thediagnosisofepilepsybygravitationalsearchalgorithmand...

Journal of Computing and Security

April 2016, Volume 3, Number 2 (pp. 111–125)

http://www.jcomsec.org

TheDiagnosis of Epilepsy byGravitational SearchAlgorithm and

Support VectorMachines

Zeinab Basereh a, Shahram Golzari a,∗, Abbas Harifi a

aDepartment of Electrical and Computer Engineering, University of Hormozgan, Bandar Abbas, Iran.

A R T I C L E I N F O.

Article history:Received: 11 March 2015

Revised: 21 May 2016

Accepted: 20 July 2017

Published Online: 26 November 2017

Keywords:

Epilepsy Diagnosis, GravitationalSearch Algorithm, Support Vector

Machines, Instance Selection,

Feature Selection, ParametersOptimization.

A B S T R A C T

In this paper, the binary gravitational search algorithm and support vector

machines have been used to diagnose epilepsy. At first, features are extracted

from EEG signals by using wavelet transform and fast fractional Fourier

transform. Then, the binary gravitational search algorithm is used to perform

feature selection, instance selection and parameters optimization of support

vector machines, and finally constructed models are used to classify normal

subjects and epilepsy patients. The appropriate choice of instances, features and

classifier parameters; considerably affects the recognition results. In addition,

the dimension reduction of the features and instances is important in terms

of required space to store data and required time to execute the classification

algorithms. Feature selection, instance selection and parameters optimization

of support vector machines have been implemented both simultaneously and

stepwise. The performance metrics in this study are accuracy, sensitivity,

specificity, number of selected features, number of selected instances and

execution time. The results of experiments indicate that the simultaneous

implementation of feature selection, instance selection and support vector

machines parameters optimization leads to better results in terms of execution

time in comparison with the stepwise implementation. In the stepwise

implementation, performing instance selection process before feature selection

leads to better results in terms of accuracy, sensitivity and specificity, as well

as reduction of execution time. The results show that the proposed methods

achieve noteworthy accuracy in comparison with other methods that were used

to diagnose epilepsy.

c© 2016 JComSec. All rights reserved.

1 Introduction

Epilepsy is a transient disorder of brain functioncaused by abnormal electrical discharges in the brain

∗ Corresponding author.

Email addresses: [email protected] (Z.

Basereh), [email protected] (S. Golzari),[email protected] (A. Harifi)

ISSN: 2322-4460 c© 2016 JComSec. All rights reserved.

neurons which has been captivated by millions aroundthe world. Nearly, two out of every three new casesof infected people; are in the developing countries [1].Epilepsy frequently occurs in young children or peo-ple over the age of 65. However, the risk exists at anyage [1]. Unfortunately, epilepsy patients nowadays canbe identified with difficulties and high costs. Sincethe diagnosis of the disease is vital, Electroencephalo-gram (EEG) signal plays a crucial role in the diag-

112 The Diagnosis of Epilepsy by Gravitational Search Algorithm and . . . — Z. Basereh, S. Golzari, et al.

nosis of epilepsy. The recorded EEG signals requirea long period to be analyzed by a qualified person.Since the traditional methods of EEG signal analysisare tedious, the great number of automatic epilepsydiagnosis systems has been emerged in recent years.

Subasi et al.[2] applied an autoregressive (AR)model and neural network to classify EEG signalsin order to diagnose the epilepsy seizure. Theirexperimental results show that the percentage ofexperimental data classification by AR and Maxi-mum Likelihood Estimation (MLE) is more than92%. Furthermore, Subasi et al. [3] have presentedan approach for analyzing EEG signals using dis-crete wavelet transform and classification by artificialneural network. In the approach, a signal has beendecomposed into five levels using db4 wavelet. Theenergy of details and approximation coefficients hasbeen considered as input features. Sivasankari andThanushkodi [4] have presented a method for epilepticseizure diagnosis from the recorded brain EEG signalsby fast Independent Component Analysis (fast ICA)and Artificial Neural Networks (ANN). Fast ICAand ANN present encouraging results in diagnosingepilepsy. The accuracy of the presented approach is76.5% for epileptic cases and 66% for healthy cases.

Guler et al. [5] conducted a research to classifyhealthy people, patients with epilepsy and seizuresintervals. They used A, E and D data groups from An-derzjak epilepsy dataset. Computed Lyapunov expo-nents have been used for feature extraction. The clas-sification accuracy of 96.68% has been achieved usingrecurrent neural network classifier. In another studyconducted by Guler and Ubeyli [6], wavelet trans-form was used for feature extraction and an AdaptiveNeuro-Fuzzy Inference System (ANFIS) was appliedfor classification, which resulted into 98.68% accuracy.

Aslan et al. [7] carried out a study for investigatingpatients with epilepsy and classifying epilepsy groups.The classification process of groups into partial andelementary epilepsy has been performed by RadialBasis Function Neural Network (RBFNN) and Mul-tilayer Perceptron Neural Network (MLPNN). Theobtained parameters from EEG signals and patients’curing features have been used for neural networktraining. They reached 95.2% and 89.2% accuracy forRBFNN and MLPNN, respectively. Akin et al. [8]have applied wavelet transform into EEG signals andhave extracted delta, theta, alpha and beta frequencysub bands. Based on these sub bands, an ANN hasbeen developed and trained with the accuracy of 97%for epileptic cases and 98% for healthy cases.

In the work conducted by Subasi and Gursoy [9],signals were decomposed into the frequency sub-bandsusing DWT and a set of statistical features was ex-

tracted from the sub-bands to represent the distri-bution of wavelet coefficients. Principal ComponentsAnalysis (PCA), Independent Components Analysisand Linear Discriminant Analysis (LDA) were usedto reduce the dimension of data. Then, these featureswere used as an input to a Support Vector Machine(SVM) with two discrete outputs: epileptic seizureor not. The classification accuracy with LDA featureextraction was the highest, 100%, and ICA rankedsecond, 99.5%. The PCA achieved the lowest classifi-cation accuracy, 98.75%, compared to LDA and ICAcounterparts.

Acharya et al. [10] have used the recorded EEGsignals in Recurrence Plots (RP), and extracted Re-currence Quantification Analysis (RQA) parametersfrom the RP in order to classify the EEG signals intonormal, epileptic (ictal ) and background (pre-ictal )classes. In this work, they have used ten RQA param-eters to quantify the important features in the EEGsignals. These features were fed to seven different clas-sifiers: SVM, Gaussian Mixture Model (GMM), FuzzySugeno Classifier, K-Nearest Neighbor (KNN), NaiveBayesian Classifier (NBC), Decision Tree (DT), andProbabilistic Neural Network (PNN). Their resultsshow that the SVM and Fuzzy classifiers were ableto identify the EEG class with an average accuracy,sensitivity and specificity of 94.4%, 97.7% and 94.7%,respectively.

In another work, Acharya et al. [11] have proposeda methodology for the automatic detection of normal,pre-ictal, and ictal conditions from the recorded EEGsignals. Four entropy features namely ApproximateEntropy (ApEn), Sample Entropy (SampEn), PhaseEntropy 1 (S1), and Phase Entropy 2 (S2) were ex-tracted from the collected EEG signals. These featureswere fed to seven used classifiers in [10]. Their resultsshow that the Fuzzy Classifier was able to differenti-ate the three classes with the high accuracy of 98.1%.Table 1, shows the summary of reviewed researches onepilepsy detection. Interested readers can find morealgorithms where have been proposed to diagnose andpredict epilepsy, in [12].

SVM is a classifier model, which is generated duringthe training process with training data. The biggestproblem in setting up a SVM model is to choose akernel function and its appropriate parameter values.Incorrect configuration parameters lead to poor results.Thus, the accuracy of SVM model largely dependson the model parameters. To design a SVM, a kernelfunction should be selected at the first step. Then, thesoft margin constant (penalty parameter) C and thekernel function parameters such as sigma (σ) in theradial basis kernel function should be optimized [13].

Suppose that a feature set with p elements has been

April 2016, Volume 3, Number 2 (pp. 111–125) 113

Table 1. The Summary of Reviewed Researches on Epilepsy Detection

Authors Feature Extraction Feature Selection Classifier

Subasi et al. [2] AR+MLE – ANN

Subasi et al. [3] DWT – ANN

Sivasankari and Thanushkodi [4] Fast ICA – ANN

Guler et al. [5] – – RNN

Guler and Ubeyli [6] WT – ANFIS

Aslan et al. [7] – – RBFNN, MLPNN

Akin et al. [8] WT – ANN

Subasi and Gursoy [9] DWT PCA, LDA SVM

Acharya et al. [10] RQA Manual SVM, GMM, Fuzzy, KNN, NBC, DT, PNN

Acharya et al. [11] Entropy – SVM, GMM, Fuzzy, KNN, NBC, DT, PNN

presented. The purpose of feature selection is to finda minimal feature subset of size pr (pr¡p), while re-taining a suitably high accuracy in representing theoriginal features. Feature selection techniques are di-vided into two main categories: filter and wrapper. Inthe filter methods, features are selected without usingany classification algorithm; whereas in the wrappermethods, features are selected by considering a classi-fication algorithm [14]. In this study, we propose somewrapper feature selection methods and SVM is usedas a classification algorithm.

Training set in the supervised learning provides in-formation, which is used to classify new instances. Inreal applications, there are several instances in thetraining set, but some of them may not be useful forthe classification task, such as noisy and redundantinstances. They are known as superfluous instances.They have negative impact on classification perfor-mance. Hence, in order to reach acceptable classifica-tion accuracy and to reduce the training time, uselessitems are ignored. This process is known as instanceselection. Selecting the most informative instancesin the training set, which are more related to theclass label, increases the classification accuracy andreduces the training time. Similar to feature selection,instance selection methods are categorized into twomain categories: wrappers and filters [15].

Using evolutionary algorithms to perform the standalone feature selection,instance selection and SVMparameters optimization nowadays are efficient andpopular. Evolutionary algorithms have shown valu-able performance to select the appropriate featuresin many applications [16–18]. The evolutionary basedinstance selection methods belong to the wrappercategory. Some evolutionary algorithms have beenused to perform instance selection, such as GeneticAlgorithm (GA) [19–21] , Artificial Immune System[22, 23], Memetic Algorithm [24] and Tabu Search [25].

Based on the results of these studies, evolutionary-based approaches have shown better performance interms of instance reduction rate and final accuracy.Furthermore, these approaches are not order sensi-tive and are able to detect a considerable portion ofnoisy instances. Some studies related to evolutionary-based optimization of the SVM parameters have beenreported in [23, 26, 27].

Rashedi et. al. [28] proposed Gravitational SearchAlgorithm (GSA) as an evolutionary optimizationtechnique inspired by Newton’s gravity law. Based onthe review carried out by Sabri et al. [29], this algo-rithm has been used in a wide range of applicationsand has shown suitable performance. Sarafrazi andNezamabadi-pour [30] have proposed a GSA-SVM hy-brid system to improve the classification accuracy byappropriate feature selection and SVM parameterstuning. In this research, the RBF kernel function hasbeen used for SVM classifier. Each mass in GSA con-tains p binary parameters for feature selection andtwo real-value parameters for SVM parameters. There-fore, the features are selected by Binary GravitationalSearch Algorithm (BGSA) and the SVM parametersare optimized by Real-valued Gravitational SearchAlgorithm (RGSA). Classification accuracy and thenumber of selected features have been used to de-fine the fitness function. Their results show that theirproposed method is able to select the discriminatinginput features and has high classification accuracy.However, their study only covers simultaneous featureselection and SVM parameters optimization scenarioof our proposed method. In [30] researchers have usedtwo different types of GSA: BGSA and RGSA. TheBGSA and RGSA have quite different evolutionaryoperators and behaviors. In our study, we use justone evolutionary algorithm (i.e. BGSA) by codingreal valued parameters in binary format. Using oneevolutionary algorithm instead of two different types


of evolutionary algorithm has been recommended in[31, 32]. Huang and Wang[32] have used only binaryGA to perform simultaneous feature selection andSVM parameter optimization.

In addition to the above-mentioned work carriedout by Sarafrazi and Nezamabadi-pour [30], other re-searchers [31–33] have used evolutionary algorithms toperform feature selection and SVM parameters opti-mization. However, there is a lack of study where bothinstance selection and SVM parameters optimization,or all three feature selection, instance selection andSVM parameters optimization are performed by evolu-tionary algorithms. The reviewed evolutionary basedmodels for feature selection, instance selection andSVM parameters optimization have been summarizedin Table 2.

The main objective of this study is to diagnoseepilepsy using BGSA and SVM. The features fromEEG signals are extracted using Wavelet Transform(WT) and Fast Fractional Fourier Transform (FFrFT).BGSA is used to perform feature selection, instanceselection and SVM parameters optimization and pro-posed methods are applied to diagnose epilepsy. Fea-ture selection, instance selection and parameters opti-mization are performed stepwise and simultaneously.In the stepwise mechanism, each preprocessing pro-cess is started after ending another one. Robust evalu-ation techniques are used to evaluate the performanceof the proposed methods.

The contribution of the study would be summa-rized as: using hybridization of BGSA and SVM as anovel method to diagnose epilepsy, proposing a newBGSA-based method to optimize the SVM parame-ters, proposing a new BGSA-based method to performinstance selection and proposing a framework to usefeature extraction, feature selection, instance selectionand parameters optimization all together in epilepsydiagnosis application. To the best of our knowledge,this is the first study where use feature selection, in-stance selection and SVM parameters optimizationsimultaneously.

The rest of the paper is organized as follows: Thebackground issues, the basic concepts of SVM andBGSA, are presented in Section 2. The details of pro-posed method are introduced in Section 3. The exper-imental design and performance evaluation method-ology are described in Section 4. The results of thestudy are presented and discussed in Section 5. Finally,conclusion is drawn in Section 6.

2 Background

2.1 Support Vector Machine

In this study, the SVM [34] has been used as the clas-sifier. SVM is a supervised learning model with as-sociated learning algorithm that analyzes data andrecognizes patterns and is; used for classification andregression analysis. This is a relatively new approachwith better performance in comparison with the ear-lier classification methods like neural networks. SVMclassifier is based on the linear classification of data inwhich the best line with maximum margin is chosen.Finding the optimum line is performed by quadraticprogramming (QP), which is a well-known methodfor solving bounded problems.

The linear discrimination function for 2-classes SVMis as follows:

f(x) =∑n

i=1yiαik(x, xi) + b (1)

Where n is sample number of training, xi is the ith

training sample and yi is the correct class of the ith

training sample. yi is assumed 1 for one class and -1for another class. b is the bias value of function. αiare classifier constants, which are identified with bduring the training process. Function k(x1, x2) is akernel. Two common kernels, which are used for SVMare mentioned below:

Polynomial Kernel:

k(x1, x2, p) = (1 + x1 · x2)p (2)

RBF Kernel:

k(x1, x2, σ) = e−‖x1−x2‖

2

2σ2 (3)

Where p and σ are adjustable parameters for poly-nomial and RBF kernels, respectively. The coefficientsαi in Equation (1) can be obtained by solving thefollowing optimization problem:

Minimize

W (α) =∑n

i=1αi −

1

2

∑n

i,jαiαjyiyjk(xi, xj) (4)

Subject to∑n

i=1αiyi = 0 0 ≤ αi ≤ C, i = 1, ..., n (5)

Where, C is constant and a parameter of SVMclassifier. One of the common and fast methods forsolving the above optimization problem (i.e. for train-ing SVM) is called sequential minimum optimization(SMO) [35]. The above-presented SVM is a binaryclassifier. For M classes problems, a combination ofbinary classifiers should be used. For this purpose,


Table 2. Reviewed Evolutionary Based Models for Feature Selection, Instance Selection and SVM Parameters Optimization

Authors Feature Selection Instance Selection SVM Parameters Optimization

Tan et al. [16] GA – –

Zhong and Zhang [17] AIS – –

Zhu et al. [18] MA – –

Bezdek and Kuncheva [19] – GA –

Cano et al. [20] – GA –

Cano et al. [21] – GA –

Garain [22] – AIS –

Garcıa et al. [24] – MA –

Zhang and Sun [25] – TS –

Aydin et al. [23] – – AIS

Gue et al. [26] – – PSO

Lorena and Carvalho [27] – – GA

Sarafrazi and Nezamabadi-pour [30] BGSA – RGSA

Huang and Dun [31] PSO – PSO

Huang and Wang [32] GA – GA

Lin et al. [33] SA – SA

the “one against the rest” or “one against one pair-wise” or “hierarchical” models can be used [36]. TheSVM performance depends on kernel function, kernelparameters and C parameter. In this research, theRBF kernel function is used for SVM classifier. It cananalyze high-dimensional data and only requires twoparameters σ and C.

2.2 Binary Gravitational Search Algorithm

Rashedi et al. [28, 37] have proposed the BGSA, whichis described as follows: Consider a system with mmasses. Each mass has a position that is an answerto the problem. xdi represents the position of the ith

mass in the dth dimension.

Xi = (x1i , . . . , xdi , . . . , x

Di ) (6)

In this system, the force acting on the ith mass fromthe jth mass at time t is calculated as follows:

F dij(t) =G(t)×Mgj(t)

Rij(t) + ε(xdj (t)− xdi (t)) (7)

Where, Mgj is the gravitational mass related to massj, G(t) is gravitational constant at time t, ε is a smallconstant, and Rij is the Euclidean distance betweentwo masses i and j.

Rij(t) = ||Xi(t), Xj(t)||2 (8)

The total acting force on the ith mass in the dimensiond is a random weighted sum of the dth components ofapplied forces from other masses:

F di (t) =

m∑j=1,j 6=i

rjFdij (t) (9)

Based on Newton’s second law, the acceleration ofthe ith mass at time t in the dth dimension is given asfollows:

adi (t) = F di (t)/Mii(t) (10)

The velocity of a mass in the next step is consideredas a fraction of its current velocity added to its accel-eration. Therefore, its position and velocity could beupdated as follows:

V di (t+ 1) = ri × V di (t) + adi (t) (11)

xdi (t+ 1) = xdi (t) + V di (t+ 1) (12)

Where, ri are random numbers with uniform distribu-tion in the interval [11]. The gravitational and inertialmass are evaluated by the fitness function as follows:

Mgi =fiti(t)− worst(t)best(t)− worst(t)

(13)

Mii = 1 +Mgi (14)

best = minj∈{1,2,···m}

fitj (t) (15)

worst = maxj∈{1,2,···m}

fitj (t) (16)

In the binary environment, every dimension has thevalue of 0 or 1. Moving in every dimension means thatits value changes from 0 to 1 or vice versa. BGSA


updates the velocity based on the Equation (11) andconsiders the new position to be 1 or 0 with probability.In other words, moving velocity is defined in terms ofchanges of probabilities that a bit will be in one state orthe other. Thus V di shows the probability of changingthe value of xdi from 0 to 1 or vice versa. FunctionS(V di ) is defined to transform V di into a probabilityfunction. Therefore, S(V di ) must be bounded between0 and 1:

S(V di (t)) = |tanh(V di (t))| (17)

After calculating S(V di ), masses will move accordingto the following equation:

xdi (t+ 1) =

xdi (t) rand < S(V di (t+ 1)

)xdi (t) rand ≥ S

(V di (t+ 1)

) (18)

In BGSA, G(t) is decreased linearly with time ac-cording to the following equation:

G(t) = G0(1− t/T ) (19)

Where, T is the total number of iterations (the totalage of system).

3 Proposed Method

3.1 Dataset

In this study, the EEG signals from Andrzejak et al.[38] have been used. This dataset includes 5 groupsA to E and each group includes 100 EEG signals(duration of 23.6 Seconds) which belong to two brainhemispheres. Sampling frequency is 173.6 Hz andresolution is 12 bits. Artifacts like the movement ofmuscles and eyes have been removed from these signals.Bandpass filter 0.53-40 Hz is adjusted [38]. Thesesignals belong to 6-43 years old people, which havebeen sampled using 10-20 standard.

Figure 1 shows the possible position of electrodeand the underlying area of cerebral cortex in thisstandard. As it can be seen, each position is identifiedby a letter and a number. The letters F, T, C, P, andO indicate the Frontal, Temporal, Central, Parietaland Occipital lobe, respectively. The even numbers(2, 4, 6, 8) are dedicated to the right hemisphere andthe odd numbers (1, 3, 5, 7) are dedicated to theleft hemisphere. The midline is shown by the letter z.Also note that the smaller the number, the closer theposition is to the midline.

A and B sets include recorded signals from the headsurface of 5 volunteers. Volunteers were awake andresting with open eyes (set A) and closed eyes (set B)during the signal recording period. The signals in C,D and E groups have been recorded from 5 patients.Group D signals have been recorded from the epilepsy

10%

20%

20%

20%

20%

10%

10%20%

10%20% 20% 20%

10 %

20 %

20 %

zC

3C

4C

4T

3T

Nasion

Inion

2F

3F

4F

7F zF

pzF

1pF2pF

20 %

20 %

10 %

3P

4P

zP 6

T5T

1O

2O

zO

Figure 1. International 10-20 Electrode Placement System[39]

region and those in group C have been recorded fromthe hippocampal region of opposite hemisphere. Sig-nals in C and D groups include measured activityduring seizure free intervals, while E group includesonly seizure activity.

In this study, signals of A and E groups have beenused, which belong to healthy and epileptic patients,respectively.

3.2 Feature Extraction

In the feature extraction, data are usually transformedto the less dimension space by linear or nonlinear trans-forms. The epilepsy diagnosis system should knowwhere and what changes happened in the brain signalslike a specialist doctor. Therefore, such a feature vec-tor should be extracted that includes both time andfrequency features [40]. Thus, the wavelet transformand the fast fractional Fourier transform have beenused for feature extraction.

In the wavelet transform, the decomposition leveland wavelet type should be determined. In this paper,the signal was decomposed by db4 wavelet into 10levels and wavelet coefficients were obtained; then,some features like log-energy, variance, wave lengthand entropy of detail and approximate coefficientswere calculated. Thus, the feature vector of the signalhas 44 elements.

In this study, the fast fractional Fourier transformwith order 5/6 was implemented as the second method


1

cg i

cg cn

cg⋯ ⋯

1g

σ

jgσ

ng σ

σ⋯ ⋯ ⋯ ⋯

1

fg k

fg fn

fg

(a) Structure of the Mass Used for Feature Selection and

Parameters Optimization

⋯ ⋯

1

ig k

ig in

ig

(b) Structure of the Mass Used for Instance Selection of

Healthy Individuals

⋯ ⋯

1

ig k

ig im

ig

(c) Structure of the Mass Used for Instance Selection forPatients With Epilepsy

Figure 2. Structure of the Mass Used for Feature Selectionand Parameters Optimization

of feature extraction and 4097 features were extractedfrom each signal. Then, the extracted features havebeen framed into 17 frames. This means that eachframe includes 241 features. Then, for each frame,features like entropy, log-energy, variance and wave-length were calculated. Therefore, the final featurevector contains 68 elements.

3.3 BGSA for Feature Selection, Instance Se-lection and Parameters Optimization

The selection of appropriate dataset features and in-stances is one of the important techniques to achievea good performance and to avoid the excessive com-putational time. In addition, the selection of the ap-propriate parameters for SVM classifier significantlyincreases the recognition accuracy. Therefore, in thispaper, the BGSA algorithm was proposed for instanceselection, feature selection and SVM parameters opti-mization. Two essential steps to determine the opti-mized parameters are defining parameters, featuresand instances as an agent position and defining a suit-able fitness function to evaluate an agent. Initial pop-ulation (20 masses for parameter optimization andfeature selection, 20 masses for instance selection ofnormal subjects and 20 masses for instance selectionof patients with epilepsy) is given randomly in binaryformat. The ith mass of the first set determines SVMparameters and selected features. The ith mass of thesecond and third set determine the selected instancesfor classification from normal subjects and epilepticpatients, respectively.

3.3.1 Features, Instances and Parameters asAgent

BGSA was used for the parameters optimization ofSVM and choice of suitable instances and features,simultaneously. For this purpose, binary coding systemhas been used to display the agents (masses) position.

Figure 2a shows the binary position of feature selec-tion and parameters optimization mass. g1c ∼ gncc and

g1σ ∼ gnσc are binary representation of C and σ param-eters, respectively. These parameters can be changedinto decimal numbers using Equation (20).

p = minp +maxp −minp

2l − 1× d (20)

Where p is phenotype bit string, minp is the mini-mum value of the parameter, maxp is the maximumvalue of the parameter, d is the decimal value of thebit string and l is the bit string length.

g1f ∼ gnff is feature mask, nf is the number of

features (48 for the wavelet transform and 68 for thefast fractional Fourier transform). If the value of abit is 1, the corresponding feature will be selected toparticipate in the classification and if its value is 0, thecorresponding feature will not be selected. In Figure 2b,g1i ∼ g

nii is a 100 bits mask, to select the instances of

healthy individuals. In Figure 2c, g1i ∼ gmii is a 100bits mask, to select the instances of epileptic patients.Like the above-mentioned rule, if the value of a bit is1, the corresponding instance will be participated inthe classification, and vice versa.

Initial population (20 masses for parameter opti-mization and feature selection, 20 masses for instanceselection of normal subjects and 20 masses for instanceselection of patients with epilepsy) is given randomlyin binary format. The ith mass of the first set deter-mines SVM parameters and selected features. The ith

mass of the second and third set determine the se-lected instances for classification from normal subjectsand epileptic patients, respectively.

3.3.2 Fitness Function

Classification accuracy, the number of selected fea-tures and instances have been used to define the fitnessfunction. Therefore, after creating the initial popula-tion of the masses, the fitness value of each mass iscalculated as follows:

fitness =WA ×Accuracy +WI × (1− sini

)+

WF × (1− sfnf

) (21)

Where, WA is the weight of the classification accu-racy, WI is the weight of the selected instances, ni isthe total number of dataset instances, si is the num-ber of selected instances by agent, WF is the weight ofselected features, nf is the total number of extractedfeatures from each signal and sf is the number of se-lected features by agent that is used for classification.WA, WI and WF have been initialized with 0.6, 0.2and 0.2, respectively. In Equation (22), the classifica-


tion accuracy is computed as follows:

Accuracy =TP + TN

TP + FP + TN + FN% (22)

Where, TP , TN , FP and FN denote true posi-tive, true negative, false positive and false negativerespectively.

4 Experiment Design and Perfor-mance Evaluation

4.1 Implementation and Design of Experi-ments

BGSA has strong global search capability of identify-ing the exact or approximate solutions to optimizationand search problems. Its performance is relatively in-dependent of the number of agents. It is a high speedalgorithm and needs to a little time to conclude. Inthis study, both Clonal Selection Algorithm (CSA)[41] and GA have been implemented and their resultsare also listed. To simulate BGSA, CSA and GA, thenumber of agents and number of iterations are consid-ered 20 and 20, respectively. Furthermore, the initialpopulation is given randomly. In BGSA, G0 value is100. The initial value of the gravitational constant Gis G0 and is reduced with time using Equation (19).In CSA, mutation rate is 0.008. In GA, crossover andmutation rates are considered 0.7 and 0.002, respec-tively. Moreover, the value of the variables nc, nσ, l,minp and maxp are considered 20, 20, 20, 1 and 200,respectively. In this study, the value of parameterswhich are related to multi objective optimization andGA implementation were taken from [32]. The BGSAparameter values were taken from [30] and CSA pa-rameter values were taken from [23]. The instanceselection parameter values were obtained from ourpreliminary experiments. No range of values for c andσ is considered. The final condition of optimization isto achieve 100% fitness for classification or to reachthe maximum iteration (for example 20) or to fix thefitness value of five successive iterations.

The designed experiments include the following:

(1) Feature extraction + Feature selection + SVMparameters optimization

(2) Feature extraction + Instance selection + SVMparameters optimization

(3) Feature extraction + Instance selection + Fea-ture selection + SVM parameters optimization

(4) Feature extraction + Feature selection + In-stance selection + SVM parameters optimiza-tion

The tests are divided into two categories:

Class I: instance selection, feature selection andSVM parameters optimization are performed simul-taneously. It means that in each iteration, instanceselection, feature selection and SVM parameters opti-mization are conducted simultaneously. In simultane-ous versions, feature selection, instance selection andSVM parameters optimization are considered at thesame time. The different mass types are not evolvedseparately. The proper parts of different mass typesare combined and considered as one mass type. Thisfinal mass type is used as a mask for dataset and isevolved during the training phase. When a stop condi-tion is reached, an agent, which has the highest fitnessvalue, determines appropriate features, instances andparameters of SVM.

Class II: instance selection, feature selection andSVM parameters optimization are performed stepwise.It means that, the whole process has three steps. Forexample, in the stepwise feature selection, instance se-lection and SVM parameters optimization, the featureselection is performed at the first step. When a stopcondition is reached, an agent, which has highest fit-ness value, determines appropriate features. Thus, inthe next steps only these features will be used. In thesecond step, instance selection is conducted. When astop condition is reached, an agent, which has the high-est fitness value, determines appropriate instances. Inthe next step, only the selected features from the firststep and selected instances from the second step willbe used. In the third step, SVM parameters are op-timized. When a stop condition is reached, an agent,which has the highest fitness value, determines the op-timal parameters of the SVM. The allowable numberof iterations at each step is 20. Figure 3 shows the dia-grams of stepwise and simultaneous implementation.

Therefore, there exist different scenarios in thisstudy as follows:

48 = 2 (Feature extraction: Wavelet Transform(WT) and Fast Fractional Fourier Transform (FFrFT))× 3 (Algorithms: GSA, CSA and GA) × 4 (Data re-duction and parameters optimization: feature selec-tion + parameters optimization, instance selection +parameters optimization, instance selection + featureselection + parameters optimization, feature selection+ instance selection + parameters optimization) × 2(Implementation: simultaneously and stepwise).

4.2 Performance Evaluation

To evaluate the proposed methods in this study, weused the combination of robust recommended valua-tion methods and metrics for signal processing, fea-ture selection, instance selection, parameter optimiza-tion and computer-aided diagnosis systems [16, 21, 27,32, 40, 42]. Figure 4 shows the system architecture of


Feature

Extraction

Instance

Selection

Feature

SelectionEEG Signals

SVM

Parameters

Optimization

Diagnosis

(a) Stepwise Implementation

Feature

ExtractionEEG Signals

Instance Selection, Feature Selection and

SVM Parameters OptimizationDiagnosis

(b) Simultaneous Implementation

Figure 3. Stepwise Implementation vs. Simultaneous Implementation

Initialization

Instance selection, Feature

selection and SVM parameters

optimization by BGSA

Dataset

Scaling

Training set Testing set

Train SVM with selected instances, selected features and

optimized paramaters

Calculate the fitness

StopBGSA operationsNo

Selected instances, selected features and optimized SVM parameters

Yes

Retrain SVM

Evaluation

Diagnosis

Figure 4. System Architecture for BGSA-SVM Epilepsy Diagnosis System

the proposed methods for epilepsy diagnosis in thisstudy. After the evolutionary process is finished, theSVM classifier is retrained by using selected instancesand features of training set and optimized parameters(based on the relative scenario). Then the test set isused to evaluate the performance of retrained (final)SVM. If the performance of final SVM is satisfiable,

then the model is used to perform diagnosis in the realworld. The system architecture of the study has beenadapted from [30, 32]. The accuracy was estimated bythe stratified 10 times 10-fold cross-validation method.In order to provide a strong methodology, the follow-ing experimental setup was employed for the dataset.10-fold cross-validation was performed 10 times. In


Table 3. Results for Feature Extraction and Classification

Using SVM

FE Accuracy Sensitivity Spesificity Time(s)

WT 66.15 61.1706 93.0025 7.093636

FFrFT 66 61.1425 93.4580 4.105224

10-fold cross-validation, firstly, the original data setwas randomly partitioned in ten equal size folds. Teniterations were carried out where in each iteration adifferent fold was left as test set while the remainingnine (of ten) were used together as training set. Then,since GSA, CSA and GA are evolutionary algorithms,for each algorithm, a total of 10 independent runswere performed. Each proposed scenario, introducedin Section 4.1, was applied on a training set and con-structed model evaluated using corresponding testset. Since each scenario produces 100 (10 × 10) esti-mates for the classification algorithm, these estimateswere averaged and their standard deviations were cal-culated. Three performance metrics were consideredhere: accuracy, sensitivity and specificity.

The following section presents the classificationperformance of each method on the test set. Theresults for each method are presented in separatetables, where the columns NOF and NOI respectivelyare the number of features and instances selected byapplying each scenario on train sets.

5 Results and Discussions

In this study, A and E groups of Andrzejak et al. [38],EEG dataset were used. For the feature extraction ofsignals, the wavelet transform and the fast fractionalFourier transform methods were applied. BGSA, CSAand GA were used for feature selection, instance se-lection and to find the optimum parameters of SVM.The proposed combination of SVM classification rate,the number of selected features and the number ofselected instances were used as fitness value.

Table 3 shows the results of classification with nofeature selection, instance selection and SVM param-eter optimization. The default value of C and σ pa-rameters of SVM was used to obtain the results. Theclassification accuracy and sensitivity are not highenough and acceptable. The reason is the crucial de-pendency of SVM performance to its parameters.

5.1 Stepwise Implementation

Tables 4 to 7 show the obtained results from the step-wise implementation. Based on the results, BGSA hasachieved better accuracy, sensitivity and specificityin comparison to CSA and GA, in the majority of

cases. The reason is the capability of BGSA to explorethe search space more than CSA and GA. In CSA,each antibody is compared with an antigen and theantibody with the most affinity is considered. Thedirection of an antibody is calculated using only oneantigen. In GA, each chromosome is generated usingonly two chromosomes (parents). However, in BGSAall agents make force to an agent and are associatedto determine the direction of an agent. In addition,the BGSA has strong exploration capability.

In all cases, the wavelet transform has achievedbetter results than fast fractional Fourier transform.The results support the results of the study previouslycarried out by Akin [48].

Conducting the instance selection step before fea-ture selection step, has led to better results comparedto perform feature selection step before the instanceselection step. In our view, this is related to the char-acterization of the used data and would not be gener-alized to the all data and application. However, morestudies on different data sets from a variety of appli-cations are needed to confirm our idea.

Using all three instance selection, feature selectionand SVM parameters optimization process togetherhave led to better results in comparison with usingcombination of feature selection and SVM parametersoptimization or combination of instance selection andSVM parameters optimization, since only effectiveinstances and features are considered in classification.

5.2 Simultaneous Implementation

Tables 8 to 10 show the obtained results from simulta-neous implementation. These results support the re-sults of stepwise implementation. BGSA has achievedcomparable accuracy, sensitivity and specificity incomparison to CSA and GA. The results, which areobtained using wavelet transform feature extraction,are better than the results obtained from fast frac-tional Fourier transform once. The combination ofinstance selection and SVM parameters optimizationis superior to the combination of feature selection andSVM parameters optimization. The combination ofinstance selection, feature selection and SVM parame-ters optimization has led to better result than that ofinstance selection and SVM parameters optimizationor feature selection and SVM parameters optimization.BGSA has led to the results in less time in comparisonto CSA and GA.

Also, the implementation results of Sarafrazi andNezamabadi-pour method [30] have been presentedin Table 8. The results show that using one algorithmto conduct feature selection and SVM parameter op-timization outperforms two algorithms with quite dif-


Table 4. Results for Stepwise Implementation of Feature Selection and the SVM Parameters Optimization

Algorithm FE Accuracy Sensitivity Spesificity NOF NOI Time(s)

BGSAWT 98.58±1.11 98.80±1.50 98.61±0.90 17.9±2.03 * 1982.6±198.16

FFrFT 95.55±0.97 96.48±1.53 95.30±1.07 32.2±4.83 * 3852±2081.9

CSAWT 96.78±1.78 97.87±2.06 96.26±1.55 17.4±2.76 * 1973.8±560.76

FFrFT 94.43±2.55 95.52±2.21 94.31±2.80 32.4±4.53 * 4134.5±1575.4

GAWT 96.81±2.24 97.20±2.66 96.97±1.84 16.2±2.10 * 1972.9±659.57

FFrFT 95.56±1.71 96.48±1.95 95.41±1.76 31.6±3.27 * 3493.5±679.09

Table 5. Results for Stepwise Implementation of Instance Selection and the SVM Parameters Optimization


BGSAWT 99.48±0.45 99.83±0.55 99.3±0.72 * 94.8±8.42 981.39±217.39

FFrFT 97.19±1.19 98.62±1.21 96.47±1.73 * 100.4±8.53 1848.7±120.91

CSAWT 99.14±0.97 99.58±0.85 99.06±0.87 * 86.2±11.12 886.76±267.98

FFrFT 97.97±1.11 99.20±1.31 97.35±1.43 * 101.9±6.45 1756.8±337.85

GAWT 99.17±0.61 99.61±0.82 99.10±0.95 * 80±12.45 890.39±268.13

FFrFT 98.28±0.88 99.34±0.79 97.65±1.63 * 100.5±8.42 1888.7±280.27

Table 6. Results for Stepwise Implementation of Instance Selection and the SVM Parameters Optimization


BGSAWT 98.12±1.71 99.27±1.37 98.08±1.59 19.2±2.62 88.2±15.26 1418.5±232.23

FFrFT 94.92±2.76 96.19±3.02 95.50±2.99 31.4±5.39 102.4±7.93 2066±161.67

CSAWT 96.42±4.78 97.7±3.28 97.3±3.60 17.6±1.65 53.7±6.26 1416.1±117.21

FFrFT 92.54±2.95 94.30±4.18 93.91±3.30 31±2.79 96.5±4.35 2210.1±214.86

GAWT 97.1±3.88 97.6±3.55 98.19±3.07 16.7±1.83 95.2±5.20 1335.8±154.03

FFrFT 93.94±2.88 95.59±2.24 94.34±2.89 32.9±2.60 105.6±8.33 2571.2±446.51

Table 7. Results for Stepwise Implementation of Instance Selection, Feature Selection and the SVM Parameters Optimization


BGSAWT 99.91±0.19 100±0 99.85±0.33 22.9±1.97 94.2±7.38 1122.5±262.70

FFrFT 96.42±2.06 98.28±1.87 95.68±2.40 35.6±3.84 101.6±6.62 2618.7±511.26

CSAWT 99.96±0.14 100±0 99.93±0.21 14.7±3.80 91.7±9.14 1207.4±140.15

FFrFT 98.08±1.40 99.25±0.76 97.55±1.96 36.3±3.74 105.5±5.68 2268.5±256.35

GAWT 99.99±0.04 100±0 99.98±0.06 12.9±4.68 83.7±11.80 1090.3±111.50

FFrFT 98.34±1.19 99.08±1.18 98.02±1.17 33.2±3.43 100.1±6.16 2366.6±165.63


Table 8. Results for Simultaneous Implementation of Feature Selection and the SVM Parameters Optimization


BGSAWT 98.85±0.08 99.16±0.07 98.74±0.18 23±2.40 * 795.76±54.84

FFrFT 96.58±0.34 98.27±0.68 95.64±0.78 35.4±3.03 * 1316.5±344.44

CSAWT 98.95±0.13 99.20±0.08 98.88±0.21 18.7±3.92 * 824.24±29.77

FFrFT 96.6±0.48 98.97±0.73 95±1.26 32±4.59 * 1145.1±363.60

GAWT 98.90±0.16 99.20±0.09 98.79±0.22 22.1±3.178 * 859.10±391.10

FFrFT 96.77±0.36 98.50±0.70 95.76±1.05 33±3.86 * 1382.4±314.21

GSAWT 98.67±1.31 100±0.0 97.46±2.48 21.8±2.86 * 637.19±14.91

FFrFT 91.33±2.05 93.20±3.76 89.77±1.81 37.30±3.43 * 772.50±100.50

Table 9. Results for Simultaneous Implementation of Instance Selection and the SVM Parameters Optimization


BGSAWT 99.47±0.50 99.87±0.20 99.30±0.82 * 93±5.08 513.74±138.10

FFrFT 98.14±1.12 99.09±0.83 97.71±1.50 * 99.2±8.93 963.03±173.12

CSAWT 99.83±0.35 99.85±0.47 99.87±0.41 * 92.4±8.37 633.82±64.24

FFrFT 98.82±0.62 99.61±0.66 98.34±0.93 * 101.2±6.56 1037.9±187.18

GAWT 99.99±0.04 99.98±0.0632 100±0 * 87.7±10.056 565.4864±78.8753

FFrFT 99±0.82 99.61±0.50 98.6791±1.2568 * 94.9±9.27 1030.8±155.52

Table 10. Results for Simultaneous Implementation of Instance Selection, Feature Selection and the SVM Parameters Optimization


BGSAWT 100±0 100±0 100±0 20.7±1.95 94.6±4.72 316.25±61.93

FFrFT 100±0 100±0 100±0 35.3±2.50 101.2±7.42 659.59±106.46

CSAWT 100±0 100±0 100±0 22.5±2.88 97.2±2.57 457.957±125.07

FFrFT 100±0 100±0 100±0 36.4±2.84 101.2±7.08 678.51±170.01

GAWT 100±0 100±0 100±0 22.6±3.03 76.4±7.11 442.95±90.41

FFrFT 100±0 100±0 100±0 37.6±2.76 88.6±4.84 792.66±220.03

ferent behavior.

5.3 Comparing Simultaneous With StepwiseImplementation

From Tables 4 to 10, it can be concluded that each sce-nario in simultaneous implementation achieves betterresults and consumes less time in comparison to itscorresponding stepwise implementation scenario. Inthe simultaneous implementation, data (feature andor instance) selection and SVM parameters optimiza-tion perform together at the same time; therefore, lesstime is needed. On the other hand, in the simultane-ous implementation, data reduction and SVM param-eters optimization for classification is considered as amulti-objective optimization problem and weightedsum method (converting multi-objective optimizationproblem to single-objective optimization problem) is

used to solve this multi-objective optimization prob-lem. Weighted sum method would find the Pareto op-timal solutions. Based on the results, the simultaneousimplementation of instance selection, feature selectionand SVM parameters optimization has achieved high-est accuracy, sensitivity and specificity in this study.This scenario consists of three objective functions: thenumber of selected instances, the number of selectedfeatures and classification accuracy.

5.4 Comparison With Previous Studies

The best method among the proposed methods in thisstudy, i.e., simultaneous instance selection, featureselection and SVM parameters optimization, has beencompared in Table 11 with other epilepsy diagnosissystems in term of classification accuracy. The resultsof experiments conducted in this study have shown


Table 11. Comparison of Classification Accuracy of Presented Method for Epilepsy Diagnosis With Previous Methods

Author Method Accuracy (%)

Subasi et al. [2] AR+MLE 92.3

Guler et al. [5] Lyapunov Exponents + Recurrent Neural Networks 96.79

Guler et al. [6] Wavelet + ANFIS 98.68

Aslan et al. [7] A RBF Neural Network Model 95.2

Subasi et al. [9] DWT,PCA, ICA, LDA + SVM 98.75 - 100

Acharya et al. [10] RQA + SVM 94.4

Acharya et al. [11] Entropy based Features + SVM 95.9

Polat and Gnes [42] Fast Fourier Transform (FFT) + Decision Tree Classifier 98.72

Song and Li [43] Sample Entropy + ELM 95.67

Chua et al. [44] HOS 93.11

Chua et al. [44] Power Spectrum Density 88.78

Tzallas et al. [45] Time-Frequency Analysis + Artificial Neural Networks 97.72 - 100

Janjarasjitt [46] Wavelet-based Scale Variance Feature 95 - 99

Tzallas et al. [47] Time-Frequency Distributions 89 - 100

Our study Wavelet + BGSA + SVM 100

that performing instance selection, feature selectionand SVM parameters optimization simultaneouslyusing BGSA would lead to better results in comparisonwith the previous methods.

6 Conclusion

The selection of appropriate parameters, features andinstances for classification can significantly increasethe classification accuracy. In this study, BGSA is usedfor instance selection, feature selection and finding theoptimal C and σ parameters of SVM. By applying theproposed methods to detect epilepsy, 100% accuracy,sensitivity and specificity were obtained. Performingthe instance selection step before the feature selectionstep, was led to better results. The proposed methodscan be used as a neurosurgeons assistant to help themfor epilepsy diagnosis, due to its high accuracy. Thissystem also can work as a powerful real-time diagnosisdevice for medical teams in the far regions, which donot have any trained neurologists.

To continue the research, the proposed idea canbe implemented by hardware. Furthermore, a com-bination of several different algorithms for instanceselection, feature selection and SVM parameters op-timization can be investigated. Moreover, the perfor-mance of the proposed methods in this study, could beevaluated in other application areas using UCI datasets. Using entropy- based feature extraction methodsin conjunction with the proposed methods is anotherfuture direction of this study.

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Zeinab Basereh received B.Sc degree inComputer Engineering from Payame noorUniversity, in 2012, and received M.Sc degree

in Artificial Intelligence from University ofHormozgan, Iran in 2015. Her research inter-

ests include data mining, knowledge discoveryand evolutionary algorithms.

Shahram Golzari received B.Sc. and M.Sc.

degrees in Computer Engineering from theIsfahan University of Technology, Iran andAmirkabir University of Technology, Iran, in

1998 and 2001, respectively. He received Ph.D.Degree in Artificial Intelligent from the Uni-versity Putra Malaysia, in 2011. He is cur-

rently assistant professor in Department ofElectrical and Computer Engineering at Uni-

versity of Hormozgan. His current research interests are Soft

Computing, Data Mining, Machine Learning and Deep Learn-ing.

Abbas Harifi received his B.Sc. degree in

Electrical Engineering from the University ofShiraz, Iran, in 2002. He received M.Sc. andPh.D. degrees from the University of Tabriz,

Iran, in 2005 and 2009, respectively. He is

currently assistant professor in Departmentof Electrical and Computer Engineering at

University of Hormozgan. His current researchinterests are Nonlinear Control, Intelligent

control, Robotics and Deep Learning.

thediagnosisofepilepsybygravitationalsearchalgorithmand...

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