Energy changes in brain under mobile phone radiation

Concern about the effect of electromagnetic radiation on human brain increases due to the drastic increase of mobile phone usage. EEG signal is selected to analyze the dynamic changes in brain since EEG reflects the functioning of brain. Due to its non linear characteristics, non linear methods namely wavelet transform is used for analysis. Wavelet Energy of each electrode, relative wavelet energy of all bands of EEG and average value of wavelet coefficients are used as features. The wavelet energy and wavelet coefficients represent the characteristics of the signals. The data set comprised of EEG of 35 healthy individuals at rest and with radiation from two types of mobile phones. In this paper, the features calculated for all 21 channels of the data set are analyzed statistically, using non linear Kruskal Wallis test. The test is significant for some of the electrodes for the dataset, it shows there are some changes in energy of some of the bands of EEG while using mobile phone.

[2]  Chulho Kim,et al.  Clinical features of headache associated with mobile phone use: a cross-sectional study in university students , 2011, BMC neurology.

[3]  K. H. Mild,et al.  Cellular and cordless telephones and the risk for brain tumours , 2002, European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation.

[4]  I. Cosic,et al.  Human brain wave activity during exposure to radiofrequency field emissions from mobile phones , 2010, Australasian Physics & Engineering Sciences in Medicine.

[5]  Ashok Agarwal,et al.  Effect of cell phone usage on semen analysis in men attending infertility clinic: an observational study. , 2008, Fertility and sterility.

[6]  Manoj Duhan,et al.  EFFECT OF MOBILE PHONE RADIATION ON BRAIN ACTIVITY GSM VS CDMA , 2011 .

[7]  N. K. Narayanan,et al.  Effect of Mobile Phone Radiation on Brain Using Wavelet Energy , 2014 .

[8]  Samir Avdakovic,et al.  Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier , 2010, ArXiv.

[9]  Selçuk Paker.  FDTD Evaluation of the SAR Distribution in a Human Head Near a Mobile Cellular Phone , 1998 .

[10]  C. Balanis,et al.  Higher-order finite-difference schemes for electromagnetic radiation, scattering, and penetration .2. Applications , 2002 .

[11]  N. K. Narayanan,et al.  Study of brain dynamics under mobile phone radiation using various fractal dimension methods , 2013, 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition.

[12]  A. T. Harding,et al.  Probability and statistics for engineers , 1969 .

[13]  J. Álvarez-Ude,et al.  New Device Against Non-Thermal Effects from Mobile Telephones , 2005 .

[14]  Christos N. Capsalis,et al.  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 .

[15]  Yan Li,et al.  Measuring and Reflecting Depth of Anesthesia Using Wavelet and Power Spectral Density , 2011, IEEE Transactions on Information Technology in Biomedicine.

[16]  Abdullah Al Jumah,et al.  Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques , 2013 .

[17]  K. H. Mild,et al.  Use of cellular telephones and the risk for brain tumours: A case-control study. , 1999, International journal of oncology.

[18]  N. K. Narayanan,et al.  Effect of mobile phone radiation on brain using EEG analysis by Higuichi's fractal dimension method , 2012, Other Conferences.

[19]  Hiie Hinrikus,et al.  Non-Thermal Effect of Microwave Radiation on Human Brain , 2005 .

[20]  K. P. Soman,et al.  Insight into Wavelets: From Theory to Practice , 2005 .

[21]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[22]  Nader Bagherzadeh,et al.  High performance parallel computing for FDTD numerical technique in electromagnetic calculations for SAR distribution inside human head , 2010 .