MULTIVARIATE FEATURES EXTRACTION
FOR DETECTION OF EPILEPTIC SEIZURES
IN ELECTROENCEPHALOGRAM
Abstract: Recently more researchers in the biomedical engineering
have introduced many techniques which try to detect
epileptic seizures in electroencephalogram (EEG). The
main objective of this paper is to develop technique that is
capable of differentiating between epileptic and normal
signals. This technique consists of two stages. The first
stage is the features extraction and the second stage is the
classification of these features. Fast Fourier transform,
autoregressive model, and other nonlinear features were
used as input features for the classification phase. The
classification phase consists of three neural network
classifiers and a majority decision to classify the input
features. The classification accuracy of the proposed
technique was superior compared to other techniques for
accuracy. The proposed technique after using the majority
method has accuracy equal 99.5%.
Keywords: Epileptic seizures, autoregressive model, correlation
dimension, Lyapunov exponent, and artificial neural
network.
1. INTRODUCTION
Epilepsy is the second common serious neurological disorder after
stroke. One percentage of the people in the world suffering from
epilepsy and 30% of epileptics are not helped by medication [1]. In
Egypt, 643,639 people suffer from epilepsy [2]. An epileptic seizure is
an abnormality in EEG recordings and is characterized by a brief and
episodic neuronal synchronous discharge with dramatically increased
amplitude. There are two types of epilepsy, generalized epilepsy and
partial epilepsy. Generalized epilepsy involves the entire brain at once,
whereas partial epilepsy involves a portion of the brain. The epileptic
seizure may cause a short period of amnesia, attack of abnormal rage,
sudden anxiety or fear, a moment of incoherent speech or mumbling or
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several twitches like contractions of muscles, usually in head region
[3].
There are two different types of EEG signals depending on where
the signal is taken in the head: scalp or intracranial. For scalp EEG, the
focus of this research, small metal discs, or electrodes, are placed on the
scalp with good mechanical and electrical contact. Intracranial EEG
(IEEG) is obtained by special electrodes implanted in the brain during a
surgery [4]. This research will concentrate in building a computer aided
diagnosis system to classify the epileptic signals and normal signals by
analyzing the EEG signals. This system consists of two stages. The first
stage is the features extraction from EEG signals and the second stage
is the classification of these features, as shown in figure 1. The
extracted features are fast Fourier transform coefficients, autoregressive
model parameters, and other nonlinear features.
EEG
Signal
Features
Extraction
Classification
Output
(Normal or
Abnormal)
Figure 1: Block diagram of proposed technique
The classification technique which used to classify these features
is artificial neural network (ANN). The classification phase consists of
three neural network classifiers for each feature and a majority
decision.
2. REVIEW OF PREVIOUS WORK
In the last decade, different techniques of classification and input
representation for the automatic recognition of EEG patterns have been
developed. For example, Chaovalitwongse et al. [1] applied data
mining techniques to EEG data in order to classify between the brain's
normal and pre-seizure epileptic activities through the measure of the
brain dynamics. These measures include Lyapunov exponents, angular
frequency, and Entropy. They used the Support vector machines as
classification technique. They considered the seizure is involving two
states; pre-seizure, and post-seizure. The total classification accuracy of
this technique was 88.3%. The probabilities of correctly predicting of
pre-seizure, post-seizure and normal EEG's were about 90%, 81%, and
94%, respectively.
Adeli et al. [4] applied discrete Daubechies with order four and
harmonic wavelets for analysis of epileptic EEG records. They found
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that the analysis of EEG signals by wavelet transform improved
understanding of the mechanisms causing epileptic disorders, and this
algorithm can be extended to create computational models for
automatic detection of epileptic discharges.
In [5], Subasia et al. used fast Fourier transform (FFT) and
autoregressive (AR) model with maximum likelihood estimation
(MLE) as features. They classified these features by using Artificial
Neural Networks (ANNs) into two-group categorization: epileptic
seizure and non-epileptic seizure. The classification accuracies were
91.6% and 92.3% for (FFT) and (AR) with (MLE), respectively.
Gigola et al. [6] analyzed the EEG signals by using accumulated
energy based on wavelet analysis to predict the epileptic seizure onset.
They showed that the accumulated energy with wavelet would
contribute for predicting epileptic seizure onset from EEGs signals
because this method predicted the seizure onset of 12 cases from 13
preseizure signals.
Abdulhamit Subasi [7] used a new approach based on neural
network and fuzzy logic technologies for detection of epileptic seizures.
the incorporation of both heuristics and deep knowledge to exploit the
best characteristics of each. A dynamic fuzzy neural network (DFNN)
is used in the classification of EEG signals. EEG signals were
decomposed into the frequency sub-bands using discrete wavelet
transform (DWT). Then these sub-band frequencies were used as an
input to a DFNN with two discrete outputs: normal and epileptic. The
classification accuracy of the DFNN model and the neural network
were 93% and 92%, respectively. He concluded that the accuracy rate
of DFNN model were higher than that of neural network model.
Gao et al. [8] applied recurrence time statistics to detect epilepsy
from continuously monitored EEG signals. They compared between
recurrence time method and nonlinear method, such as the short-term
maximum Lyapunov exponents (STLmax) method. They found that the
detection using the STLmax method is still much noisier and less
accurate than that using the recurrence time based method. The
recurrence time based method is faster at least 10 times than STLmax
method, and much easier to use for automatic seizure detection.
Harikumar et al. [9] developed a fuzzy classification model for
epilepsy risk level analysis for EEG signals. They extracted five
features from EEG signal for each epoch. These features are energy of
the epoch, the number of positive and negative peaks, spikes, the total
number of spikes and sharp waves in the channels, and variance of each
epoch. The percentage performance for Fuzzy systems was as low as
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40%. They tried to enhance this performance by using an optimization
method. After optimization, performance became 80 %.
Hoeve [10] presented an algorithm for the detection and
classification of epileptic activity in the EEG using independent
component analysis (ICA). His results showed that the sensitivity and
the selectivity of classification were 74% and 19%, respectively.
McSharry et al. [11] focused on the detection of epileptic seizures
from scalp EEG recordings by linear & non-linear technique, and
multidimensional probability evolution (MDPE). Linear analysis
methods have been used to detect linearly changes in EEG signal, such
as variance, power spectrum, and auto-correlation function. Nonlinear
analysis methods have been used to detect dynamical changes in EEG
signal, such as correlation dimension, correlation density, crosscorrelation integral, Lyapunov exponent similarity measures, and nonlinear predictability. They found that the variance and MDPE were able
to detect the seizure in each of the ten scalp EEG recordings.
Esteller [12] detected seizure onset by using automatic system
which consists of several stages: preprocessing, processing (feature
extraction and selection), classification (training of a neural network),
and validation (testing of the designed neural network). He focused in
his research on the extraction and selection of features which intended
to detect seizure onset. He used several features such as correlation
dimension, lyapunov exponent, energy, energy derivative, accumulated
energy, nonlinear energy, zero crossings, power spectrum, power
spectrum of frequencies bands, coherence, fractal dimension, derivative
of fractal dimension, accumulated fractal dimension, entropy, mutual
information, mean frequency, cross correlation, 4th coefficients of 5th
order AR model, absolute value of 4th wavelet coefficients, and spike
detector. He used k-factor equation to select discriminate feature. He
didn't implement the classification stage and validation stage.
In [13], McGrogan presented a system for automatically detection
of epileptic seizures within EEG recordings. He proposed the power
parameters, discrimination of single parameters, reflection coefficients,
prediction error, total and band-limited power, and rhythmicity as
features. He used artificial neural networks to classify EEG signals as
either seizure or non-seizure. His results presented in two measures,
sensitivity and specificity. The specificity and the sensitivity of
classification were 88.5% and 67.5%, respectively. From the specificity
and the sensitivity, the total classification accuracy of this system was
78%.
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From previous work, we indicate that the highest accuracy of the
previous techniques for detection epileptic seizures is 93%. In this
paper, we develop technique that is capable of differentiating between
epileptic and normal signals and this technique reached the accuracy of
99.5% compared to the accuracy of the technique in [7] which equal
about 93%.
3. PROPOSED TECHNIQUE
3.1 EEG Data Acquisition
We get the EEG data from clinical Neurophysiology unit of El-kasr
El-einy hospital. The personal computer picked up EEG signals by data
acquisition system which contains data acquisition card (Unilink type,
Schwarzer manufactured) and signal processors. EEG data which
produced by EEG device (model: Etas 32, electrode placement system:
10-20 international system) can be recorded into computer memory
using this card. Brainlab programming package was used. The brainlab
program gives facilities to select desired montage. The data acquisition
system provides real time data processing. Eighteen channels of EEG
are recorded simultaneously, where all the electrodes are referenced to
common potential (referential montage). The EEG is sampled at 250
Hz. The EEG is broken down into epochs for the purpose of feature
extraction, where each epoch is 10 seconds. The five patients with
known clinical epilepsy findings are undertaken for classification. We
select the normal and epileptic forms from the recording by assisting
from expert neurophysiologist who describes the clinical case for this
recording. The software for analyzing the EEG data was implemented
using Matlab 7.
3.2 Features extraction
3.2.1 Autoregressive Model
The Autoregressive (AR) method is an alternative way to calculate
the spectrum of signals. It is especially useful when the signals have
low signal-to-noise ratio. The autoregressive (AR) model of an order p
can be written as:
xt  1 xt 1  .....   t  p xt  p  zt
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(1)
Here: xt is the time series of data, Zt is a purely random process
and the parameters α1… αp are called the AR coefficients. The name
"autoregressive'' comes from the fact that xt is regressed on the past
values of itself. The selection of the model order in AR spectral
estimation is a critical subject. The most popular approach to find the
optimal order of Autoregressive model was done by trial and error.
After using different orders of autoregressive model, we found the 5th
order gave best performance for classification. So, we used 5 AR
coefficients in our experiment. There are different methods to calculate
AR coefficients. In our experiment, we estimate the autoregressive
(AR) model parameters by using Yule-Walker method [4].
3.2.2 Fast Fourier transform
The Fourier transform (FFT) is a Mathematical transformation
which is applied to signals to obtain frequency components of the
signals. We used the first 18 coefficients of FFT on our data set as input
to the ANN classifier, as in [4]. We found that the 18 coefficients give
the worst performance about 67.8%.
We used other method to increase the performance of classification.
This method is Fisher’s discriminant ratio (FDR) [11], and it is
considered as a feature selection method. By using FDR, we select
different numbers of features (3, 5, 10, 15, 20, 25, 30, and 35) from
FFT according to the variance between them. We found that the best
number of features is three features, which give us the best
performance, as shown in fig. 2.
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100
95
Accuracy of classification
90
85
80
75
70
65
60
55
50
0
5
10
15
20
25
FFT coefficients
30
35
40
Figure 2. The accuracy of classification of FFT with FDR
3.2.3 Nonlinear features
Nonlinear (dynamical) system is defined as the system which is
moving or changing in time. The brain is considered as dynamical
device. In fact there is ample evidence for nonlinearity, in particular, in
small assemblies of neurons. Brain electrical activity sometimes
exhibits unpredictable (chaotic) EEG pattern. This unpredictable
behavior is called chaos. Dynamical system can be described by several
features, such as correlation dimension, Lyapunov exponents,
approximate entropy, etc. In this research, we calculate two important
chaotic parameters, namely correlation dimension (D2) and largest
Lyapunov exponents (λ k).
The mathematical description of a dynamical system consists of
two parts: the state which is a snapshot of the process at a given instant
in time and the dynamics which is the set of rules by which the states
evolve over time. In the case of the brain as a dynamical system, the
available information about the system is a set of EEG measurements
from scalp electrodes. There is no mathematical description of the
underlying dynamics of the brain because the total number of state
variables is not known. Therefore, to study the dynamics of such
system, we first need to reconstruct the state space trajectory. The most
common method to do this is using delay time embedding theorem to
create a larger dimensional geometric object by embedding into a larger
m-dimensional embedding space. We can know suitable m by using the
false nearest neighbor (FNN) algorithm. The dimension m in which
false neighbors disappear is the smallest dimension that can be used for
the given data. From knowing the dimension m, the state space
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trajectory of system can be reconstructed. So, we can estimate the
dynamics of this system.
3.2.3.1 Correlation dimension (D2)
The Grassberger–Procaccia algorithm [15] uses a correlation
integral C(r) to represent the object, which is defined as the average
number of neighbors each point in the reconstructed phase space has
within a given distance r, given as in equation (2),
C (r ) 
1
 [r  x(i)  x( j) ]
N p i, j
(2)
Here: ||..|| symbolized the Euclidean distance between reconstructed
state vectors x(i) and x(j), Np=k(k-1)/2 is the number of distinct pairs of
reconstructed state vectors, θ is the Heaviside unit step function (i.e.,
θ(x)=0 when x<0 and θ(x)=1 when x ≥ 0). The correlation dimension
D2 is defined as the slope of the linear region of the plot of log (C(r))
versus log(r) for small values of r [16]. That is presented in equation
(3),
log c(r )
r  0 log r
D2  lim
(3)
3.2.3.2 Largest Lyapunov Exponent (λk)
Lyapunov exponents quantify the sensitivity of the system to initial
conditions, which is an important feature of chaotic systems and
describes how small changes in the state of a system grow at an
exponential rate and eventually dominate the behavior. Lyapunov
exponents are defined as the long time average exponential rates of
divergence of nearby states [15]. A positive, finite, value of λ means an
exponential divergence of nearby trajectories. If a system has at least
one positive Lyapunov exponent, then the system is chaotic. The larger
the positive exponent, the more chaotic the system becomes. Lyapunov
exponents will be arranged such that λ1 ≥λ2≥.... ≥λn, where λ1and λn
correspond to the most rapidly expanding and contracting principal
axes, respectively. The largest Lyapunov exponent λ1 is calculated as a
measure of the chaotic behavior of the system using the Wolf algorithm
[16]. Consider two trajectories with nearby initial conditions on an
attracting manifold. When the attractor is chaotic, the trajectories
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diverge, on the average, at an exponential rate characterized by the
largest lyapunov exponent λ1. The algorithm used is as follows,
1. Compute the distance d0 of two, very close points in the
reconstructed phase space orbit.
2. Follow both points as they travel a short distance along the
orbit. The distance d1 between them is calculated.
3. If d1 become too large, one of the points is kept and an
appropriate replacement for the other point is chosen.
4. The two points are now allowed to evolve again following
steps 1-3.
5. After s propagation steps, the largest lyapunov exponent λ1
is estimated by using equation (4),
1 
 d (t ) 
1 S
log 2  1 k 

ts  t0 k 1
 d0 (tk 1 ) 
(4)
In this work, the correlation dimension and largest lyapunov
exponent were calculated using a software package for signal
processing with emphasis on nonlinear time-series analysis [14].
3.3 Classifier
In our experiment, we used artificial neural network as classifier. There
are different types of artificial neural networks. We used a feed forward
multilayered neural network which is a commonly used type of
artificial neural networks. It consists of a layer of input neurons, a layer
of output neurons and one or more hidden layers. We used one hidden
layer for this network. We used each features group (AR, FFT, and
Nonlinear features) as input features to a neural network and we
computed the performance of each neural network in each case. We
used five input neurons for 5 order of AR, three input neurons for three
best features of FFT, and five for nonlinear features. The number of
output neuron is two. The most popular approach to find the optimal
number of hidden neurons was done by trial and error. The data set
used for training the network equal 432 signals (216 for normal and
epileptic signals) and the data set used for testing the network equal 216
signals (108 for normal and epileptic signals).
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3.4 Majority method
The majority method is a novel method to enhance the accuracy of
classification. There are three networks for classification; the network
1, 2, and 3 are used for validating three FFT features, five AR features,
and and five nonlinear features of testing data, respectively. The
majority method depends on the result of simulation from each
network. We enter the result of testing of each network to majority
stage. The outcome of the majority stage is majority result from all
features. If the output of network 1 and network 2 is normal case, the
majority of total output is normal case, as in Fig. 3.
Testing
data
AR features
Net1
Nonlinear
features
FFT features
with FDR
Net2
1
Net3
0
1
Majority method
1
Normal case
Figure 3. Block diagram of validation stage with majority method
4. RESULTS
The results of classification are expressed in term of the accuracy. The
accuracy is the total percentage of correct predictions. The results
presented in following table which contain the classification
performance of ANN for fast Fourier transform with FRD,
autoregressive, and nonlinear features. In table 1, we showed that the
AR, FFT, and nonlinear features gave 99.07%, 90.3%, 98.6%,
respectively. After using the majority method, the accuracy of
discriminate between epilepsy and normal case became 99.5%, as
shown in table 1. From this results, we noted that the majority method
improved the performance of the system by 0.5% relative to the AR
feature alone but this improvement is significant improvement which is
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helped the neurophysiologist to give correct diagnostic decision for any
case. Conspicuously, the majority method of FFT, AR, and nonlinear
features enhance the performance and give the best accuracy of
classification than FFT, AR, or nonlinear features alone.
Table 1: The accuracy of classification of different features and after using majority
method
Accuracy
3 FFT
coefficients
by FDR
5 AR
coefficients
D2 with λ k
Majority
method
90.3%
99.07%
98.6%
99.5%
5. CONCLUSION
From the previous results, we conclude that the system of ANN
classifier with autoregressive model is better for detection epileptic
seizures than that with FFT, or nonlinear features. The majority method
with three classifiers of three features is best method for enhancement
the performance of system. From this system, we can create
computational models for automatic detection of epileptic discharges in
EEG signals that can be used to predict the onset of seizure.
6. ACKNOWLEDGEMENT
The authors would like to thank El-kasr El-einy Hospital, especially
the clinical neurophysiology unit, for their assistance with regards to
accessibility to their equipment, the acquisition of the samples used in
this study and their expertise in EEG analysis.
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