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ORIGINAL ARTICLE
Year : 2019  |  Volume : 9  |  Issue : 2  |  Page : 100-110

Nonlinear analysis of electroencephalogram signals while listening to the holy Quran


1 Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
3 Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
4 Department of Child and Adolescent Psychiatry, Tehran University of Medical Sciences, Tehran, Iran

Correspondence Address:
Dr. Mahsa Vaghefi
Engineering Faculty, Islamic Azad University, Shiraz Branch, Pardis Complex, Kilometer 5 Sadra Road, Shiraz
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_37_18

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Background: Electrical activity of the brain, resulting from electrochemical signaling between neurons, is recorded by electroencephalogram (EEG). The neural network has complex behavior at different levels that strongly confirms the nonlinear nature of interactions in the human brain. This study has been designed and implemented with the aim of determining the effects of religious beliefs and the effect of listening to Holy Quran on electrical activity of the brain of the Iranian Persian-speaking Muslim volunteers. Methods: The brain signals of 47 Persian-speaking Muslim volunteers while listening to the Holy Quran consciously, and while listening to the Holy Quran and the Arabic text unconsciously were used. Therefore, due to the nonlinear nature of EEG signals, these signals are studied using approximate entropy, sample entropy, Hurst exponent, and Detrended Fluctuation Analysis. Results: Statistical analysis of the results has shown that listening to the Holy Quran consciously increases approximate entropy and sample entropy, and decreases Hurst Exponent and Detrended Fluctuation Analysis compared to other cases. Conclusion: Consciously listening to the Holy Quran decreases self-similarity and correlation of brain signal and instead increases complexity and dynamicity in the brain.


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