International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 7- Feb 2014
Analysis of Psychological Disorders in Children Using
Discrete and Stationary Wavelet Transforms
Dr. K. Chandra Bushana Rao 1
Dr. A. Guruva Reddy 2
Ch. Srinivas 3
1 Associate Professor, Dept of ECE, JNTUK, UCE, Vizianagaram.
2 Professor & Head, Dept of ECE, DVR & Dr. H.S MIC College of Tech, Kanchikacherla
3 Associate Professor, Dept of ECE, DVR & Dr. H.S MIC College of Technology, Kanchikacherla
Abstract. Electrical impulses generated by nerve
firings in the brain diffuse through the head and can
be measured by electrodes placed on the scalp, is
known as electroencephalogram (EEG). The EEG
gives a coarse view of neural activity and has been
used to non-invasively study cognitive processes and
the physiology of the brain. The analysis of EEG data
and the extraction of information from this data is a
difficult problem. However many procedures and
steps of classical algorithms could improve the
results in terms of removing artifacts. The artifact
removals are used to promote knowledge of the
boundaries between normality and abnormality in
clinical EEG work & to enhance the understanding of
the EEG as a phenomenon with psycho physiological
implications. The wavelet transforms are used to
remove the artifacts in EEG signals. In this paper, we
use two types of wavelet transforms, they are discrete
wavelet transform and stationary wavelet transform.
Then EEG signals are normalized to zero mean and
unit variance. The signal is segmented into 4
sections. From the EEG features, we can say the
children having psychological disorder or not.
1. Introduction
The electroencephalogram (EEG) was first measured
in humans by Hans Berger in 1929. Electrical
impulses generated by nerve firings in the brain
diffuse through the head and can be measured by
electrodes placed on the scalp, & is known as
electroencephalogram
(EEG)
[3].
Electroencephalogram(EEG) analysis is an extensive
research avenue related to understanding the inner
working of the human mind.
Although EEG is designed to record cerebral activity,
it also records electrical activities arising from sites
other than the brain. The recorded activity that is
ISSN: 2231-5381
not of cerebral origin is termed artifact and can be
divided into physiologic and extraphysiologic
artifacts. While physiologic artifacts are generated
from the patient, they arise from sources other than the
brain (ie, body). Extraphysiologic artifacts arise from
outside the body (ie, equipment, environment).
Artifact removal is the process of identifying and
removing artifacts from brain signals. An artifact
removal method should be able to remove the artifacts
as well as keep the related neurological phenomenon
intact. Common methods for removing the artifacts in
EEG signals are linear filtering, linear combination
and regression, blind source separation, principle
component analysis, wavelet transform, nonlinear
adaptive filtering and source dipole analysis (SDA)[2].
Wavelet transforms are signal-processing algorithms
able to functionally localize a signal in both time and
frequency space, thus allowing transformed data to be
simultaneously analyzed in both domains (frequency
and time). The wavelet transforms are discrete
wavelet transform and stationary wavelet transform.It
consists of approximation and detailed coefficients.
The wavelet transform of the noisy signal generates
the wavelet coefficients which denote the correlation
coefficients between the noisy EEG and the wavelet
function. Depending on the choice of mother wavelet
function (which may resemble the noise component),
larger coefficients will be generated corresponding to
the noise affected zones. Ironically smaller
coefficients will be generated in the areas
corresponding to the actual EEG.
2. Psychological disorders
Because of stress in educating children, the children
are affected by the psychological
disorders are
PTSD (post Traumatic Stress Disorder), ADHD
(Attention DeficitHyperactivity Disorder) and
Autism spectrum Disorder(ASD).
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 7- Feb 2014
PTSD is a severe anxiety disorder, that can develop
after exposure to any event that
results in
psychological trauma.This event may involve the
threat of death to oneself (or) to someone else, or to
one’s own (or) someone else’s physical, sexual,(or)
psychological
integrity,overwhelming
the
individual’s ability to cope. ADHD occurs when the
children concentration deficit exists. ASD occurs
where the difficulty in learning and comprehending
the arithmetic exists.
Start
EEG samples
Artifact removal using
wavelet transforms
3. Method
The overall processing procedure for analysis of
psychological disorders in children from EEG signals
is shown in Fig.1. First, the child EEG signal is
extracted from chb-mit (Children’s hospital BostonMassachusetts Institute of Technology) database. The
artifacts in child EEG signals are removed by using
wavelet transforms. The wavelet transforms are
discrete and stationary wavelet transforms. Signal
normalization is an essential part of patient
independent algorithms used for the analysis of
physiological signals and the automatic detection of
features and salient points.
Normalization is an essential tool for correcting
broad level amplitude differences in recorded signals,
for example between different patients, to allow
patient independent classification. So, for the signal
analysis, the signal is normalized to zero mean and
unit variance. Segmentation means the division of an
EEG into sections. The signal is segmented into 4
sections. Then we get the EEG features. By
comparing the EEG frequencies (Delta, Theta, Alpha,
Beta, Gamma) with normal child EEG frequencies
we can say the children having psychological
disorder or not.
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Normalization
Segmentation
EEG Features
Stop
Fig. 1 Flow diagram shows the overall procedure for
analysis of psychological disorders in children from EEG
signals
4. Results
The 11 years girl EEG sample is extracted from the
chb-mit (children’s hospital boston - massachusetts
Institute of technology) database. The 11 years girl
EEG signal is generated using MATLAB processing
toolbox as shown in Fig: 2.a. The artifacts are
removed by using discrete wavelet transform. This
wavelet transform consists of approximation and
detail signals as shown in Fig: 2.a, Fig: 2.b. For the
signal analysis, the 11 years girl EEG signal is
normalized to zero mean and unit variance as shown
in Fig: 2.c. Then the signal is segmented into 4
sections as shown in Fig: 2.d. The EEG input signal
frequencies are shown in Fig: 2.e. After removing the
artifacts, the signal frequencies are shown in Fig: 2.f.
From the EEG features, the EEG frequencies are
Delta: 7Hz, Theta: 9Hz, Alpha: 22Hz, Beta: 48Hz,
Gamma: 115Hz. By comparing the 11 years girl EEG
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 7- Feb 2014
frequencies with normal child EEG frequencies
(Delta :< 4Hz, Theta: 4-8Hz, Alpha: 8-13Hz, Beta:
13-30 Hz, gamma: 30-100 Hz). The 11 years girl
EEG frequencies are beyond the normal child EEG
frequencies. Therefore, we can say that 11 years girl
has psychological disorder.
Fig. 2.d
Fig: 2. a
Fig. 2.e
Fig: 2. b
Fig: 2. c
Fig.2.f
Fig: 2. Discrete wavelet transform
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 7- Feb 2014
The 11 years girl EEG sample is extracted from
the
chb-mit
(children’s
hospital
bostonmassachusetts institute of technology) database. The
11 years girl EEG signal is generated using
MATLAB processing toolbox as shown in Fig: 3.a.
The artifacts are removed by using stationary wavelet
transform. This wavelet transform consists of
approximation and detail signals as shown in Fig: 3.a,
Fig: 3.b. For the signal analysis, the 11 years girl
EEG signal is normalized to zero mean and unit
variance as shown in Fig: 3.c. Then the signal is
segmented into 4 sections as shown in Fig: 3.d.The
EEG input signal frequencies are shown in Fig: 3.e.
After removing the artifacts, the signal frequencies
are shown in Fig: 3.f. From the EEG features, the
EEG frequencies are Delta: 7Hz, Theta: 9Hz, Alpha:
22Hz, Beta: 48Hz, Gamma: 115Hz. By comparing
the 11 years girl EEG frequencies with normal child
EEG frequencies (Delta :< 4Hz, Theta: 4-8Hz, Alpha:
8-13Hz, Beta: 13-30 Hz, Gamma: 30-100 Hz). The
11 years girl EEG frequencies are beyond the normal
child EEG frequencies. Therefore, we can say that 11
years girl has psychological disorder.
Fig: 3.b
Fig: 3.c
Fig: 3.a
Fig: 3.d
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 7- Feb 2014
8Hz,Alpha:8-Hz,Beta:13-30 Hz,gamma:30-100 Hz).
The 11 years girl EEG frequencies are beyond the
normal child EEG frequencies. Therefore, we can say
that 11 years girl has psychological disorder.
Fig: 3.e
Fig: 4.a
Fig: 4.b
Fig: 3.f
Fig: 3. Stationary wavelet transform1
The 11 years girl EEG sample is extracted from
the chb-mit (children’s hospital boston massachusetts institute of technology) database. The
11 years girl EEG signal is generated using
MATLAB processing toolbox as shown in Fig:
4.a.The denoised signal is taken from the wave menu
in command window by using stationary wavelet
transform. For the signal analysis, the 11 years girl
EEG signal is normalized to zero mean and unit
variance as shown in Fig: 4.b, Then the signal is
segmented into 4 sections as shown in Fig: 4.c. The
EEG input signal frequencies are shown in Fig: 4.d.
After removing the artifacts, the signal frequencies
are shown in Fig: 4.e. From the EEG features, the
EEG frequencies are Delta:7 Hz, Theta:13 Hz,
Alpha:22 Hz, Beta:48 Hz, Gamma:90Hz.By
comparing the 11 years girl EEG frequencies with
normal child EEG frequencies(Delta:<4Hz,Theta:4-
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Fig: 4.c
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 7- Feb 2014
The Swt1 has the least mean square error value.
Based on comparison table the Dwt has less mean
and variance.
5. Conclusion
In this paper, we first extracted the EEG samples
from chb-mit database. After implementing the EEG
signal, the artifacts are removed by using discrete and
stationary wavelet transforms. By comparing the
MSE (mean square error), mean and variance of
wavelet transforms, The stationary wavelet
transform1 has least mean square error.
By
comparing the 11 years girl EEG frequencies with
normal child EEG frequencies, we can say the 11
years girl has psychological disorder.
Fig: 4.d
References
Fig: 4.e
1.
Qiang wang and olga sounia ”Real time
mental task
recognition from EEG
Signals”VOL 21,NO 2 .
2.
G. Geetha and Dr. S. N. Geethalakshmi, ”
Scrutinizing different techniques for artifact
removal in EEG signals”Vol. 3 No. 2 Feb
2011.
3.
Rohtash Dhiman, J.S. Saini, Priyanka, A.P
Mittal, “Artifact removal from
EEGrecordings”,March 2010
4.
The normal EEG of the waking adult.
5.
Performance comparison of Known ICA
algorithms to a wavelet-ICA merger.
6.
chb-mit database
http://www.physionet.org/physiobank/datab
ase/chbmit/
7.
http://www.mathworks.com accessed on
2008-11-21
Fig: 4 stationary wavelet transform2
Table 1 Comparison table for wavelet transforms
Wavelet
type
MSE(mean
square error)
Mean
Variance
Dwt
8.78
32.45
1
Swt1
-12.56
31.66
1
Swt2
-16.16
13.22
1
The results are shown in table1. The “MSE” (mean
square error) column determines the mean square
values for discrete and stationary wavelet transforms.
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