JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
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ARTEFACTS REMOVAL IN EEG SIGNAL USING WAVELET TRANSFORM AND
ARTIFICIAL NEURAL NETWORK
Prof Dr. R. Kawitkar and Ms. Rohini More.
Abstract— Electroencephalography is a medical imaging technique that reads scalp electrical activity generated by
brain structures. The electroencephalogram (EEG) is defined as electrical activity of an alternating type recorded
from the scalp surface after being picked up by metal electrodes and conductive media. The EEG measured directly
from the cortical surface is called electrocortiogram while when using depth probes it is called electrogram. We will
refer only to EEG measured from the head surface. The recognition of epileptic waveform from EEG signal is important physiolgical task as epilepsy is still one of the most frequently occurring disorder. The main goal of this paper is
to provide new method to diagnose the epileptic waveform directly from the EEG, by performing quick signal
processing which makes it possible to apply in on-line monitoring system. Use of ANN makes the rate of recognition very high and also makes the on-line monitoring and ‘paperless’ task of EEG analysis.
Index Terms: Electroencephalography, EEG, Wavelet transform, Artificial Neural Network(ANN).
—————————— ——————————
1 INTRODUCTION
EEG signals are the electrical activities in the cortex or
on the surface of scalp causing by the physiological
activities of the brain. In general, EEG signals are the
typical multi-dimensional nonstationary random
processes. Classification of the changes of these special waves is critical for understanding of brain functions. EEG was discovered by Hans Berger in 1929.
Brain patterns form wave shapes that are commonly
sinusoidal. These EEG signals are measured from
peak to peak and normally range from 0.5 to 100 V in
amplitude, which is about 100 times lower than ECG
signals. Brain waves have four main spectral components as (>13 Hz), (8-13 Hz), (4-8 Hz) and (0.5-4
Hz). Epileptic seizures are occational expressions of
fundamental and continuous brain disorder. In most
cases they appear as high magnitude waves of frequency around 3Hz. So, when these seizures will occur , reflected by waves.
complishes the recognition of epileptic waveforms. Figure-1 shows the detection method.
2 METHODOLOGIES
The epileptic waveform detection is done in two steps In
the first step by using multi-resolution wavelet decomposition, we obtain different spectral components(α, β, δ, θ)
of the measured signal. These components serve as input
signals for the artificial neural network(ANN), which ac• Prof. Dr. R.S. Kawitkar : working as a Professor in E&TC Dept. in Sinhgad college of Engineering in Pune, Maharashtra(India)
Fig1: The detection method
2.3 DISCRETE WAVELET TRANSFORM
Calculating wavelet coefficients using contnuous wavlet
transform is fair amount of work and it will generate an
awful lot of data, hence discrete wavelet transform is
suggeted.
• Rohini More: Post-graduate student of. Sinhgad college of Engineering in
Pune, Maharashtra(India)
© 2010 JOT
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JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
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2.2 ANN BASED DETECTION
ANN uses standard back propagation
feed
forward three-layer network having one hidden layer.
Input layer has 650 nodes, hidden layer ha 11 nodes and
1 output node, since ANN has to distinguish between
epileptical wave and normal wave.
3 GUI FOR EEG ANALYSIS
Fig2: The DWT algorithm
Figure 2 illustrates this procedure, where x[n] is the original signal to be decomposed, and h[n] and g[n] are lowpass and highpass filters, respectively. The bandwidth of
the signal at every level is marked on the figure as "f".
The signal is band limited to 60 Hz using a band
stop filter. Then we take 4 level wavelet decomposition.
We use ‘sym6’ wavelet because it is smooth wavelet than
other wavelet such as sym2,sym3 etc. so reconstruction of
EEG signal using various wavelet coefficients is possible
with less noise. ‘sym’ wavelet family is modification to
‘db’ wavelet family so we use ‘sym6’ wavelet. ‘symN’ is
symmentrical wavelet family where N is order of filter,
2N is length of filter. We get the alpha, beta, delta and
theta waves using filters at various decomposition levels.
Delta waves are used for detection of Epilepsy so we have
taken four level decomposition which gives us signal up
to 3.5 Hz only and then delta wave can be easily separated using Butterworth band pass filter having cutoff
frequency 1, 3 Hz , order of filter is 10.
Fig4: The GUI ued for analysis
4. RESULT AND CONCLUSION
This new method of detection will provide a good accuracy
and GUI will help neurologist for on-line analysis of EEG
signal. We have tested our tool for 24 data samples out of
which 23 are correctly detected. And we have verified these
results from neurologist, so our accuracy of detection of
disorder is 95.83%
ACKNOWLEDGMENT
The authors wish to thank Principal , Head Of The department for their kind support
.
REFERENCES
Fig3: EEG signal and its spectral components after WT
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2, 2002.
[2] James N. Knight “Signal Fraction Analysis and Artifact Removal in
EEG” 2003.
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Author:
Dr. R. S. Kawitkar: This author has 20 year of experience of teaching. Currently working with Dept. of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune as Professor. He has done Ph.D. (Electronics Engineering) from Amravati
University, 2005. MBA (HRM) from YCMOU, (Nashik in 1998).M.S.
(Electronics & Control) from BITS-Pilani in 1994 (1st class), B.E.
(Electronics) from Amravati University in 1990 (1st class).
Ms. Rohini More: This author is a post graduate student of E&Tc
engineering, alo has a four year of experience in teaching. Compteted her B.E. from Pune university in 2003 with First Classs & Ditinction. He is a life time member of ISTE Society.
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