Sheibani R, Sadeghi Bajestani G, Goshvarpour A. Study of Interactive Variation Between Brain and Heart Signals While Listening to the Holy Quran by Fusion Technique. Caspian J Neurol Sci 2023; 9 (2) :78-91
URL:
http://cjns.gums.ac.ir/article-1-614-en.html
1- Department of Biomedical Engineering, Faculty of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran
2- Health Technology Research Center, Faculty of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran
Abstract: (742 Views)
Background: In recent years, much attention has been paid to the impact of spirituality on people’s health. Some signals can alter brain function and affects the autonomic nervous system to reduce blood pressure, heart rate, and anxiety levels.
Objectives: This study aimed to investigate the effect of listening to the Holy Quran on the electrocardiogram (ECG) and electroencephalogram (EEG) signals of healthy people with the fusion technique.
Materials & Methods: Cardiac signal recording and two brain signal channels in the C3 and C4 areas of 25 female students between 20 and 23 years old were performed in three stages: silence, listening to the Holy Quran, and silence again. We used standard complementary plots, then we matched the circles with different radii (0.1 to 1) on the complementary diagram and extracted the number of intersection points with the hypothetical lines of the complementary plot as a feature. We then examined all possible modes with the support vector machine classifier. A new data fusion technique was used to study the interactions between the heart and the brain.
Results: The best accuracy of 98.75% was obtained for a distinction between pre and no-voice using the brain signal.
Conclusion: The results of the present study show the effect of listening to the Holy Quran on physiological signals with the fusion technique.
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● The sound of the Holy Quran creates a significant change in brain and heart signals.
● The maximum accuracy of 98.75% was achieved for C3 signals in discriminating baseline and with-voice classes.
● The lowest recognition rates were perceived by utilizing electrocardiogram (ECG) signals alone.
● Generally, the results of fusion frameworks outperformed the signal-based approaches.
Type of Study:
Research |
Subject:
Special Received: 2023/03/28 | Accepted: 2023/01/11 | Published: 2023/04/23