA Worrell L Parish SD Cranstoun R Jonas G Baltuch B Litt High-frequency oscillations and seizure generation in neocortical epilepsy Brain127 (2004) 1496ndash1506

[34] M Paul W Lin CT Lau BndashS Lee A long term reference frame for hierarchicalb-picture based video coding IEEE Trans Circuits Syst Video Technol (2014)httpdxdoiorg101109TCSVT20142302555

[35] Z Gu W Lin CT Lau BndashS Lee and M Paul Two dimensional singular valuedecomposition (2D-SVD) based video coding in Proceedings of the IEEEconference on image processing (IEEE ICIP-10) 181ndash184 2010

Mohammad Zavid Parvez received BScEng (hons)Degree in Computer Science and Engineering fromAsian University of Bangladesh in 2003 and MScDegree in Electrical Engineering with emphasis onSignal Processing from Blekinge Institute of TechnologySweden in 2010 Parvezs research interests includebiomedical signal processing and machine learning

He has more than three year industry experience asa software developer Moreover he was involved sev-eral software projects in Denmark Currently he is aPhD student and teaching staff in Charles Sturt Uni-versity

Parvez is a Graduate student member of IEEE He hasreceived full funded scholarship from the Faculty He has published several journaland conference papers in the area of EEG signal analysis and cognitive radio

Manoranjan Paul received BScEng (hons) Degree inComputer Science and Engineering from BangladeshUniversity of Engineering and Technology (BUET) Ban-gladesh in 1997 and PhD Degree from Monash Uni-versity Australia in 2005 He was an Assistant Professorin Ahsanullah University of Science and Technology Hewas a Post-Doctoral Research Fellow in the Universityof New South Wales in 2005ndash2006 Monash Universityin 2006ndash2009 and Nanyang Technological Universityin 2009ndash2011 He has joined in the School of Comput-ing and Mathematics Charles Sturt University (CSU) at2011 Currently he is a Senior Lecturer and AssociateDirector of the Center for Research in Complex Systems

(CRiCS) in CSUDr Paul is a senior member of the IEEE and ACS He is in editorial board of three

international journals including EURASIP Journal on Advances in Signal Processing(JASP) Dr Paul has served as a guest editor of Journal of Multimedia and Journal of Computers for 1047297ve special issues He has been in Technical Program Committees of more than 25 international conferences Dr Paul obtained more than A$1Mcompetitive grant money including Australian Research Council (ARC) DiscoveryProject He has published more than 65 refereed publications including journalsand conferences He organized a special session on ldquoNew video coding technolo-giesrdquo in IEEE ISCAS 2010 He was a keynote speaker on ldquoVision friendly videocodingrdquo in IEEE ICCIT 2010 and tutorial speaker on ldquoMultiview Video Codingrdquo inDICTA 2013 His major research interests are in the 1047297elds of video coding computervision and EEG Signals analysis Dr Paul received Vice Chancellor ResearchExcellence Award in Faculty level at 2013




Existing methods [11ndash18] used small epilepsy dataset [19] (for

detailed information of the dataset see Section 31) The small

dataset with duration 236 s has seizure (ie ictal) and non-seizure

signals which can be distinguished by their visual phenomena

such as magnitude of amplitude and changing rate of frequency

(see in Fig 1(a)) For the non-seizure signal the amplitude is low

and the frequency is high while the nature of seizure signal is

totally opposite (see Fig 1(a)) Fig 1(b) and (c) shows the ictal and

interictal EEG signals from a large dataset [22] The 1047297gure

demonstrates the non-abrupt phenomena (ie not easily distin-

guishable between ictal and interictal based on amplitude and

frequency) of the ictal and interictal signal for both cases of Frontal

and Temporal lobes compared to the dataset in [19] Moreover

EEG signals from different locations exhibit different phenomenal

activities for an ictal and interictal period Thus the classi1047297cation

of ictal and interictal EEG signals is challenging compared to that

of seizure (ie ictal) and non-seizure EEG signals It also demon-

strates that the characteristics of EEG signals are varied fromdataset to dataset

Birvinskas et al [17] used DCT (discrete cosine transformation) to

reduce the EEG signals and then extracted features from the

reduced signals to classify EEG signals They achieved good

classi1047297cation accuracy by keeping low frequency DCT coef 1047297cient

for feature extractions Worrell et al [33] claimed that the seizure

onset frequencies are in the range of gamma frequency ie high

frequency band (typically between 30 to 100 Hz) They explicitly

mentioned that seizure onset frequencies are on the range of

frequency 74376 137 and 23776 28 Hz for Frontal and

Temporal lobes respectively They also observed that dominant

spectral peaks are observed in the high frequency spectral com-

ponents for the Frontal lobe and in the lower gamma frequency for

the Temporal lobe during the seizure period Other literature such

as Panda et al [11] Ocak et al [13] and Ghosh-Dastidar et al [12]

used other frequency bands such as delta theta alpha and beta

with gamma band to extract features for EEG signal classi1047297cation

From the above 1047297ndings and the characteristics of the different

datasets we 1047297nd that the frequency range of seizure onset can be

varied based on the brain location seizure type (generalpartial)

patient and hospital setting (eg capturing machine quality

artifacts etc) However we observe that the signal has larger

spikes ie high amplitude in the ictal (ie seizure) period

compared to the interictal period Thus based on the temporal

and spatial features of raw EEG signals and the above 1047297ndings of

the recent literatures our hypothesis is that the distinguishing

features between ictal and interictal signals would be in the high

frequency spectral area irrespective of the brain location patient

type of seizure and hospital setting

To verify our hypothesis we perform an experiment to 1047297nd the

gamma frequency of signals which provides the maximum ampli-

tude using ictal and interictal signals in different brain locationsThe results for 200 EEG signals from ictal and interictal are shown

in Fig 2 The 1047297gure shows that relatively high gamma frequencies

ie high frequencies in the signals provide the maximum ampli-

tude of ictal compared to the interictal signals for both Frontal and

Temporal lobes from different patients We use the maximum

amplitude criteria for the gamma band selection in this analysis as

our observation and Worrell et als observation indicate that

dominant spectral peaks for the seizure onset are in the high

frequency (ie gamma band) Thus the features extracted from

gamma frequencies ie the high frequencies should provide better

classi1047297cation accuracy between the ictal and interictal irrespective

of brain location patients type of seizure and hospital setting

Based on the above mentioned observation in this paper we

proposed generic seizure detection techniques which use different

Fig 1 Ictal and interictal EEG signals from different datasets and different brain locations (a) Seizure and non-seizure EEG signals from the small dataset [19] the 1047297rst row

and remaining four rows indicated the seizure and non-seizure signals respectively [19] (b) Ictal (patient one 7th datablock channel one) and interictal (patient one 95th

datablock channel one) of Frontal lobe signals [22] and (c) Ictal (patient 1215th datablock and channel one) and interictal (patient 12 35th datablock and channel one) of

Temporal lobe signals [22]






















classi1047297cation






































































kernel





















3 Proposed methods
















31 Dataset












































































Energy frac14 sumn

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i frac14 1
















cation































































Σ frac14

λ1 0 ⋯ 0

0 λ2 ⋯ 0

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0 0 ⋯ λn

⋮ ⋮ ⋮

0 0 ⋯ 0

2666666664

3777777775

eth2THORN














classi1047297cation










































residual component


i frac14 1














N DethwiTHORN 2

eth4THORN




i













signal








36 Classi 1047297er











testing























i 1


eth6THORN









singular values


singular values




coef 1047297cients

















as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References







































































classi1047297cation






































































kernel





















3 Proposed methods
















31 Dataset












































































Energy frac14 sumn

i frac14 1

W i X ietht THORN


n

i frac14 1
















cation































































Σ frac14

λ1 0 ⋯ 0

0 λ2 ⋯ 0

⋮ ⋮ ⋱ ⋮

0 0 ⋯ λn

⋮ ⋮ ⋮

0 0 ⋯ 0

2666666664

3777777775

eth2THORN














classi1047297cation










































residual component


i frac14 1














N DethwiTHORN 2

eth4THORN




i













signal








36 Classi 1047297er











testing























i 1


eth6THORN









singular values


singular values




coef 1047297cients

















as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References



































































3 Proposed methods
















31 Dataset












































































Energy frac14 sumn

i frac14 1

W i X ietht THORN


n

i frac14 1
















cation































































Σ frac14

λ1 0 ⋯ 0

0 λ2 ⋯ 0

⋮ ⋮ ⋱ ⋮

0 0 ⋯ λn

⋮ ⋮ ⋮

0 0 ⋯ 0

2666666664

3777777775

eth2THORN














classi1047297cation










































residual component


i frac14 1














N DethwiTHORN 2

eth4THORN




i













signal








36 Classi 1047297er











testing























i 1


eth6THORN









singular values


singular values




coef 1047297cients

















as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References








































































Energy frac14 sumn

i frac14 1

W i X ietht THORN


n

i frac14 1
















cation































































Σ frac14

λ1 0 ⋯ 0

0 λ2 ⋯ 0

⋮ ⋮ ⋱ ⋮

0 0 ⋯ λn

⋮ ⋮ ⋮

0 0 ⋯ 0

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3777777775

eth2THORN














classi1047297cation










































residual component


i frac14 1














N DethwiTHORN 2

eth4THORN




i













signal








36 Classi 1047297er











testing























i 1


eth6THORN









singular values


singular values




coef 1047297cients

















as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References






























































classi1047297cation










































residual component


i frac14 1














N DethwiTHORN 2

eth4THORN




i













signal








36 Classi 1047297er











testing























i 1


eth6THORN









singular values


singular values




coef 1047297cients

















as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References

























































signal








36 Classi 1047297er











testing























i 1


eth6THORN









singular values


singular values




coef 1047297cients

















as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References





















































as


2THORN eth7THORN



k frac14 1


h iexpeth xk xk




lssvmlab






















artifacts




























Table 2




RBF Frontal SEN 2941 870 6666 100 9583 7678 4914

SPE 8051 7973 8220 8130 8186 8337 8382

ACC 7790 7810 8150 8160 8220 8300 7980

Temporal SEN 6667 400 100 9824 9710 9747 9647

SPE 8028 9863 8351 8473 8571 8665 8710

ACC 8020 7970 8420 8550 8650 8750 8790

Average SEN 4804 635 8333 9912 9647 8713 7281

SPE 8040 8918 8286 8302 8379 8501 8546

ACC 7905 7890 8285 8355 8435 8525 8385


SPE 8101 7978 8216 8126 8112 8396 8042

ACC 7780 789 8130 8140 8130 8180 7560

Temporal SEN 8462 200 100 9841 9012 9368 9468

SPE 8354 9900 8430 8527 8618 8632 8775

ACC 8360 7960 8510 8610 8650 8690 8840

Average SEN 5922 100 8192 9365 9173 7684 5955

SPE 8228 8939 8323 8327 8365 8514 8409

ACC 8070 7925 8320 8375 8390 8435 8200


SPE 8035 8061 8131 8089 8155 8081 8223

ACC 7840 766 8130 8110 8190 8100 8240

Temporal SEN 8421 600 9800 9722 9811 9800 9836

SPE 8528 9613 8411 8288 8437 8412 8509

ACC 8520 7810 8480 8340 8510 8480 8590

Average SEN 5658 1664 8948 9861 9906 9900 9293

SPE 8282 8837 8271 8189 8296 8247 8366

ACC 8180 7735 8305 8225 8350 8290 8415







































respectively




















methods




















5 Conclusion





























brain locations







References
























































































respectively




















methods




















5 Conclusion





























brain locations







References


































































brain locations







References





















































References



















































Analyzing EEG

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