Effect of Mobile Phone Radiation on Brain Using EEG ...

Effect of Mobile Phone Radiation on Brain Using EEG Analysis By Higuichi’s Fractal Dimension Method 1

C. K. Smitha and 2 N. K. Narayanan Department of Electronics & Instrumentation Engg College of Engineering, Vadakara, Kerala- 673105 2 Department of Information Technology Kannur University, Kerala- 670567, 1 [email protected], [email protected]

1

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 is moderately to strongly co-relate with brain state. Fractal dimension, 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 Higuichi’s fractal method is applied to find the fractal dimension. EEGs of 5 volunteers were recorded at rest and on exposure to radiofrequency (RF) emissions from mobile phones having different SAR values. Mobiles were positioned near the ears and then near the cz position. Fractal dimensions for all conditions are calculated using Higuich’s FD estimation algorithm. 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. Keywords: EEG, Mobile phone, Fractal dimension, Higuich’s Algorithm.

1. INTRODUCTION

Researches conducted during past decades shows that long-term usage of mobile phones can damage health. It is associated with brain tumors [18,19], head ache[17], decrease in sperm count and mobility[16], memory loss[20] 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. Frequent exposure of human body to electromagnetic fields is a growing concern of our present lifestyle. It raises question about the effect of electromagnetic field on human body. Most of the studies performed during recent years concluded with contradictory results. In almost all the studies conducted on 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 minute changes in mental condition will affect the signals. Moreover linear methods work properly only for stationary signals, but assumptions of stationarity required for the correct use of this linear algorithm are often ignored. Mobile phones generate a modulated radio frequency electromagnetic field (RF-EMF), which is a form of non-ionizing radiation. Typically, RF-EMF refers 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 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.

International Conference on Communication and Electronics System Design, edited by M. Salim, K. K. Sharma, V. Janyani, Proc. of SPIE Vol. 8760, 87601C © 2013 SPIE · CCC code: 0277-786/13/$18 · doi: 10.1117/12.2012177 Proc. of SPIE Vol. 8760 87601C-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 02/06/2013 Terms of Use: http://spiedl.org/terms

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 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. James C. Lin [2] suggested that pulse-modulated microwaves from cellular phones may promote sleep and modify human brain activity in his paper during 2003. Aruna et al [13] 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 [14] 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 [6] in 2003 concluded that full power mode exposure may influence human brain activity than standby mode. In 2005 J L Bardasano [15] 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 [4]. Non linear analysis of EEG is conducted by A. Accardo, M. Ffinto et al in 1997 [8]. Rosanna Esteller and colleagues compared fractal dimension algorithms [3]. 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 [5]. 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[10]. The performances of three waveform FD estimation algorithms (i.e. Katz’s, Higuchi’s and the k-nearest neighbour algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram by Polychronaki and et. Al [7]. Klonowsky discussed the importance of nonlinear methods of contemporary physics in EEG analysis[9].

2. EXPERIMENTAL METHOD The electroencephalogram (EEG) makes a 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. Data Acquisition: Seven healthy individuals participated in the study, but data of 5 subjects were used for study, namely subject-1, subject-3, subject-4, subject-5, 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. This procedure is repeated using different mobile phones with different SAR values. SAR for the phone 1 is 1.3W/Kg and for phone 2 is 0.987 W/Kg. phone 3 and phone 1 are same with different sim cards. Subject 1 is female

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and subject 4 is a male who uses phone occasionally. Subject 3 and 5 are females who use phone very rarely. Subject 7 is a male who uses phone 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) for 21 electrodes (excluding the earlobe electrodes).

Preprocessing visual inspecti

Feature Extraction

filter

Analysis

Met h od -X

on

i-

Visual Ins pecti on

Input -1: EEG normal Input -2: EEG data with phone

filter

Method -X

Method -X

F DComparison : bet

Higuichi's fractal method

1& 2

.

Fig -1 Block diagram for EEG analysis

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. Feature Extraction: The method used for feature extraction in this study is Fractal dimension method. 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. Signal complexity can be analyzed either directly in time domain, or in frequency domain, or in the phase space. Fractal dimension D calculated this way characterizes complexity of the curve representing the signal on a plane. Since the dimension of a line is equal to 1 and that of a plane is equal to 2, the greater is D, the more complex is the signal under consideration. In time domain calculation of D, Higuchi’s algorithm is much quicker and so easier. 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. where The sequence to be analyzed is represented as xN ( : 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 from 1 to , construct new times series defined as follows: where

.The variables

and

are integers indicates the initial time and the discrete


time interval between the points (delay). The function int() denotes fix() or round off the value. For example, in case of k=4 and N=1000, four time series are produced as follows:

Step 2. Calculate the length

of each curve

as follows: ------------ (1)

The term

serves as a normalization factor for the curve length

Step 3. Calculate the mean length of the curve for each , for , as = Repeat the calculation for

ranging from

as the average value over

sets of -------------(2)

to

value of

is fixed as 5.

∝

, then the trajectory is fractal with dimension D. In that case, the plot of against should fall on a straight line with slope equal to −D. Then slope of this plot will give fractal dimension of the EEG signal. By Higuichi’s algorithm the FD of a curve is estimated by means of a least squares linear best-fitting procedure.

Step 4. If

3. RESULTS Evaluation using synthetic signals: In this section, the systematic way of the parametric algorithms Higuchi’s is studied and the accuracy of the algorithm is tested by using a synthetic signal. For the generation of synthetic signal deterministic Weierstrass cosine function, WH (t) = , -----------------(3) 0 < H < 1, sampled at N equidistant points, is used. The above-defined Weierstrass’s cosine function is an example of a continuous function that is nowhere differentiable and has a known theoretical FD. The values for λ, M and N were kept as fixed as λ = 5, M = 26, N= 1000 following [3], and t [0, 1] is used. The above-defined function is Weierstrass’s example of a continuous function that is nowhere differentiable and has a known theoretical FD. More specifically, parameter H, and this parameter alone, is connected to the theoretical FD of the Weierstrass waveform by FD = 2−H. Weierstrass sequences, each having a different theoretical FD value (i.e. 1.1, 1.2, 1.3 . . . 1.9), can be generated using (3). In figure 2, two of those sequences are depicted.

∈

Weierstrauss Cosine function

Weierstrauss Cosine function

8

1.5

6

1

0.5 Amplitude ----->

Amplitude ----->

4

2

0

0

-0.5

-2

-1

-4

0

100

200

300

400 500 600 Sample no. ----->

700

800

900

1000

-1.5

0

100

200

300

(a)

400 500 600 Sample no. ----->

(b)

Figure- 2 Weierstrass cosine function a) for H= 0.1 FD =1.9 b) for H= 0.9 FD =1.1


700

800

900

1000

∝

Interpretation : According to Higuchi’s algorithm, the FD of a curve is estimated by means of a least squares linear best-fitting procedure. In step 4 of section 2.5, it is mentioned that if (Lk) k−D, then the curve is fractal with dimension D and, in that case, the plot of ln(Lk) against ln(k) should fall on a straight line with a slope equal to −D. Slope of the graph of ln(Lk) versus ln(k) will give the slope, which is equal to fractal dimension D. The sample graph obtained using Higuichi’s algorithm is shown in fig 3. EEG data recorded for a subject under rest and with radiation from a mobile phone having SAR of 1.3W/Kg is used as data set. lnLk versus lnk

lnLk versus lnk

14.5

14

14

13.5

13

lnLk----->

lnLk----->

13.5

13

12.5

12

12.5 11.5

12

11.5

11

0

0.2

0.4

0.6

0.8 1 lnk. ----->

1.2

1.4

1.6

1.8

10.5 0

0.2

0.4

0.6

0.8 1 lnk. ----->

1.2

1.4

1.6

1.8

a) b) Fig -3 Plot of lnLk versus Lnk for a subject a) at rest, Value of mean FD = 1.7 b) with radiation from a cell phone having SAR 1.3W/Kg here mean valus of FD = 2.07

When analyzing real data, EEG of the volunteers/subjects, the points (ln(k), ln(Lk)) didn’t fall on a straight line for the whole range of k values. So the mean of the slope is calculated. Phone is kept at two positions ie next to ears and next to Cz position. FD is calculated for both conditions. There are some variations in FD. This may be due to the effect of mobile phone radiation. Result obtained for each of the subjects /volunteers are shown in figure- 4. Figures a to h of Fig -4 shows plot of ln(k) versus ln(Lk) of all subjects for all conditions. The slope is calculated for each case and mean value shown in table 4. As per the proportionality fractal dimension is proportional to –D. So FD is taken as positive. Here subject1 is female who uses phone occasionally and doesn’t show much difference in FD. Subject 3 is a female who uses phone very rarely, and there is a considerable difference in FD. Subject 4 is a male who uses phone occasionally, shows difference in FD for a particular type of phone. Subject 7 is a male who uses phone continuously, doesn’t show much difference in FD.

a)

b)


c)

d)

e)

f)

g)

h)

Figure – 4 Plot of lnk versus lnLk of a) the subject- 1 when phone kept near to ears and b) subject-1 when phone kept near to cz c) the subject- 3 when phone kept near to ears and d) subject -3 when phone kept near to cz e) the subject-4 when phone kept near to ears and f) subject-4 when phone kept near to cz g)the subject - 7 when phone kept near to ears and f) subject -7 when phone kept near to cz


The difference in FD can be interpreted as follows. Fractal dimension ( 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. Table -1 shows the difference in FD.

rest phone 1 ears phone 2 ears phone 3ears

subject1 subject3 subject4 subject5 subject7 2.1 1.7 2 1.9 2.1 2.2 2.1 2.1 2 2 2 2.1 2 2.1 2 2 2 a)

rest phone 1 cz phone 2 at cz phone 3 cz

subject1 subject3 subject4 subject5 subject7 2.1 1.7 2 1.9 2.1 2.1 2.1 2.1 2 2 2 2.1 2 2 2 2.1 2 b)

Table -1 Comparison table of Mean FD calculated for all subjects at rest and with mobile phone positioned a) near to ears and b) near to Cz position The change in FD varies from person to person. Here subject1 is female who uses phone occasionally and doesn’t show much difference in FD. Subject 3 is a female who uses phone very rarely, and there is a considerable difference in FD. Subject 4 is a male who uses phone occasionally, shows difference in FD for a particular type of phone. Subject 7 is a male who uses phone continuously, doesn’t show much difference in FD.

4. CONCLUSION The FD of the signal is calculated in both conditions ie., while using the mobile phone and without using it. 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. Better results may be obtained if, kmax is selected appropriately. The FD is calculated to optimally approximate the slope of the linear part of the ln(Lk) versus ln(k) plot. The effect of radiation may vary, due to gender difference, age difference, mode of usage of phone (frequent or occasional usage), etc has to be further investigated using more number of data.

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