A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION

USING EXTREME LEARNING MACHINE

Yuedong Song

Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, U.K.

Sarita Azad

Department of Applied Mathematics and Theoretical Physics, University of Cambridge

Wilberforce Road, Cambridge CB3 0WA, U.K.

Pietro Lio

Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, U.K.

Keywords:

Epileptic seizure detection, Electroencephalogram (EEG), Discrete Wavelet Transform, Extreme Learning

Machine (ELM).

Abstract:

In this paper, we investigate the potential of discrete wavelet transform (DWT), together with a recently-

developed machine learning algorithm referred to as Extreme Learning Machine (ELM), to the task of clas-

sifying EEG signals and detecting epileptic seizures. EEG signals are decomposed into frequency sub-bands

using DWT, and then these sub-bands are passed to an ELM classiﬁer. A comparative study on system per-

formance is conducted between ELM and back-propagation neural networks (BPNN). Results show that the

ELM classiﬁer not only achieves better classiﬁcation accuracy, but also needs much less learning time com-

pared to the BPNN classiﬁer. It is also found that the length of the EEG segment used affects the prediction

performance of classiﬁers.

1 INTRODUCTION

Epilepsy, the second most common serious neurolog-

ical disorder in human beings after stroke, is a chronic

condition of the nervous system and is characterized

by recurrent unprovoked seizures. Approximately

one in every 100 persons (about 50 million peo-

ple) worldwide are suffering from epilepsy(Iasemidis

et al., 2003). Electroencephalography(EEG) is an im-

portant clinical tool for monitoring, diagnosing and

managing neurological disorders related to epilepsy.

In recent years, there has been an increasing

interest in the application of pattern recognition

(PR) methods for automatic epileptic seizure detec-

tion. Several methods have been developed for han-

dling EEG signals classiﬁcation, and among these

methods, Multi-layer Perceptron Neural Network

(MLPNN)(Acir et al., 2005; Kalayci and Ozdamar,

1995; Jahankhani et al., 2006; Wang et al., 2005;

¨

Ubeyli, 2009; Ghosh-Dastidar et al., 2007) and Sup-

port Vector Machine (SVM)(Guler and

¨

Ubeyli, 2007;

Chandaka et al., 2009; Lima et al., 2009) are two

widely used classiﬁcation paradigms.

The general trend in automatic epileptic seizure

detection has focused on high accuracy but has not

considered the time taken to train the classiﬁcation

models, which should be an important factor of de-

veloping an EEG-based automatic detection device

for epileptic seizures because the device will need to

update its training during use. Therefore some clas-

siﬁcation models with high classiﬁcation accuracy

may not be satisfactory when considering the trade-

off between the classiﬁcation accuracy and the time

for training the classiﬁcation models. In our study,

to obtain a balance between high classiﬁcation accu-

racy and short training time, we investigate the use

of a novel paradigm of learning machine called Ex-

treme Learning Machine (ELM)(Huang et al., 2004).

In recent years, Extreme Learning Machine has been

increasingly popular in classiﬁcation task due to its

high generation ability and fast learning speed. In

(Wang and Huang, 2005), a classiﬁcation system is

built using ELM to classify protein sequences with

ten classes of super-families obtained from a domain

database, and its performance is compared with that

of Backpropagation Neural Networks. The results

436

Song Y., Azad S. and Lio P. (2010).

A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE.

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 436-441

DOI: 10.5220/0002745904360441

Copyright

c

SciTePress

show ELM greatly outperforms BPNN in terms of

both training time and classiﬁcation accuracy.(Zhang

et al., 2007) developed an ELM for multi-category

classiﬁcation in three Cancer Microarray Gene Ex-

pression datasets, and the results reveal that ELM can

not only obtain high classiﬁcation accuracy but also

avoid problems such as overﬁtting, local minima, and

improper learning rate. In addition to the ﬁeld of

Bioinformatics, Extreme Learning Machine has also

been successfully applied to the ﬁeld of Biosignal

Processing. (Kim et al., 2007) proposed an ELM

based classiﬁcation scheme for arrhythmia classiﬁ-

cation, and ﬁnally achieved 97.5% in average accu-

racy, 97.44% in average sensitivity, 98.46% in aver-

age speciﬁcity, and 2.423 seconds in learning time.

Up to now, to the best of our knowledge, there

is no study in the literature related to the assess-

ment of ELM classiﬁcation performance when ap-

plied speciﬁcally to the epileptic/non-epileptic dis-

crimination problem.

The present study is organized as follows: Sec-

tion 2 describes the EEG signals benchmark dataset

used in the experiments. Section 3 presents the mech-

anisms of discrete wavelet transform (DWT), and pro-

vides a description of backpropogation neural net-

work (BPNN) and extreme learning machine (ELM)

classiﬁers. The feature extraction from DWT and per-

formance comparison of ELM with BPNN is reported

in Section 4. Finally, Section 5 concludes the paper.

2 DATA ANALYZED

In this study, a publicly-available database introduced

in (Andrzejak et al., 2001) has been used. The whole

data is composed of 5 sets (denoted A-E), each con-

taining 100 single-channel EEG data of 23.6s dura-

tion. Sets A and B were taken from surface EEG

recordings of 5 healthy volunteers with eyes open and

closed, respectively. Sets C, D, and E originated from

the EEG archive of presurgical diagnosis. Signals in

Set C were recorded from the hippocampal formation

of the opposite hemisphere of the brain, and signals

in Set D were recorded from within the epileptogenic

zone. While Sets C and D contain only brain activity

measured during seizure free intervals, Set E contains

only seizure activity. All EEG signals were recorded

with the same 128-channel ampliﬁer. The data were

digitized at 173.6 samples per second at 12-bit reso-

lution. Band pass ﬁlter was set to 0.53–40 Hz. In the

present work, we classiﬁed only two (A and E) of the

complete datasets. The total number of EEG signals

is 200 (100 normal signals and 100 seizure signals).

Each data set has 4096 sampling points.

3 THEORY AND MODELS

3.1 Discrete Wavelet Transform (DWT)

The discrete wavelet transform (DWT) can be thought

of as an extended version of the classic Fourier Trans-

form and instead of working on a single scale, it

works on a multi-scale basis where each signal is de-

composed into several scales, each scale providing

a particular coarseness of the signal. Each phase of

decomposition of a signal is composed of two down

samplers by 2 and two digital ﬁlters. Figure 1 gives

the process of Discrete Wavelet Transform. Here in

each phase h(.) is the high-pass ﬁlter of that phase

and serves as the discrete mother wavelet; and g(.)

gives the low-pass ﬁlter, which acts as the mirror ver-

sion of the corresponding h(.). The down-sampled

outputs of the ﬁrst low-pass and high-pass ﬁlters sup-

ply the approximation A1, and the detail D1, respec-

tively. The ﬁrst approximation, A1, is further decom-

posed and the procedure is continued. Each low-pass

ﬁlter g satisﬁes the quadrature mirror condition given

in (Guler and

¨

Ubeyli, 2007):

G(Z)G(Z

−

1) + G(−Z)G(−Z

−1

) = 1 (1)

where G(Z) stands for the Z−transform of the ﬁl-

ter g. Its corresponding high-pass ﬁlter can be ob-

tained as:

H(Z) = ZG(−Z

−1

) (2)

Therefore, a sequence of ﬁlters, in increasing sub-

script of i can be obtained:

G

i

+ 1(Z) = G(Z

2

i

)G

i

(Z), (3)

H

i

+ 1(Z) = H(Z

2

i

)H

i

(Z),i = 0,...,I − 1 (4)

Where initially G

0

(Z) = 1.

For classiﬁcation purpose two models, namely

Backpropagation neural network and Extreme learn-

ing machine, are discussed in this work.

3.2 Extreme Learning Machine (ELM)

Recently a new learning algorithm called Extreme

Learning Machine (ELM) has been proposed for

single-hidden layer feedforward networks in(Huang

et al., 2004). In comparison with some well-known

classiﬁcation paradigms such as Support Vector Ma-

chine (SVM) and Backpropagation Neural Network

(BPNN), ELM has some prominent features such as

higher generalization ability, faster learning speed,

A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE

437

D1

A3

Figure 1: Wavelet decomposition of a signal.

and suitability for almost all nonlinear activation

functions, and it can avoid problems like local minima

and improper learning rate, which are usually faced

by traditional learning methods. Figure 2 shows the

structure of ELM. Here, we brieﬂy present the idea

behind ELM.

Suppose learning N arbitrary different instances

(x

i

,t

i

), where x

i

= [x

i1

,x

i2

,...,x

in

]

T

∈ R

n

, and t

i

=

[t

i1

,t

i2

,...,t

im

]

T

∈ R

m

, standard single-layer feedfor-

ward networks with N

h

hidden neurons and activation

function g(x) are mathematically modelled as a linear

system

N

h

∑

i=1

β

i

g(w

i

· x

j

+ b

i

) = o

j

, j = 1,...,N, (5)

where w

i

= [w

i1

,w

i2

,...,w

in

]

T

denotes the weight

vector connecting the ith hidden neuron and the in-

put neuron, β

i

= [β

i1

,β

i2

,...,β

im

]

T

denotes the weight

vector connecting the ith hidden neuron and output

neurons, and b

i

represents the threshold of the ith hid-

den neuron. w

i

∗ x

j

represents the inner product of

w

i

and x

j

. If the single-layer feedforward network

with N

h

hidden neurons with activation function g(x)

is able to approximate N distinct instances (x

i

,t

i

) with

zero error means that

Hβ = T, (6)

where

H(w

1

,...,w

Nh

,b

1

,...,b

Nh

,x

1

,...,x

N

)

=

g(w

1

· x

1

+ b

1

) ... g(w

N

h

· x

1

+ b

N

h

)

. . .

. . .

. . .

g(w

1

· x

N

+ b

1

) ... g(w

N

h

· x

N

+ b

N

h

)

w =

w

T

1

.

.

.

w

T

N

h

N

h

×m

T =

t

T

1

.

.

.

t

T

N

N×m

H is the hidden layer output matrix of the SLFN.

Hence for ﬁxed arbitrary input weights w

i

and the hid-

den layer bias b

i

, training a single-layer feedforward

network equals to discovering a least-squares solution

ˆ

β of the linear system Hβ = T,

ˆ

β = H

†

T is the best

weights, where H

†

is the Moore-Penrose generalized

inverse. In terms of(Huang et al., 2006), Extreme

Learning Machine utilizes such Moore-Penrose in-

verse approach for obtaining good generalization per-

formance with extremely fast learning speed. Unlike

some conventional methods, for example Backprop-

agation algorithm, ELM is able to avoid problems in

tuning control parameters (learning epochs, learning

rate, and so on) and keeping to local minima.

The procedure of ELM for single-layer feedfor-

ward networks is expressed as follows:

1. Choose arbitrary value for input weights w

i

and

biases b

i

of hidden neurons.

2. Calculate hidden layer output matrix H.

3. Obtain the optimal

ˆ

β using

ˆ

β = H

†

T.

Figure 2: The structure of ELM.

4 EXPERIMENTS AND RESULTS

As previously mentioned, the whole dataset is divided

into 5 categories (Sets A-E), each containing 100

EEG signals of 23.6s. In the present work, follow-

ing the work of(Subasi, 2007) and (Chandaka et al.,

2009), we use only Set A (normal EEG signals) and

Set E (epileptic seizure signals) to conduct the com-

putational simulation with ELMs. Therefore, totally

200 EEG signals are obtained, and each EEG signal

contains 4096 sampling points. The discrete wavelet

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

438

0 5 10 15 20

−6000

−4000

−2000

0

2000

4000

6000

Number of features

Value of features

0 5 10 15 20

−6000

−4000

−2000

0

2000

4000

6000

Number of features

Value of features

Figure 3: EEG data after feature extraction (Left: Set A. Right: Set E.).

transform (DWT) was used to extract features from

these two EEG data sets, since DWT has ability to

capture transient characteristics in EEG signals and

to localize them in both time and frequency content

accurately according to(Subasi, 2007). After the pro-

cess of multi-resolution decomposition, the EEG sig-

nals were decomposed into details D1-D4 and one

approximation A4. These approximation and details

were reconstructed from the Daubechies 4-wavelet

ﬁlter. In order to further reduce the dimensionality of

these extracted features, statistics over these wavelet

coefﬁcients were utilized and four statistical features

was used: (1) Maximum of the wavelet coefﬁcients

in each sub-band; (2) Minimum of the wavelet co-

efﬁcients in each sub-band; (3) Mean of the wavelet

coefﬁcients in each sub-band; (4) Standard deviation

of the wavelet coefﬁcients in each sub-band. Hence,

each EEG data ﬁnally consists of 20 features. From

Figure 3, we can see the difference between these

two EEG datasets clearly. The values of features (in

amplitude) extracted from EEG signals in Set E are

much larger than the values of features extracted from

EEG signals in Set A. Hence, these statistical fea-

tures which represent the signiﬁcant properties of the

two data sets can be utilized for evaluating the perfor-

mance of classiﬁers.

All the simulations are based on a 2.27 GHz 2-

core CPU with 2 GB memory. In order to compare

the performance of ELM classiﬁers, we also imple-

mented a backpropagation neural network (BPNN)

based on a Levenberg-Marquardt backpropagation

(LMBP) learning algorithm which is thought of as the

fastest method for training moderate-sized feedfor-

ward neural networks according to(Hagan and Men-

haj, 1994). For the BPNN and ELM, all of the in-

put value were normalized in the range of [-1,1]. The

weight vector w

i

and the threshold b

i

of ELM were

randomly generated in the range of [-1,1]. The num-

ber of hidden neurons in BPNN was set to 25 accord-

ing to (Subasi, 2007) and the number of hidden neu-

rons in ELM was set to 20. The performance of the

BPNN and ELM algorithms was evaluated by the fol-

lowing measures:

• Sensitivity: number of true positive detec-

tions/number of actually positive subjects.

Sensitivity = TP/(TP+ FN).

• Speciﬁcity: number of true negative detec-

tions/number of actually negative subjects.

Specificity = TN/(TN + FP).

• Classiﬁcation Accuracy: number of correct detec-

tions/total number of subjects.

Classification Accuracy = (TP + TN)/(TP +

TN + FP+ FN).

• Learning time: A measure of the time spent in

training classiﬁers.

Among these 200 EEG data (100 normal signals

and 100 epileptic seizure signals), half of the normal

EEG signals and half of the epileptic EEG signals

were used for training, and the rest for testing.

After training BPNN and ELM, the test data was

used to evaluate the performance of these two classiﬁ-

cation algorithms. Classiﬁcation results of the BPNN

and the ELM classiﬁers are displayed by confusion

matrices. The confusion matrices demonstrating clas-

siﬁcation results of two classiﬁers are given in Table

1. In terms of the confusion matrices, 5 epileptic sub-

jects were classiﬁed incorrectly by the BPNN classi-

ﬁer as normal subjects, however only 2 epileptic sub-

jects were classiﬁed incorrectly using the ELM clas-

siﬁer. Both classiﬁers identiﬁed correctly all normal

subjects. In order to calculate the average test per-

formance of two classiﬁers, the classiﬁcation experi-

ment was repeated for ten times and all the simulation

results were averaged over 10 trials. The results are

given in Table 2.

A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE

439

Table 1: Confusion matrices of the classiﬁers.

Output result

Classiﬁers Desired result

Epileptic Normal

Epileptic 45 5

BPNN

Normal 0 50

Epileptic 48 2

ELM

Normal 0 50

Table 2: Performance comparison of ELM and BPNN.

Average Average Average Average

Classiﬁers Learning Sensitivity Speciﬁcity Accuracy

Time(Seconds) (%) (%) (%)

ELM 0.0187 93.6 100 96.8

BPNN 52.9969 90.6 100 95.3

As quoted in Table 2, it is clear that the ELM

classiﬁer outperforms the BPNN classiﬁer in its av-

erage classiﬁcation accuracy of the EEG signals. It

is shown that performance rate with ELM is 96.8%

whereas with BPNN it is 95.3%. The ELM classiﬁer

recognized precisely all the normal and epileptic sub-

jects with sensitivity 93.6% and speciﬁcity 100%, fol-

lowed by the BPNN classiﬁer with sensitivity 90.6%

and speciﬁcity 100%. Thus the ELM classiﬁer can

obtain better generalization than the BPNN classiﬁer.

In addition, the learning time of the ELM classiﬁer

is 0.0187s while the learning time of the BPNN clas-

siﬁer is 52.9969s. The ELM classiﬁer can run 2834

times faster than the BPNN classiﬁer. Hence, in the

case of real-time implementation of epilepsy diagno-

sis support system, ELM classiﬁers are more appro-

priate than BPNN classiﬁers.

In Table 3, we present a comparison in classi-

ﬁcation performance achieved by different methods.

we have quoted results from our present proposed

method (Wavelet-ELM) and also from recently re-

ported in(Subasi, 2007) and (Chandaka et al., 2009).

The datasets used in these experiments are the same

(Set A and Set E). It is shown in the table that our

method generates better classiﬁcation accuracy com-

Table 3: Comparison of statistical parameters of various

classiﬁcation algorithms.

Type of ME model Cross-correlation Wavelet

Classiﬁer (Subasi2007) SVM(Chandaka2009) ELM

(%) (%) (%)

Speciﬁcity 94 100 100

Sensitivity 95 92 93.6

Accuracy 94.5 95.96 96.8

Table 4: Effect of change in window size.

Window Size Average Test Accuracy (%)

4096 96.80

2048 96.35

1024 96.15

512 95.76

256 94.18

pared with those of others.

In the next step of the experiment, we further in-

vestigate the generalization of our Wavelet-ELM clas-

siﬁcation scheme by varying the length of EEG sig-

nals. Four rectangular windows are formed, using

2048, 1024, 512 and 256 sampling points respec-

tively, such that each EEG signal is divided into 2,

4, 8, 16 small segments. Hence, in Case Study 2,

there are 200 EEG signals with 2048 sampling points

in each set; in Case Study 3, there are 400 EEG sig-

nals with 1024 sampling points in each set; in Case

Study 4, there are 800 EEG signals with 512 sampling

points in each set; in Case Study 5, there are 1600

EEG signals with 256 sampling points in each set. In

all case studies, we still use 50% EEG data from each

set for training and the remaining 50% for testing. Ta-

ble 4 describes the average test accuracy over 10 trials

using ELM classiﬁers for every case study. It is ob-

vious from the results that the larger the window size,

the greater the average test accuracy. Figure 4 sum-

marises the obtained results.

0 500 1000 1500 2000 2500 3000 3500 4000 4500

94

94.5

95

95.5

96

96.5

97

Window Size

Average Tesing Accuracy of ELM (%)

Figure 4: The relationship between the generalization per-

formance and window size.

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

440

5 CONCLUSIONS

In this paper, we have investigated the Wavelet-

Extreme Learning Machine classiﬁcation scheme for

identifying epileptic seizures. Using statistical fea-

tures extracted from the DWT sub-bands of EEG sig-

nals, ELM and BPNN classiﬁers are built and com-

pared according to their test accuracy and learning

time. The proposed system using the ELM classiﬁer

can achieve test accuracy as high as 96.8%, as com-

pared to BPNN classiﬁer and two recently-proposed

methods, where the test accuracies are 95.3%, 94.5%

and 95.96% respectively. In addition, this study also

shows that the ELM classiﬁer needs much less learn-

ing time compared to the stand-alone BPNN classi-

ﬁer for the task of epileptic seizure detection, which

demonstrates its potential for real-time implementa-

tion in an epilepsy diagnosis support system.

ACKNOWLEDGEMENTS

This work is ﬁnanced by the EU 6 Framework

Programme Project: Measuring and Modelling

Relativistic-Like Effects in Brain and NCSs.

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