Skip to main content

Advertisement

Springer Nature Link
Log in

Mobile Phone Radiations Effect on the Synchronization Between Heart and Brain

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper investigates the impact of radio frequency (RF) radiations from mobile phones operating in the standard frequency ranges of 2G and 3G mobile communications on the synchronization between heart and brain of the users. The coherence between Electrocardiogram (ECG) and Electroencephalogram (EEG) signals has been calculated in the real time when the subjects used mobile phones for their routine calls. The subjects are post graduate students and the investigations have been undertaken in five different modes viz. ideal mode, when no mobile phone is used; the reception (Rx) and transmission (Tx) modes in 2G and 3G communication named as 2GRx, 2GTx and 3GRx, 3GTx, respectively. For each channel on the brain, coherence has been calculated between ECG waves viz. P, QRS and T waves with the five brain waves viz; delta, theta, alpha, beta and gamma. The results obtained have been statistically analyzed using the SPSS statistical software. The findings of the presented work show that there exists a strong coherence between various heart and brain waves and the coherence strength is significantly affected under the impact of RF radiations for most of the heart and brain waves’ pairs. This study will help in developing a system for controlling desired brain waves by adjusting the corresponding heart waves under the controlled RF radiation exposure. The findings of this study may facilitate the extraction of EEG information from ECG traces for improved diagnosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are not publicly available because it is human data but are available from the corresponding author on reasonable request.

Code Availability

The MATLAB and SPSS softwares have been employed for the calculations and investigations. The code/instructions are available from the corresponding author on reasonable request.

References

  1. Rad, S. A., Jaleeli, K. A., & Ahmad, A. (2015). Influence of electromagnetic radiation produced by mobile phone on viscosity of human blood. International Journal of Science, Environment Technology, 4(6), 1469–1475.

    Google Scholar 

  2. Lu, Y., & Huang, Y. (2012). Biological effects of mobile phone radiations. Proceedings of IEEE international conference on microwave and millimeter wave technology (ICMMT), Shenzhen, China, 2, 1–4.

  3. Lin, J. C. (2010). Acquired cognitive behavior changes in children from cell phone use-health effects. IEEE Microwave Magazine, 11(4), 112–114.

    Article  Google Scholar 

  4. Repacholi, M. H. (2001). Health risks from the use of mobile phones. Toxicology Letters, 120, 323–331.

    Article  Google Scholar 

  5. Golinska, A. K. (2011). Coherence function in biomedical signal processing: A short review of applications in neurology, cardiology and gynecology. Studies in Logic, Grammar and Rhetoric, 25, 73–81.

    Google Scholar 

  6. Originlab: Coherence and Correlation, 2004. Retrieved 22 May, 2015, from https://www.originlab.com/doc/Tutorials/Coherence-and-Correlation.

  7. Jurystaa, F., Borneb, P. V. D., Migeottec, P. F., Dumontd, M., Lanquarta, J. P., Degauteb, J. P., & Linkowski, P. (2003). A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men. Clinical Neurophysiology, 114, 2146–2155.

    Article  Google Scholar 

  8. Khandoker, A. H., Karmakar, C. K., & Palaniswami, M. (2008). Analysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events. Proceedings of IEEE international conference in medicine and biology society, Vancouver, Canada, 3876–3879.

  9. Keissar, K., Davrath, L. R., & Akselrod, S. (2009). Coherence analysis between respiration and heart rate variability using continuous wavelet transform. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367, 1393–1406.

    Article  Google Scholar 

  10. Thomas, R. J., Joseph, E., & Mietus, B. S. (2011). Mapping sleep using coupled biological oscillations. Proceedings of IEEE international conference of EMBS, USA, 1479–482.

  11. Mezeiova, K., & Palus, M. (2012). Comparison of coherence and phase synchronization of the human sleep electroencephalogram. Clinical Neurophysiology, 123, 1821–1830.

    Article  Google Scholar 

  12. Orini, M., Bailon, R., Mainardi, L. T., Laguna, P., & Flandrin, P. (2012). Characterization of dynamic interactions between cardiovascular signals by time-frequency coherence. IEEE Transactions on Biomedical Engineering, 59, 663–673.

    Article  Google Scholar 

  13. Lee, J., Nam, Y., McManus, D. D., & Chon, K. H. (2013). Time-Varying coherence function for atrial fibrillation detection. IEEE Transactions on Biomedical Engineering, 60, 2783–2793.

    Article  Google Scholar 

  14. Wen, W., Liu, G., Cheng, N., Wei, J., Shangguan, P., & Huang, W. (2014). Emotion recognition based on multi-variant correlation of physiological signals. IEEE Transactions Affective Computing, 5, 126–140.

    Article  Google Scholar 

  15. Chaudhary, A., Chauhan, B., & Singh, G. (2014). Estimation of coherence among ECG and EEG signals using various auto-regressive methods. International Journal of Health Sciences and Research, 4, 294–301.

    Google Scholar 

  16. Lin, P. F., Lo, M. T., Tsao, J., Chang, Y. C., Lin, C., & Ho, Y. L. (2014). Correlations between the signal complexity of cerebral and cardiac electrical activity: A multiscale entropy analysis. PloS One, 9(2), e87798.

    Article  Google Scholar 

  17. Vargas, R. C., Dissanayaka, P. C., Patti, C. R., Schilling, C., Schred, M., & Cvetkovic, D. (2014). Linear and non-linear interdependence of EEG and HRV frequency bands in human sleep. Proceedings of IEEE international conference in medicine and biology society (EMBC), 1010–1013.

  18. Martin, A., Guerrero-Mora, G., Dorantes-Méndez, G., Alba, A., Mendez, M. O., & Chouvarda, I. (2015). Non-linear analysis of EEG and HRV signals during sleep. Proceedings of IEEE international conference in medicine and biology society (EMBC), Milan, 4174–4177.

  19. Chiu, H. C., Lin, Y. H., Lo, M. T., Tang, S. C., Wang, T. D., Lu, H. C., Ho, Y. L., Ma, H. P., & Peng, C. K. (2015). Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization. Scientific Reports, 5(1), 1–14.

    Article  Google Scholar 

  20. Gao, J., Fan, J., Wu, B. W. Y., Zhang, Z., Chang, C., Hung, Y. S., Fung, P. C. W., & Hung Sik, H. (2016). Entrainment of chaotic activities in brain and heart during MBSR mindfulness training. Neuroscience Letters, 616, 218–223.

    Article  Google Scholar 

  21. Bahari, F., Ssentongo, P., Schiff, S. J., & Gluckman, B. J. (2018). A brain–heart biomarker for epileptogenesis. Journal of Neuroscience, 38(39), 8473–8483.

    Article  Google Scholar 

  22. Pattnaik, S., Dhaliwal, B. S., & Pattnaik, S. S. (2019). Impact analysis of mobile phone electromagnetic radiations on human electroencephalogram. Sadhana, 44, 1–12. https://doi.org/10.1007/s12046-019-1116-y

    Article  Google Scholar 

  23. Sharma, R., Pachori, R. B., & Sircar, P. (2020). Automated emotion recognition based on higher order statistics and deep learning algorithm. Biomedical Signal Processing and Control, 58, 101867.

    Article  Google Scholar 

  24. Jiang, H., He, B., Guo, X., Wang, X., Guo, M., Wang, Z., Xue, T., Li, H., Xu, T., Ye, S., & Suma, D. (2020). Brain-Heart interactions underlying traditional Tibetan Buddhist meditation. Cerebral Cortex, 30(2), 439–450.

    Google Scholar 

  25. Guillet, A., & Arneodo, A. (2021). Tracking rhythms coherence from polysomnographic records: A time-frequency approach. Frontiers in Applied Mathematics and Statistics, 7, 624456. https://doi.org/10.3389/fams.2021.624456

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their thanks and regards to Dr. Pawan Kansal, senior cardiologist in Mukat Hospital Chandigarh, India for offering his valuable time and guidance during this research work.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. ECE Department, SSIET, Dera Bassi, Punjab, India

    Suman Pattnaik

  2. National Institute of Technical Teachers’ Training and Research, Chandigarh, India

    Balwinder Singh Dhaliwal & Shyam Sundar Pattnaik

Authors
  1. Suman Pattnaik
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Balwinder Singh Dhaliwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shyam Sundar Pattnaik
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed equally to the study conception and design. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Balwinder Singh Dhaliwal.

Ethics declarations

Conflict of interest

The authors report that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pattnaik, S., Dhaliwal, B.S. & Pattnaik, S.S. Mobile Phone Radiations Effect on the Synchronization Between Heart and Brain. Wireless Pers Commun 124, 3205–3234 (2022). https://doi.org/10.1007/s11277-022-09509-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09509-0

Keywords