40120130405007

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
INTERNATIONAL JOURNAL OF ELECTRONICS AND
6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 5, September – October (2013), © IAEME

COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 5, September – October, 2013, pp. 73-79
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2013): 5.8896 (Calculated by GISI)
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IJECET
©IAEME

WAVELET BASED EPILEPSY DETECTION IN EEG USING NEURAL
NETWORK
Shah Aqueel Ahmad
Royal Institute of Tech. & Science,
Hyderabad – 501503, India

Dr. Syed Abdul Sattar
Royal Institute of Tech. & Science,
Hyderabad – 501503, India

Dr. D. Elizabath Rani
GITAM University,
Vishakapatnam, AP, India

ABSTRACT
One of the many challenges in automated detection of seizure is to draw a line of demarcation
between seizure activity and non seizure activity. To accomplish this task, identification of related
features and their extraction from EEG plays a key role. The aim of this work is to develop a
new method for automatic detection and classification of EEG patterns using Discrete wavelet
transform and neural network. A wavelet based technique has been developed to extract features,
energy covariance, entropy, power spectral density min and maximum. These feature have been
applied to neural network for training and classification of epileptic and non epileptic. The good
accuracy is achieved.
Keywords: Electroencephalogram (EEG) signals, epileptic seizures, discrete wavelet transform
(DWT), artificial neural network (ANN).
1. INTRODUCTION
Brain is the most complex of all the other organs of human body. It generates electrical
signals to directly or indirectly control the entire body. The electrical activity of the brain generated
by millions of neurons is recorded by medical technique known as electroencephalogram (EEG). An
EEG channel is formed by taking the difference between potentials measured by placing two
electrodes, and records the summed potential of neurons. EEG is measured using 10-20 electrode
system. EEG is used for diagnosis of brain disorders like coma, to verify brain death, to monitor
anesthetic depth etc. One of the main applications of EEG lies in diagnosing epilepsy. A patient
suffering from epileptic attack has a distinctly different EEG as compared to excessive neural
activity in the brain.
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The detection of epilepsy, which includes visual scanning of EEG recordings for the spikes
and seizures, is very time consuming, especially in the case of long recordings. In addition, biosignals are highly subjective so disagreement on the same record is possible. These EEG signal
parameters extracted and analyzed using computers, are highly useful in diagnostics. The EEG
signals, like most biological signals, are inherently difficult to quantify. These signals can be
characterizes as non stationary. The EEG signals, like most biological signals, are inherently difficult
to quantify. These signals can be characterized as non stationary.
2. EXPERIMENTAL DATA
The EEG data used in the work is available at the Department of Epileptology, University of
Bonn . The complete data set consists of five sets (Z, O, F, N, S), each containing 100 single channel
EEG signals. Z is obtain from healthy subject with eyes closed and S is taken from ictal subject
These segments of EEG signals were cut out from continuous multi-channel EEG recordings and that
too after taken care for artifacts like muscle activity or eye movements. Each signal segment is of
23.6 s duration and Sampling frequency is 173.61 Hz, so each segment contains N = 4096 Samples.
3. DISCRETE WAVELET TRANSFORM
Wavelet transform can be defined as a spectral estimation technique in which any general
function can be expressed as a sum of an infinite series of wavelets. The decomposition of the signal
results in a set of coefficients called wavelet coefficients. The decomposition is done by low pass and
high pass filter in time domain. DWT successfully analyses the multi-resolution signal at different
frequency bands, by decomposing the signal into approximation and detail information. Here Data is
first preprocessed by removing dc component from the signal thereby achieving different levels of
decomposition for Daubechies order-2 wavelet with a sampling frequency of 173.6 Hz on each
signal of 4096 samples. Figure 1 shows the decomposition of the EEG signal x(n) at Nyquist
frequency which is the maximum useful frequency i.e. half of sampling frequency 86.8 Hz. Signal is
subjected to six level decomposition where each stage of this scheme consists of two digital filters.
The first filter, h[n] is the high pass filter or it is the discrete mother wavelet and the second, g[n] is
low-pass filter. After the first level of decomposition, the EEG signal (0-86.81Hz), is decomposed
into its lower resolution components, CA1 (0-43.4) Hz and higher resolution components, CD1
(43.4-86.81 Hz). by CAi and CDi where i is 1,2,3.... at each level of decomposition. The data after
decomposition to various levels has approximation and detail coefficients at each level of different
frequency resolution. These frequencies are to be then recombined in order to achieve subbands
delta, theta, alpha, beta, gamma at their perspective frequencies. The delta band is of frequency range
0-4 Hz so lower resolution components i.e. approximation coefficients CA7 (0-2.7 Hz) and CA8
(2.7- 4.05 Hz) are to be recombined. But the problem arises when the number of samples of CA7 and
CA8 mismatch. CA7 has 128 samples while CA8 has 64 samples. For this up-sampling of CA8 is
done to 128 samples and then the DELTA band is reconstructed successfully by recombination of
CA7 and up-sampled CA8 through Inverse Discrete Wavelet Transform using Daubechies order-2.
Similarly THETA subband is of frequency range 4-8 Hz so CA9 and up-sampled CD8 are
recombined. After recombining all coefficients falling in their respective subbands, delta, theta, alpha
band with 256 samples and beta with 1024 and gamma with 4096 samples are achieved as shown in
Table 1.

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Table 1. Subbands with their Frequency Range, Recombining Coefficients and no of Samples
Subband

Frequency

Coefficients

Samples

DELTA

0-4 Hz

CA7,CA8(up-sampled)

256

THETA

4-8 Hz

CD8(up-sampled), CA9

256

ALPHA

8-12 Hz

CD9, CA11(up-sampled)

256

BETA

12-30 Hz

CD11(up-sampled), CD10,

1024,CD6, CA 4

GAMMA

> 30 Hz

CD4(doubly up-sampled)

4096 CD1

4. FEATURE EXTRACTIONS
In this study, the discrete wavelet transform is used as a primary computational tool for
extracting features of the epileptic EEG signals at different resolutions. Decomposition of Epileptic
and Non- Epileptic data into Delta, Theta, Alpha, Beta, Gamma subbands are shown in figure 1 and
Figure 2 resp. It is apparent from figures that the amplitude of gross epileptic signal is considerably
higher than non-epileptic one. Also the amplitudes of subbands are significantly high in case of
epileptic data, especially in gamma subband.
1. Covariance
Covariance is defined as a measure of the dispersion of a set of data points around their
meanvalue. Assume some random variable X that have the sample values of each EEG subband
signal. Let the sample valueof X is Xi= {X1,X2, ....... Xn}. Where i represent a sample set from the
subbands delta, theta, alpha, beta, and gamma. The covariance can thus be expressed as

where µ is the mean value of the set X and n is the number of samples in the EEG dataset.
2. Energy
The energy of the signal is defined as the sum of squared modulus of the sample values of
any signal. The wavelet based energy of each decomposed subbands such as delta, theta, alpha beta
and gamma can be calculated using the formula

where X is the samples values in each subbands and N is the total number of samples.
3. Power Spectral Density (PSD)
The PSD is the amount of power per unit frequency as a function of frequency. Periodogram
is commonly used for computing PSD. This is computed by squared modulus of the Fourier
transform of the time series of the signal.
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భ
Min(w) = ೙ |ܺሺ‫ݓ‬ሻ|2

The Maximum and minimum values are estimated from the PSD of each EEG subbands can
be considered as feature for classification.
4. Entropy
Entropy is a numerical measure of the randomness of a signal. Entropy can act as a feature
and used to analyze psychological time series data such as EEG data.

Entropy is the statistical descriptor of the variability within the EEG signal and can be a
strong feature for epilepsy detection.
The above statistical features are extracted from each subband i.e.Delta, Theta, Alpha, Beta,
Gamma of each set to represent the time-frequency distribution of the EEG signals and are
nomenclatured as
1. Covariance of the coefficients in each subband (y1, y2, y3, y4)
2. Energy of the wavelet coefficients in each subband (E1,E2,E3, E4)
3. Power Spectral Density (max) and Power Spectral Density (min) of each subband (PSDmax1,
PSDmax2, PSDmax3,PSDmax4, ,PSDmin1, PSDmin2, PSDmin3, PSDmin4)
4. Entropy of wavelet coefficients in each subband (Eadelta,Eatheta ,Eaalpha,EaBeta) The
same thing is reflected in the figure 9.

Fig. 2: Block Diagram of Epileptic Seizure Classification systems
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5. Proposed Algorithm
Step 1: Read EEG Signal
Step 2: Using Wavelet Transform extract Delta, Theta, Alpha and Beta
Step 3: Extract Energy “E” from Delta, Theta, Alpha, Beta.
Step 4: Extract the Covariance
Step 5: Extract the Entropy
Step 6: Extract the Minimum Power Spectral Density.
Step7: Extract the features of maximum Power spectral Density
Step 8: Input the data of extracted features in Neural Network.
Step 9: Train the Neural Network
Step 10: Apply Threshold value and perform classification of epilepsy
Step 11: End
6. NEURAL NETWORKS
Artificial neural networks are computing systems made up of large number of simple, highly
interconnected processing elements (called nodes or artificial neurons) that abstractly emulate the
structure and operation of the biological nervous system. . Learning in ANNs is accomplished
through special training algorithms developed based on learning rules presumed to mimic the
learning mechanisms of biological systems. There are many different types and architectures of
neural net-works varying fundamentally in the way they learn. In this paper, feed forward back
propagation neural network considered.
From the decomposed EEG signal the features calculated for each sub band a total of 20
features are considered for classification. Neural network is designed with 20 input nodes and one
output node. The network is trained with parameters a Lavenberg Marquardt algorithm with
momentum factor included (TRAINLM)was used for training. The stopping criterion was specified
to be 0.001 Root Mean Square Error (RMSE). The training was stopped when the RMSE between
the network outputs and the targets was lesser than or equal to 0.001. The learning rate was fixed at
0.5. The number of training epochs was fixed uniformly at 1000. . The output from threshold
detector value is greater than threshold value the subject considered as epileptic otherwise normal.
The threshold value of 0.5 is considered. In this work. Feed Forward back propagation is used.
7. RESULTS AND DISCUSSIONS
Each EEG signal is decomposed using DWT .from each sub band the five statistical features
Energy, Covariance, Entropy, Power Spectral Density min, and Power Spectral Density max
calculated and average values are tabulated in Table-I. This table shows the deviation of values for
different subjects. In each sub band the variation of values of features indicting the Presence of
epilepsy
Features of two different Subjects and each having 300 sets. Epoch duration of 7 second is
considered for each type dataset. Therefore 600 feature sets are generated from each subject and the
total feature sets are 12000. The target value are fixed as per the type of the subjects. For epileptic
subject is assigned a maximum value of 0.9 and normal subject is assigned 0.1. These feature sets are
arranged in random manner before neural network training. The trained network is simulated with
normal and epileptic subject's data.
The performance of the classification is evaluated in terms of specificity, sensitivity and
classification accuracy .The performance analysis is tabulated in Table II.

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The Fig2 shows the plot of classification performance of Feed Forward Back Propagation
neural network.

Fig. 3: Performance analysis of Epileptic Seizure Classification systems

Work

Specificity

Sensitivity

Accuracy

Proposed Method

100

99.8

99.899

98.59

97.69

98.32

Zakir Hussain and K.
S Rao(ICINT 2012) ,
IPCSIT vol 37
2012[7]
Nidal Rafiuddin , Y.U
Khan

96.5

ICMSPCT 2011[3]
Abibullaev Berdakh
IEEE ICIT 2009[8]

96.49

94.39

Table. 2: Performance evaluation Neural network classifier

78

95.8


8. CONCLUSION
Diagnosing epilepsy is a difficult task requiring observation of the patient, an EEG, and
gathering of additional clinical information. An artificial neural network that classifies subjects as
having or not having an epileptic seizure provides a valuable diagnostic decision support tool for
neurologists treating potential epilepsy, since differing etiologies of seizures result in different
treatments. In this work, classification of EEG signals was examined. The features are extracted
using wavelet transform technique. Five features , Energy, Covariance, Entropy and PSD min and
max from each sub band of EEG are extracted The generated data from wavelet technique are given
to ANN for training of normal and abnormal EEG conditions. The ANN is used to discriminate
between the two tasks with a success rate of 99.89%.
Further research is needed to find more elaborated memory architectures and its appropriate
training algorithms. Neural networks as classifiers have here a high potential because they can
compute in real time with a high numbers of features. This characteristic enable the development and
construction of transportable devices, improving substantially the quality of life of epileptic patients
intractable by medication and that must learn to live with seizures.
9. REFERENCES
[1]

Abdulhamit Subasia, Ergun Erc¸elebi "Classification of EEG signals using neural network
and logistic regression." In Computer Methods and Programs in Biomedicine (2005) 78, 8799.
[2] D. Najumnissa, S. ShenbagaDevi " ntelligent identification and classification of epileptic
seizures using wavelet transform" Int. J. Biomedical Engineering and Technology, Vol. 1,
No. 3, 2008,293-313.
[3] Y Nidal Rafiuddin1Yusuf Uzzaman Khan and Omar Farooq, “Feature Extraction and
Classification of EEG for Automatic Seizure Detection”, IEEE International Conference on
Multimedia, Signal Processing and Communication Technologies, pp. 184-187, 2011.
[4] EEG time timeseries (epilepticdata)(2005,Nov.).
http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html.
[5] J. Gotman., “Automatic recognition of epileptic seizures in the EEG,” Clinical
Neurophysiology, vol. 54, pp. 30 –540, 1982.
[6] Y.U. Khan, J. Gotman, “Electroencephalogram Wavelet based automatic seizure detection
iintracerebral”, linical Neurophysiology, vol. 114, pp. 899-908, 2003.
[7] Sheikh.Jakeer Husain and K.S Rao “Epileptic Seizures classification from EEG Signal using
Neural Network” IPCSIT vol.37 (2012) IACSIT press, Singapore ICINT 2012.
[8] Abibullaev Berdakh, Seo Hee Don, “Epileptic Seizures Detection using Continuous Time
Wavelet Based Artificial Neural Networks”2009 IEEE sixth International Conference on
Information Technology: New Generation.
[9] Alexandros T.Tzallas, M.G. Tsipouras “Epileptic Seizure Detection in EEGs Using TimeFrequency Analysis” IEEE TRANSACTION ON IT IN BIOMEDICINE, VOL.13, NO.5,
SEPTEMBER 2009.
[10] G. Hemalatha, Dr.B. Anuradha and V. Adinarayana Reddy, “Efficient Extraction of Evoked
Potentials from Noisy Background EEG”, International Journal of Electronics and
Communication Engineering & Technology (IJECET), Volume 4, Issue 1, 2013,
pp. 216 - 229, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.
[11] Imteyaz Ahmad, F Ansari and U.K. Dey, “A Review of EEG Recording Techniques”,
International Journal of Electronics and Communication Engineering & Technology
(IJECET), Volume 3, Issue 3, 2012, pp. 177 - 186, ISSN Print: 0976- 6464, ISSN Online:
0976 –6472.
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