Chinese-chi and Kundalini yoga Meditations Effects on the Autonomic Nervous System: Comparative Study

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DOI:

https://doi.org/10.9781/ijimai.2016.3713

Keywords:

3D Printing, Test, Frequency, Student-T

Abstract

Cardiac disease is one of the major causes for death all over the world. Heart rate variability (HRV) is a significant parameter that used in assessing Autonomous Nervous System (ANS) activity. Generally, the 2D Poincare′ plot and 3D Poincaré plot of the HRV signals reflect the effect of different external stimuli on the ANS. Meditation is one of such external stimulus, which has different techniques with different types of effects on the ANS. Chinese Chi-meditation and Kundalini yoga are two different effective meditation techniques. The current work is interested with the analysis of the HRV signals under the effect of these two based on meditation techniques. The 2D and 3D Poincare′ plots are generally plotted by fitting respectively an ellipse/ellipsoid to the dense region of the constructed Poincare′ plot of HRV signals. However, the 2D and 3D Poincaré plots sometimes fail to describe the proper behaviour of the system. Thus in this study, a three-dimensional frequency-delay plot is proposed to properly distinguish these two famous meditation techniques by analyzing their effects on ANS. This proposed 3D frequency-delay plot is applied on HRV signals of eight persons practicing same Chi-meditation and four other persons practising same Kundalini yoga. To substantiate the result for larger sample of data, statistical Student t-test is applied, which shows a satisfactory result in this context. The experimental results established that the Chi-meditation has large impact on the HRVcompared to the Kundalini yoga.

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References

[1] R.A. Baer, “Mindfulness training as a clinical intervention: A conceptual and empirical review,” Clin. Psychol. Sci. Pract., vol. 10, pp. 125-143, 2003.

[2] M. B. Ospina, T. K. Bond, M. Karkhaneh, L. Tjosvold, B. Vandermeer, Y. Liang, L. Bialy, N. Hooton, N. Buscemi, D.M. Dryden, T. P. Klassen, “Meditation practices for health: state of the research,” Evid. Rep. Technol. Assess (Full Rep), vol. 115, pp. 1-263, 2007.

[3] H.C. Lou, T.W. Kjaer, L. Friberg, G. Wildschiodtz, S. A Holm, “15O-H2OPET study of meditation and the resting state of normal consciousness,” Hum. Brain Mapp., vol. 7, no.2, pp. 98-105, 1999.

[4] A. Newberg, A. Alavi, M. Baime, M. Pourdehnad, J Santanna, E. D. Aquili, “The measurement of regional cerebral blood flow during the complex cognitive task of meditation: A preliminary SPECT study,” Psychiatr. Res., vol. 106, no. 2, pp. 113-122, 2001.

[5] M. M. S. Ahuja, M. G. Karmarkar, S. Reddy, TSH, LH, “Cortisol response to TRH and LH-RH and insulin hypoglycaemia in subjects practising transcendental meditation,” Ind. J. Med. Res., 74, pp. 715, 1981.

[6] T. K. Akers, D. M. Tucker, R. S. Roth, J. S. VIDILOFF, “Personality correlates of EEG change during meditation,” Psychological Reports, vol. 40, 1977.

[7] Y. Akishige, “Psychological studies on Zen,” Bull. Fac. Lit. Kyushu Univ. (Japan), 5, 1968.

[8] I. B. Albert, B. McNeece, “The reported sleep characteristics of meditators and non-meditators,” Bull. Psychon. Soc., vol. 3, 1974.

[9] C. Y. Liu, C. C. Wei, P. C. Lo, “Variation analysis of the sphygmogram to assess the cardiovascular system under meditation,” Evid. Based Complement Alternat. Med., vol. 6, 2009.

[10] P. Lehrer, Y. Sasaki, Y. Saito, “Zazen and cardiac variability,” Psychosom. Med. Vol. 61,1999.

[11] C. K. Peng, I. C. Henry, J. E. Mietus, J. M. Hausdorff, G. Khalsa, H. Benson, A. L. Goldberger, “Heart rate dynamics during three forms of meditations,” Int. J. Cardiol., vol. 95, 2004.

[12] C. K. Peng, J. E. Mietus, Y. Lie, G. Khalsa, P. S. Douglas, H. Benson, A. L. Goldberger, “Exaggerated heart rate oscillations during two meditation techniques,” Int. J. Cardiol., vol. 70, 1999.

[13] D.P. Goswami, D.N. Tibarewala, D.K. Bhattacharya, “Analysis of heart rate variability signal in meditation using second-order difference plot,” J. Appl. Phys., vol. 109, 2011.

[14] Y.H. Shiau, “Detecting Well-Harmonized Homeostasis in Heart Rate Fluctuations,” BMEI, vol. 2, pp.399-403, 2008.

[15] A. Dey, S. Mukherjee, S. K. Palit, D. K. Bhattacharya, D. N. Tibarewala, “A new technique for the classification of pre-meditative and meditative states,” Proc. of the International Conf. ICCIA, Kolkata, India, pp. 26 – 28, 2010.

[16] M. Toichi, T. Sugiura, T. Murai, A. Sengoku, “A new method of assessing cardiac autonomic function and its comparison with spectral analysis and coefficient of variation of r-r interval,” J. Auton. Nerv. Syst., vol. 62, pp. 79-82, 1997.

[17] M. P. Tulppo, T. H. Makikallio, T. E. S. Takala, T. Seppanen, H. V. Huikuri , “Quantitative beat-to-beat analysis of heart rate dynamics during exercise,” Am. J. Physiol., vol. 271, pp. 244–252, 1996.

[18] M. P. Tulppo, T. H. Makikallio, T. Seppanen, J. K. E. Airaksinen, H. V. Huikuri, “Heart rate dynamics during accentuated sympathovagal interaction,” Am. J. Physiol., vol. 247, pp.810–816, 1998.

[19] J. Hayano, H. Takahash, T. Toriyama, S. Mukai, A. Okada, S. Sakata, A, Yamada, N. Ohte, H. Kawahara, “Prognostic value of heart rate variability during long term follow-up in chronic haemodialysis patients with endstage renal disease,” Nephrol. Dial. Transplant., vol.14, pp. 1480–1488, 1999.

[20] Heart Rate Variability: An Indicator of Autonomic Function and Physiological Coherence, Institute of Heart Math, 2003. Available http://www.heartmath.org/research/ science-of-the-heart/soh_13.html.

[21] B. Irfan, A. E. Metin, K. Dayimi, T. Muhsin, K. Osman, M. Mehmet, B. E.Ozlem, B. Yelda, “Cigarette smoking and heart rate variability: dynamic influence of parasympathetic and sympathetic maneuvers,” Ann. Noninv. Electrocardiol., vol. 10, pp. 324-329, 2005.

[22] U. R. Acharya, K. P. Joseph, N. Kannathal, L. C. Min, J. S. Suri, “Advances in Cardiac Signal Processing: Heart Rate Variability,” Springer, New York, pp. 121–165, 2007.

[23] C. K. Peng, J. E. Mietus, Y. Liu, G. Khalsa, P. S. Douglas, H. Benson, A. L. Goldberger, “Exaggerated heart rate oscillations during two meditation techniques,” International journal of cardiology, vol. 70, no. 2, pp. 101- 107, 1999.

[24] G. Kheder, A. Kachouri, R. Taleb, M. Ben Messaoud, M. Samet, “Feature extraction by wavelet transforms to analyze the heart rate variability during two meditation techniques,” Advances in Numerical Methods, pp. 379-387, Springer US, 2009.

[25] G. Kheder, A. Kachouri, M. B. Massoued, M. Samet, “Heart rate variability analysis using threshold of wavelet package coefficients,” International Journal on Computer Science and Engineering, Vol. 1, no. 3, pp. 131- 136, 2009.

[26] S. Mukherjee, S. K. Palit, “A New Scientific Study towards Distinction of ECG Signals of a Normal healthy person and of a Congestive Heart Failure Patient,” J. Inter. Acad. Phys. Sci,. vol.15, no. 4, pp. 413-433, 2011.

[27] J. Piskorski, “Filtering Poincaré plots,” Comp. Methods in Sci. & Tech., vol. 11, 2005.

[28] M. Brennan, M. Palaniswami, P. Kamen, “Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability?,” IEEE Trans. Biomed. Eng., vol 48, 2001.

[29] A. Voss, S. Schulz, R. Schroeder, M. Baumert, P. Caminal, “Methods derived from nonlinear dynamics for analysing heart rate variability,” Phil. Trans. R Soc., pp. 277-296, 2009.

[30] C. Lerma, O. Infante, H. P. Grovas, M. V. Jose´,” Poincare´ plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients,” Clin. Physiol. & Func. Im., vol.23, pp. 72–80, 2003.

[31] M. Brennan, M. Palaniswami, P. Kamen, “Poincare´ plot interpretation using a physiological model of HRV based on a network of oscillators,” Am. J. Physiol. Heart Circ. Physiol., vol. 283, pp. H1873–H1886, 2002.

[32] J. Haaksma, J. Brouwer, W.A. Dijk, W.R.M. Dassen, D.J.V. Veldhuisen, “The dimension of 2D and 3D Poincaré plots obtained from 24 hours ECG registrations,” IEEE Comp. in Cardiol., vol. 29, pp. 453−456,2002.

[33] A. S. Khaled, I. O. Mohamed, A. S. A. Mohamed, “Employing TimeDomain Methods and Poincaré Plot of Heart Rate Variability Signals to Detect Congestive Heart Failure,” BIME J., vol. 6, no.1, 2006.

[34] S. Mensing, J. Limberis, G. Gintant, A. Safer, “A Novel Method for Poincaré Plot Shape Quantification Demonstrates Cardiac Tissue Repolarization Inhomogeneities Induced by Drugs,” IEEE Comp. in Cardiol., vol. 35, 2008.

[35] M. Weeks, “Digital Signal Processing,” Infinity Science Press LLC, Massachusetts, 2007.

[36] J. H. Zar, “Biostatistical Analysis,” Pearson Education, 2006.

[37] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley, “Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals,” Circul., vol. 101, no.23, 2000.

[38] G. P. Williams, “Chaos Theory Tamed,” Joseph Henry Press, Washington, D.C.,1997.

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2016-06-01
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How to Cite

Dey, A., Bhattacha, D. K., Tibarewala, D. N., Dey, N., Ashour, A., Le, D. N., … Gospodinov, M. (2016). Chinese-chi and Kundalini yoga Meditations Effects on the Autonomic Nervous System: Comparative Study. International Journal of Interactive Multimedia and Artificial Intelligence, 3(7), 87–95. https://doi.org/10.9781/ijimai.2016.3713

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