International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
Effect of Mobile Phone Radiation on EEG Using
Various Fractal Dimension Methods
1
C. K. Smitha ,2 N. K. Narayanan
1
Department of Electronics & Instrumentation Engg. College of Engineering, Vadakara, Kerala- 673105, smi_c_k@yahoo.com, 2
Department of Information Technology Kannur University, Kerala- 670567, nknarayanan@gmail.com
ABSTRACT
The electroencephalogram (EEG) is a record of the oscillations of brain electric potentials. The EEG provides a convenient
window on the mind, revealing synaptic action that strongly co-relate with brain state. Fractal dimension, the measure of
signal complexity can be used to characterize the physiological conditions of the brain. As the EEG signal is non linear, non
stationary and noisy, non linear methods will be suitable for the analysis. In this paper various methods of fractal dimension
analysis especially Higuichi’s fractal method, Katz, and k-NN algorithms were applied to find the fractal dimension of EEG.
The main attraction of fractal geometry is its ability to describe the irregular shape of natural features as well as other complex
objects that traditional Euclidean geometry fails to analyze. EEGs of 10 volunteers were recorded at rest and on exposure to
radiofrequency (RF) emissions from mobile phones having different SAR values. Mobiles were positioned near the auricle
and then near the Cz position. Fractal dimensions for all conditions are calculated using three algorithms. The FD of sample
data sets were tested using F- test. Null hypothesis is rejected in 70% , 90%and 90% respectively for Higuichi, Katz and
k_NN for data set prepared by keeping phone at Auricle position. Similarly null hypothesis is rejected in 75%, 90%and 90%
respectively for Higuichi, Katz and k_NN for data set prepared by keeping phone at auricle position. The result shows that
there are some changes in the FD while using mobile phone. The change in FD of the signal varies from person to person. The
changes in FD show the variations in EEG signal while using mobile phone, which demonstrate transformation in the
activities of brain due to radiation.
IJOART
Keywords: EEG, Mobile phone, Fractal dimension, Higuichi’s Algorithm, Katz Algorithm, k –NN algorithm, F-test.
1. INTRODUCTION
In recent years usage of mobile phone
increased drastically. Since the mobile phone comes close
to the head, concern about adverse effects of mobile
phone radiation on the nervous system increased. A large
number of investigations were conducted to study the
effects of mobile phone exposure. Some of the studies
conducted shows that long-term usage of mobile phones
can damage health. It is associated with brain tumors
[1],[2], head ache[3], decrease in sperm count and
mobility[4], memory loss[5] which leads to Alzheimer’s
and concentration problems. The brain has greater
exposure to mobile-phone radiation (MPR) than the rest of
the body, and there are experimental findings suggesting
that electromagnetic fields may modulate the activity of
neural networks. Most of the studies performed during
recent years concluded with contradictory results. In
almost all the studies conducted using EEG, the signal is
considered as linear signal and the analysis is conducted
on the basis of that. But in practice the electric signal from
brain is non predictive, non linear and fluctuating. Even
infinitesimal changes in mental condition will affect the
signals. Moreover linear methods work efficientl for
stationary signals, but assumptions of stationarity required
are ignored while using these linear algorithms.
Mobile phones generate a modulated radio
frequency electromagnetic field (RF-EMF), which is a
form of non-ionizing radiation. Typically, RF-EMF refers
Copyright © 2013 SciResPub.
to the frequency range from 100 kHz up to 300 GHz.
Mobile phone radiation is unable to cause ionizations in
atoms or molecules. However, it is unknown whether
mobile phone radiation could affect cellular and
physiological
functions
by other
mechanisms.
Electromagnetic radiation can be classified into ionizing
radiation and non-ionizing radiation, based on whether it
is capable of ionizing atoms and breaking chemical bonds.
Ultraviolet and higher frequencies, such as X-rays or
gamma rays are ionizing. Non-ionizing radiation, is
associated with two major potential hazards electrical
and biological. Additionally, induced electric current
caused by radiation can generate sparks and create a fire
or explosive hazard. Electromagnetic fields induce an
electric field and a current in the body. A strong electric
field, depending on its frequency, might warm up tissues
or disturb the neuronal functions. Thermal effects are
based on energy absorption from the field to the tissue,
which causes the oscillation of molecules. The radio
waves emitted by a GSM handset can have a peak power
of 2 watts, and CDMA use lower output power, typically
below 1 watt. Mobile phone systems continuously adapt
the transmission power output level, and the maximum
transmission power is only used when the field is weak.
The rate at which radiation is absorbed by the
human body is measured by the Specific Absorption Rate
(SAR). The maximum power output from a mobile phone
is regulated by the mobile phone standard and by the
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
regulatory agencies in each country. The Federal
Communications Commission (FCC) has fixed SAR limit
of 1.6 W/kg, averaged over a volume of 1 gram of tissue,
for the head. The potential health hazards may occur at
high radiation power levels when SAR >4 W/kg.
The previous work by same authors is accepted
in the conference, ICCESD 2013 at Jaipur and ICSIPR
2013 at Karunya University. In the 1st paper, signal
complexity is measured using Higuichi’s method by
considering EEG signals from all electrodes as a whole.
And in the second paper, three methods were used to find
FD. There is considerable difference in fractal dimension
of EEG, in case of some individuals.
James C. Lin [6] suggested that pulse-modulated
microwaves from cellular phones may promote sleep and
modify human brain activity in his paper during 2003.
Aruna et al [7] in a study in 2011 using EEG analysis,
concluded that GSM mobile phone has larger effect on
brain compared to CDMA phones. Andrew et al [8]
conducted study on rabbits in 2003 and concluded that
fields from standard phone can alter brain function as a
consequence of absorption of energy by the brain. In
another research by H.D Costa et al [9] in 2003 concluded
that full power mode exposure may influence human brain
activity than standby mode. In 2005 J L Bardasano [10]
and colleagues concluded a study by stating that use of a
protective device can reduce the effect of mobile phone
radiation. A study on “Influence of a 900MHz signal with
gender on EEG by Eleni Nanou [11] and colleagues
concluded that “without radiation the spectral power of
males is greater than of females, while under exposure the
situation is reversed”. In another study by Hie Hinrikus et
al [12], stated that microwave stimulation causes increase
of the EEG energy level – the effect is most intense at
beta1 rhythm and higher modulation frequencies using
statistical methods.
The method of fractal dimension is published by
T. Higuichi in 1988 [13]. Non linear analysis of EEG is
conducted by A. Accardo, M. Ffinto et al in 1997 [14].
Rosanna Esteller and colleagues compared fractal
dimension algorithms [15]. By using Higuichi’s method
Klonowsky, made quick and easy assessment of
individual susceptibility to EMF used in mobile
communication as well as for testing of different cellular
phones models for their certification [16]. W.
Klonowski.et.al used Higuchi’s fractal method for sleep
study, the different sleep stages were characterized and
reconstructs a hypnogram based on the whole-night sleep
EEG-signal [17]. The performances of three waveform FD
estimation algorithms (i.e. Katz’s, Higuchi’s and the knearest neighbour algorithm) were compared in terms of
their ability to detect the onset of epileptic seizures in
scalp electroencephalogram by Polychronaki and et. Al
[18]. Klonowsky discussed the importance of nonlinear
methods of contemporary physics in EEG analysis [19]. In
1999 Asvestas et.al introduced a method from the field of
chaotic dynamics, the kth nearest neighbour, for the
estimation of the fractal dimension[20]
In section 2, methods of data acquisition,
preprocessing and in section 3 an outline of different
methods of FD estimation algorithms utilized is provided.
In section 4, the evaluation of the FD algorithms using
synthetic signals of known FD followed by the different
results obtained by using different algorithms. The
interpretation of the result is included in section 4 and
conclusion and scope of further work is discussed in
section 5
2. MATERIALS AND METHODS
The electroencephalogram (EEG) makes scalp
recording of electrical activity, or brain waves, emitted by
nerve cells from the cortex of the brain. This activity
appears on the screen of the EEG machine as waveforms
of varying frequency and amplitude measured in voltage
(specifically micro voltages). An EEG signal is a
measurement of currents that flow during synaptic
excitations of the dendrites of many pyramidal neurons in
the cerebral cortex. When brain cells (neurons) are
activated, the synaptic currents are produced within the
dendrites. This current generates a magnetic field
measurable by electromyogram (EMG) machines and a
secondary electrical field over the scalp measurable by
EEG systems. The current in the brain is generated mostly
by pumping the positive ions of sodium, Na+, potassium,
K+, calcium, Ca++, and the negative ion of chlorine, Cl−,
through the neuron membranes in the direction governed
by the membrane potential. The activities in the CNS are
mainly related to the synaptic currents transferred between
the junctions (called synapses) of axons and dendrites, or
dendrites and dendrites of cells. A potential of 60–70 mV
with negative polarity may be recorded under the
membrane of the cell body. This potential changes with
variations in synaptic activities. EEG waveforms are
generally classified according to their frequency,
amplitude, and shape, as well as the sites on the scalp at
which they are recorded. The most familiar classification
uses EEG waveform frequency (eg, Alpha 8-13 Hz, Beta
> 13 Hz, Theta 3.5-7.5 Hz & Delta 3 Hz or less).
Information about waveform frequency and shape is
combined with the age of the patient, state of alertness or
sleep, and location on the scalp to determine significance.
The block diagram in figure -1 depicts the experimental
method used in this study. The steps involved are data
collection, preprocessing, feature extraction and analysis.
IJOART
Copyright © 2013 SciResPub.
Fig -1 Block diagram for EEG analysis
2.1 Data Acquisition:
Seven healthy individuals in different age groups
were participated in the study, namely subject-1 to
subject-7. EEGs were recorded from EEG Lab under
Neurology department of MIMS Hospital, Calicut using
Galelio N.T machine and Galelio NT EEG Viewer
software (version 2.44) by ebneuro.
EEG of the
volunteers was recorded by keeping mobile phones at two
different positions of head for 5 minutes each, ie near to
auricle and at Cz position. This procedure is repeated
using two different mobile phones with different SAR
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
values. SAR for the phone 1 is 1.3W/Kg and for phone 2
is 0.987 W/Kg. Details of the subjects participated for this
study is as per table 1
Table 1 : Details of the subjects studied
Sl
No
1
2
3
4
5
6
7
7
7
7
Name
Subject 1
Subject 2
Subject 3
Subject 4
Subject 5
Subject 6
Subject 7
Subject 8
Subject 9
Subject
10
Sex
Fe-male
Male
Fe-male
Male
Fe-male
Male
Male
Male
Male
Male
Age
group
35-45
35-45
55-65
65-75
55-65
65-75
45-55
55-65
35-45
35-45
Mode of phone
usage
Occasionaly
Continuously
Rarely
Occasionaly
Rarely
Occasionaly
Continuously
Moderately
Moderately
Continuously
In EEG recording, electrodes and their proper
function are crucial for acquiring high quality data.
Commonly used scalp electrodes consist of Ag–AgCl
disks, less than 3 mm in diameter, with long flexible leads
that can be plugged into an amplifier. The International
Federation of Societies for Electroencephalography and
Clinical Neurophysiology has recommended the
conventional electrode setting also called 10–20 system
for EEG recording, which is shown in fig 2. In this study,
21 electrodes of 10-20 system excluding the earlobe
electrodes were used for EEG recording
A fractal is a set of points that when looked at smaller
scales, resembles the whole set. A fractal dimension is a
ratio providing a statistical index of complexity
comparing, how detail in a pattern changes with the scale
at which it is measured. Roughly, the fractal dimension of
a set can be defined when the following limit exists as a
𝑁(𝑟) 𝐹𝐷
finite number, lim𝑟→0 � � where FD is the fractal
𝑟
dimension and N(r) is the number of balls of radius r
necessary to cover the set .Signal complexity can be
analyzed either directly in time domain, or in frequency
domain, or in the phase space.
2.3.1 Higuichi’s Algorithm:
Higuchi’s algorithm is based on curve length
measurement. The algorithm estimates the mean length of
the curve, by using a segment of k samples as a unit of
measure. Higuchi’s FD estimation technique consists of
the following steps.
Step 1. Let us define the values of a finite set of time .
series observations, which are taken in a regular interval.
The sequence to be analyzed is represented as
xN= x(1), x(2), x(3), … . . x(i), … . x(N)
where i= 1, 2, … . . N (N : number of points in
the time series). In our case, x would be the
successive
EEG amplitude values. For a range of 𝑘 values ranging
k
defined
from 1 to k max , construct k new times series xm
as follows:
k
xm
∶ {x(m), x(m + k), x(m + 2 ∗ k), … …,
N−m
x(m + ik), … … x(m + int �
∗ k�}
k
where m = 1,2, … … . , k. The variables m and k
are integers indicates the initial time and the discrete time
interval between the points (delay).
IJOART
k
Step 2. Calculate the length Lm (k) of each curve xm
follows:
N−m
)
k
int(
Lm (k) = ��∑i=1
|x(m + i ∗ k) −
x(m + (i − 1) ∗ k)|�
Fig 2)10 -20 system of electrode positioning
2.2 Preprocessing:
Unwanted signals or artefacts (noises) can be
removed by visual inspection and by filtering. Normally
the EEG signals contain neuronal information below 100
Hz and in many applications the information lies below 30
Hz. Any frequency component above these frequencies
can be simply removed by using low pass filters. Here all
the frequencies above 70 Hz are filtered using a low pass
filter. The EEG data acquisition system is unable to cancel
out the 50 Hz line frequency due to a fault in grounding or
imperfect balancing of the inputs to the differential
amplifiers associated with the EEG system, a notch filter
is used to remove it.
2.3 Feature Extraction:
The method used for feature extraction in this study is
Fractal dimension method, by using Higuichi’s algorithm,
Katz algorithm and k-Nearest Neighbour hood algorithm.
Copyright © 2013 SciResPub.
The term �
as
N−1
N−m
�∗k
k
int�
N−1
N−m
int�
�∗k
k
� ∗ k −1
� k −1
---(1)
serves as a
k
normalization factor for the curve length xm
Step 3. Calculate the mean length of the curve for each k,
⟨Lk ⟩ is the average value over k sets of Lm (k), for
1
m = 1,2, … , k , as ⟨Lk ⟩ = ∑km=1 Lm (k) ------(2)
𝑘
Repeat the calculation for k ranging from 1 to
k max . value of k max is fixed as 5.
Step 4. If ⟨Lk ⟩ ∝ k −D, then the trajectory is fractal with
dimension D. In that case, the plot of ln ⟨Lk ⟩ against
ln(k) should fall on a straight line with slope equal to −D.
Then slope of this plot will give fractal dimension of the
EEG signal.
2.3.2 Katz’s Algorithm
According to Mandelbroot, the FD of a planar curve is
log(L)
given by FD = log
------------(3)
(d)
Where L is the total length of the curve or sum of
distances between successive points, and d is the diameter
. For waveforms, the total length L is the sum of the
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
distances between successive points. According to Katz,
average value of F D = log( L/a)
--------------(4)
d
log ( )
a
Defining n as the number of steps in the curve, then
n = L/a and FD can be written as
n)
FD = log(
-------------(5)
d
log +log (n)
L
This expression (5) summarizes Katz’s approach to
calculate the FD of a waveform.
2.3.3 k-nearest neighbour algorithm:
FD estimation is based on the measurement of length of
the waveform sizes of cubes which are scaled
appropriately as to contain the same number of
points(fixed mass). The average distance, ⟨𝑟𝑘𝛾 ⟩ of a point
from its kth nearest neighbour can be expressed as of k
as ⟨𝑟𝑘𝛾 ⟩ ~k1/FD
----------(6)
𝛾
𝛾/𝐷(𝛾)
𝑘
⟨𝑟𝑘 ⟩ = 𝐺(𝑘, 𝛾)( �𝑛 ).
------------(7)
where 𝛾 = (1 − 𝑞)𝐷𝑞 , 𝐷𝑞 is the multifractal dimension of
order q, N is the number of points and 𝐺(𝑘, 𝛾) is function
of k and γ , which is near unity for large k.
Step 1. An initial value of γ , i.e. γ0, is chosen arbitrarily
and G(k, γ) is set to unity.. Since the FD of
waveforms
lies theoretically between 1 and 2 it would be better to
choose γ0 in this range, i.e. γ 0 = 1.5.
Hundred samples are selected randomly from
each data set for analysis. Various methods of fractal
dimension analysis namely, Higuichi’s fractal method,
Katz, and k-NN algorithms were applied to find the fractal
dimension of EEG. Due to chaotic characteristics,
behavior of EEG signals become unpredictable for
relatively long periods. The length of samples were taken
as 128 points, equivalent to sampling rate, to get almost
constant characteristics. The FD are charted and analysed
using F test.
3.1 Using Higuichi’s Algorithm
In Higuchi’s algorithm, as per step 4 of section
2.3.1, it is mentioned that if (Lk) ∝ k−D, then the curve is
fractal with dimension D and, slope of the graph of ln(Lk)
versus ln(k) will give FD. EEG of the volunteers/
subjects, FD is calculated for both conditions, ie while
phone kept near to auricle position and Cz position. a to d
of Fig 3 shows plot of ln(k) versus ln(Lk) of subjects 1
and 3 for all conditions.
From the fig 3.it is evident that plot of lnk Vs
lnLk curve are different for different cases.The difference
is very prominent case of some individuals. Here k is
selected as 5
Step 2. For every point ���⃗
pı = (xi, , yi), i = 1, 2, . . , N, we
calculate the Euclidian distances rki from its k- nearest
neighbours, k = kmin, . . . , kmax.
IJOART
Step 3. For j = 1, 2, . . . , the following recursive relations
γ
are applied: D �γj � = sj−1 , γj = D(γj )
-- ----- (8)
j−1
where sj−1 is the slope of the best-fitting line at
γ
the points( ln(k/N), ln⟨rkj−1 ⟩) least-squares sense and
𝛾
𝛾𝑗−1
1
⟨𝑟𝑘 𝑗−1 ⟩ = ∑𝑁
The calculation of (8) is repeated
𝑖=1 𝑟𝑘
𝑁
until the quantity
𝐷�𝛾𝑗�−(𝛾𝑗−1 )
1
[ 𝐷�𝛾𝑗�+�𝛾𝑗−1 �]
2
drops below a certain
(a)
value or a maximum number of iterations is reached. FD
is calculated as D(γ j ) for the last j before the above
criterion is met.
2.4 Methods for Comparison : Statistical Analysis
If same measurement method was used, the
two samples come from a population have same
variance. Hypothesis testing is use for depicting an
inferences about a population, based on statistical
evidence, Here, F-test is used for statistical analysis.
2.4.1 Two sided F-test : Two sided F-test is
used to know if population standard deviation of one set
of data (s1 ) is different from that of another set(s2 ).The
null and alternative hypotheses for the two-sided F-test are
null hypothesis : Ho = σ12 = σ12
alternative hypothesis: Ha = σ12 = σ12
In two sided F test, the ratio called Fcalc is calculated as
s2
Fcalc = num
. The traditional logic is
s2
( b)
Fig 3 (a) lnLk s lnk curve for subject 1 at Cz position (b) lnLk s lnk
curve for subject 1 at Auricle position
den
(a) If Fcalc > Fcrit , then reject the null hypothesis and
accept the alternative hypothesis;
(b) or (b) If Fcalc ≤ Fcrit, then do not reject the null
hypothesis.
3.RESULTS
Copyright © 2013 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
(c)
(b)
IJOART
(c)
(d)
Fig 3 c) lnLk s lnk curve for subject 3 at Cz posirtion. (d) lnLk Vs
lnk curve for subject 3 by keeping phone at Auricle position
3.2. Using Katz Algorithm :
The same data set is used as input to the Katz algorithm
and FD is calculated for all conditions. a to h of Fig 4
shows the plot of D versus time using Katz algorithm for
subject 1 and subject 3. There are evident differences in
the plot as shown in case of some subjects fig 4.
The difference in FD using Katz method is very
prominent case of some individuals From the fig 4.
(d)
Fig 4 ( b) Plot of FD versus Time by Katz algorithm for subject 1 by
keeping phone at auricle position, ( c) Plot of FD versus Time by
Katz algorithm for subject 3 by keeping phone at Cz position ( d)
Plot of FD versus Time by Katz algorithm for subject 3 by keeping
phone at Auriclez position
(a)
Fig 4 (a) Plot of FD versus Time by Katz algorithm for subject 1 by
keeping phone at Cz position
Copyright © 2013 SciResPub.
3.3 Using k-NN Algorithm :
k-NN algorithm is used for the calculation of FD
for all conditions and the graph is plotted from which FD
can be calculated by curve fitting. Fig 5 shows the plot
for subject 2 and subject 5 at rest and with radiation from
two cell phones.
From the Fig 5, shows the difference in FD
calculated using k –NN algorithm. The difference in FD is
very prominent case of Subject-3.
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
(a)
3.5 Statistical Analysis
Data set of individuals at different conditions
were analyzed using F-test. The table 2a and 2b shows
the result of hypothesis testing while using a F-test for
different conditions namely keeping the phone at Auricle
position and keeping the phone at Cz position. Two sided
F-test is used to test if population standard deviation of
one set of data is different from that of another set.
The null hypotheses is kept as : Ho = σ12 = σ22 .
The result of F-test shows that, the null hypothesis is
rejected in 70% , 90%and 90% respectively for Higuichi,
Katz and k_NN for data set prepared by keeping phone at
Auricle position (Table 2a). The null hypothesis is
rejected in 75%, 90%and 90% respectively for Higuichi,
Katz and k_NN (Table 2b) for data set prepared by
keeping phone at Cz position . It is evident that there are
some effects in the brain due to mobile phone radiation,
especially keeping at Auricle position.
Table 2.a) : Result of Hypothesis testing using F-test at Cz position
Number of
Subjects
Phone
Result of Hypothesis testing
Higuichi
katz
kNN
1
Reject
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Reject
1
Not Reject
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Reject
1
Not Reject
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Not Reject
1
Reject
Reject
Reject
2
Not Reject
Not Reject
Not Reject
1
Not Reject
Reject
Reject
2
Not Reject
Not Reject
Reject
1
Not Reject
Reject
Reject
2
Reject
Reject
Reject
IJOART
Subj-1
(b)
Subj-2
Subj-3
Subj-4
Subj-5
Subj-6
(c)
Subj-7
Subj-8
Subj-9
Subj-10
(d)
Fig 5 Fig 5 (a) Plot of FD versus Time byk NN algorithm for subject
2 by keeping phone at Cz position (b) Plot of FD versus Time by
using k NN algorithm for subject 2 by keeping phone at Auricle
position (c) Plot of FD versus Time by using k NN algorithm for
subject 5 by keeping phone at Cz position (d) Plot of FD versus
Time by using k NN algorithm for subject 5 by keeping phone at
Auricle position
Copyright © 2013 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue5, May-2013
ISSN 2278-7763
Table 2.b) : Result of Hypothesis testing using F-test at Auricle
position
Number of
Subjects
Subj-1
Subj-2
Subj-3
Subj-4
Subj-5
Subj-6
Subj-7
Subj-8
Subj-9
Subj-10
Result of Hypothesis testing
Phone Higuichi
1
Reject
katz
kNN
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Not Reject Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Not Reject Not Reject Reject
1
Reject
2
Not Reject Reject
Reject
1
Not Reject Reject
Not Reject
2
Reject
Reject
Reject
1
Reject
Reject
Not Reject
2
Reject
Reject
Reject
1
Not Reject Reject
Reject
2
Reject
Reject
Reject
1
Reject
Reject
Reject
2
Reject
Reject
Reject
Not Reject Reject
The result obtained through F test shows 70%
,90% and 90% rejection of the hypothesis, while
analysing the data obtained by keeping phone in
Auricle position and 75%,90% and 90% rejection
while analysing the data obtained by keeping phone in
Cz position for Higuichi,katz and k-NN methods
respectively.
REFERENCES
[1] Hardell L, Nasman A, Pahison A et al , ” Use of cellular
telephones and the risk for brain tumours: a case-control study.
1999,Int J Oncol 15:113–116
[2]Hardell L, Hallquist A, Mild H et al , “ Cellular and cordless
telephones and the risk for brain tumors, 2002, Fur J Cancer Prev
11:377–386
[3] Min Kyung Chu , Hoon Geun Song, Chulho Kim and Byung
Chul Lee, “ Clinical features of headache associated with mobile
phone use: a cross-sectional study in university students”, BMC
Neurology 2011
[4] Ashok Agarwal, Fnu Deepinder, Rakesh K.Sharma, Geetha
Ranga, and Jianbo Li, “Effect of cell phone usage on semen analysis
in men attending infertility clinic: an observational study”, 2008
American Society for Reproductive Medicine,
Published by
Elsevier Inc. 124-128
[5] IARC/A/WHO (2011) Classifies radiofrequency electromagnetic
fields as possibly carcinogenic to humans. Press release.
[6] James C Lin, “Cellular Telephone Radiation and EEG of Human
Brain’,IEEE Antennas and Propagation Magazine. Vol 45, No -5
October 2003.
[7]ArunaTyagi1,Manoj Duhan and Dinesh Bhatia,“Effect of Mobile
phone radiation on brain activity GSM Vs CDMA” IJSTM Vol. 2,
Issue 2, April 2011
[8]Andrew A.Marino, Erik Nilsen,Clifton Frilot,”Nonlinear chandes
in
Brain
Electrical
Activity
due
to
Cell
Phone
Radiation”,Bioelectromagnetics 24:339-346(2003).
[9] H. D’Costa, G. Trueman, L. Tang, U. Abdel-rahman, W. Abdelrahman, K. Ong and I. Cosic” Human brain wave activity during
exposure to radiofrequency field emissions from mobile phones”,
Australasian Physical & Engineering Sciences in Medicine Volume
26 Number 4, 2003
[10]J. L. Bardasano, J Alvarez- Ude, I Guiterrez , R. Goya,”New
Devices Against Non- thermal Effects from Mobile Telephones”,
2005 Springer Science, The Environmentalist, 25, 257-263, 2005.
[11]Eleni Nanou, Vassilis Tsiafakis, E. KApareliotis, “Influence of
the Interaction of a 900 MHz Signal with Gender On EEG Energy:
Experimental Study on the Influence of 900 MHz Radiation on EEG”
2005 Springer Science, The Environmentalist, 25, 173–179, 2005.
[12]Hie Hinkirikus, Maie Bachmann, Ruth Tomson and Jaanus Lass
,”Non-Thermal Effect of Microwave Radiation on Human Brain”
,2005 Springer Science, The Environmentalist, 25, 187–194, 2005
[13] T. Higuchi, “Approach to an irregular time series on the basis
of the fractal theory,” Physica D, vol. 31, pp. 277- 283, 1988.
[14] A. Accardo, M. Affinito, M. Carrozzi, and F. Bouquet, “Use of
the fractal dimension for the analysis of electroencephalographic
time series,” Biol.Cybern., vol. 77, pp. 339–350, 1997.
[15] Rosanna Esteller et al , “A comparison of waveform Fractal
dimension algorithms,”IEEE tran on Circuits and systems, Vol.
48,2001, pp177-183.
[16] W. Klonoski, “Non linear EEG signal analysis reveals
hypersensitivity to electro magnetic fields generated bycellular
phones,” IFMBE Proceedings Vol.14/2,2007, pp1056-1058.
[17]W. Klonoski , E. Olejarczyk and R. Stepien, “Sleep EEG
analysis using Higuichi’s fractal Dimension,”
International symposium on Nonlinear theory and its applications,
Belgium, 2005, 222-225 3:2
[18] C E Polychronaki et al,”Comparison of fractal dimension
estimation algorithms for epileptic seizure onset detection,” Journal
of Neural Eng, 7 (2010) 046007 (18pp).
[19] Wlodzimierz Klonoski, “Everything you wanted to ask about
EEG but where afraid to get the right answer,” Nonlinear
Biomedical physics 2009.
[20] Asvestas P, Matsopoulos G K and Nikita K S 1999 Estimation
of fractal dimension of images using a fixed mass approach Pattern
Recognit. Lett. 20 347–54
IJOART
4. CONCLUSION
The FD of the signal is calculated in both
conditions ie., while using the mobile phone and without
using mobile phone using Higuichi’s Katz and k-NN
algorithmfor 10 subjects. The calculated values of FD
were compared, statistically using F-test. The differences
in fractal dimension are evident in case of some
individuals.
The difference in FD can be interpreted as
follows: FD shows complexity of the signal, as
complexity decreases, signal become linear or the effect
which makes decrease in FD is strong enough to linearize
the action of brain. Similarly as FD increases the signal
become more complex or the effect is able to stimulate the
brain. This may be due to the effect of mobile phone
radiation. Due to chaotic characteristics, behavior of EEG
signals become unpredictable for relatively long periods.
The length of samples was taken as 128 points, equivalent
to sampling rate, to get almost constant characteristics.
This is very advantageous because EEG-signal remains
stationary during short intervals.
The changes in FD show the variations in EEG
signal while using mobile phone, demonstrate
transformation in the activities of brain due to radiation. it
must be further investigated and the tolerance limit of the
complexity level has to be determined. The effect of
radiation may vary; due to gender difference, age
difference, and mode of usage of phone (frequent or
occasional usage) etc has to be further investigated.
Copyright © 2013 SciResPub.
IJOART