Study of Heart Rate Variability to Comprehend the Significance of Singing Bowl Meditation on the Functioning of the Autonomic Nervous System
,
Abstract
This study is aimed to determine whether Himalayan singing bowl vibrations could lead to deeper and faster relaxation than supine silence. Numerous civilizations have used singing bowls, gongs, bells, didgeridoos, and voice sounds and chants as instruments for sound healing for ages in religious rites, festivals, social celebrations, and meditation activities. The effect of sound vibrations on physical and mental wellness is supported by scientific research. Although various pieces of research have demonstrated the effect of meditation on humans, very few studies have been done on the beneficial effects of singing bowls on the body and the mind (decrease in unease and temperament, Electroencephalogram etc.). This study suggests two machine learning (ML) models for automatic classification of the meditative state from the normal state using the HRV data. The HRV parameters were subjected to statistics-based t-test method to choose appropriate inputs for the ML models. In the first case study, it seems that the MLP 31-13-2 model, boasting a training accuracy of 83.75%, was the most effective model. On average, the RBF 31-17-2 model performed the best in testing for the second case study.
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[2] H. C. Lou, T. W. Kjaer, L. Friberg, G. Wildschiodtz, S. Holm, and M. Nowak, "A 15O‐H2O PET study of meditation and the resting state of normal consciousness," Human brain mapping, vol. 7, no. 2, pp. 98-105, 1999.
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[4] A. Mariotti, "The effects of chronic stress on health: new insights into the molecular mechanisms of brain–body communication," Future science OA, vol. 1, no. 3, 2015.
[5] P. A. Thoits, "Stress and health: Major findings and policy implications," Journal of health social behavior, vol. 51, no. 1_suppl, pp. S41-S53, 2010.
[6] M. R. Salleh, "Life event, stress and illness," The Malaysian journal of medical sciences: MJMS, vol. 15, no. 4, p. 9, 2008.
[7] H. Benson and W. Proctor, Relaxation revolution: The science and genetics of mind body healing. Simon and Schuster, 2011.
[8] Y. Trivedi Gunjan, R. Hemalatha, and K. Ramani, "Chronic Diseases and Mind Body Management, an Introduction (Technical Note), Reference No: CMHS0044TEC," Indian Institute of Management, 2018.
[9] J. Fachner and S. Rittner, "Ethno Therapy, Music and Trance: an EEG investigation into a sound-trance induction," in States of Consciousness: Springer, 2011, pp. 235-256.
[10] P. Hess, "Singing Bowls for Health and Inner Harmony," Through Sound MassageAccording to Peter Hess, Germany: DruckereiRindt GmbH, Fulda, 2008.
[11] L. Pigaiani, Bagno Armonico®-Massaggio sonoro con campane tibetane: Basi teoriche e campi di applicazione. Fontana Editore, 2014.
[12] N. H. Fletcher and T. D. Rossing, The physics of musical instruments. Springer Science & Business Media, 2012.
[13] T. D. Rossing, J. Yoo, and A. Morrison, "Acoustics of percussion instruments: An update," Acoustical science technology, vol. 25, no. 6, pp. 406-412, 2004.
[14] O. Inácio, L. Henrique, and J. Antunes, "The physics of Tibetan singing bowls," Revista de Acústica, vol. 35, pp. 1-2, 2003.
[15] D. Terwagne and J. W. Bush, "Tibetan singing bowls," Nonlinearity, vol. 24, no. 8, p. R51, 2011.
[16] M. Sakakibara, S. Takeuchi, and J. Hayano, "Effect of relaxation training on cardiac parasympathetic tone," Psychophysiology, vol. 31, no. 3, pp. 223-228, 1994.
[17] V. Pichot et al., "Wavelet transform to quantify heart rate variability and to assess its instantaneous changes," Journal of Applied Physiology, vol. 86, no. 3, pp. 1081-1091, 1999.
[18] S. Phongsuphap, Y. Pongsupap, P. Chandanamattha, and C. Lursinsap, "Changes in heart rate variability during concentration meditation," International journal of cardiology, vol. 130, no. 3, pp. 481-484, 2008.
[19] Y. Bai et al., "Nonlinear coupling is absent in acute myocardial patients but not healthy subjects," vol. 295, no. 2, pp. H578-H586, 2008.
[20] S. Akselrod, D. Gordon, F. A. Ubel, D. C. Shannon, A. C. Berger, and R. J. Cohen, "Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control," science, vol. 213, no. 4504, pp. 220-222, 1981.
[21] M. N. Levy, "Brief reviews: sympathetic-parasympathetic interactions in the heart," Circulation research, vol. 29, no. 5, pp. 437-445, 1971.
[22] C.-K. Peng et al., "Exaggerated heart rate oscillations during two meditation techniques," vol. 70, no. 2, pp. 101-107, 1999.
[23] A. Goshvarpour, A. Goshvarpour, and S. Rahati, "Analysis of lagged Poincare plots in heart rate signals during meditation," Digital Signal Processing, vol. 21, no. 2, pp. 208-214, 2011.
[24] A. Goshvarpour and A. Goshvarpour, "Poincare indices for analyzing meditative heart rate signals," Biomedical journal, vol. 38, no. 3, 2015.
[25] J. Li, J. Hu, Y. Zhang, and X. Zhang, "Dynamical complexity changes during two forms of meditation," Physica A: Statistical Mechanics its Applications, vol. 390, no. 12, pp. 2381-2387, 2011.
[26] R. Song, C. Bian, and Q. D. Ma, "Multifractal analysis of heartbeat dynamics during meditation training," Physica A: Statistical Mechanics its Applications, vol. 392, no. 8, pp. 1858-1862, 2013.
[27] J. Alvarez-Ramirez, E. Rodríguez, and J. Echeverría, "Fractal scaling behavior of heart rate variability in response to meditation techniques," Chaos, Solitons Fractals, vol. 99, pp. 57-62, 2017.
[28] A. Goshvarpour and A. Goshvarpour, "Recurrence plots of heart rate signals during meditation," International Journal of Image, Graphics Signal Processing, vol. 4, no. 2, p. 44, 2012.
[29] A. Goshvarpour and A. Goshvarpour, "Comparison of higher order spectra in heart rate signals during two techniques of meditation: Chi and Kundalini meditation," Cognitive neurodynamics, vol. 7, no. 1, pp. 39-46, 2013.
[30] A. Goshvarpour and A. Goshvarpour, "Classification of heart rate signals during meditation using Lyapunov exponents and entropy," Int. J. Intell. Syst. Appl, vol. 2, pp. 35-41, 2012.
[31] J. Gao et al., "Entrainment of chaotic activities in brain and heart during MBSR mindfulness training," vol. 616, pp. 218-223, 2016.
[32] T. L. Goldsby and M. E. Goldsby, "Eastern integrative medicine and ancient sound healing treatments for stress: recent research advances," Integrative Medicine: A Clinician's Journal, vol. 19, no. 6, p. 24, 2020.
[33] T. Stegemann, M. Geretsegger, E. Phan Quoc, H. Riedl, and M. Smetana, "Music therapy and other music-based interventions in pediatric health care: An overview," Medicines, vol. 6, no. 1, p. 25, 2019.
[34] A. Maratos, C. Gold, X. Wang, and M. Crawford, "Music therapy for depression," Cochrane database of systematic reviews, no. 1, 2008.
[35] D. Leubner and T. Hinterberger, "Reviewing the effectiveness of music interventions in treating depression," Frontiers in psychology, vol. 8, p. 1109, 2017.
[36] Q. Tang, Z. Huang, H. Zhou, and P. Ye, "Effects of music therapy on depression: A meta-analysis of randomized controlled trials," PloS one, vol. 15, no. 11, p. e0240862, 2020.
[37] H. Kamioka et al., "Effectiveness of music therapy: a summary of systematic reviews based on randomized controlled trials of music interventions," vol. 8, p. 727, 2014.
[38] T. Van Criekinge, K. D'Août, J. O'Brien, and E. Coutinho, "The influence of sound-based interventions on motor behavior after stroke: A systematic review," Frontiers in neurology, vol. 10, p. 1141, 2019.
[39] J. Stanhope and P. Weinstein, "The human health effects of singing bowls: A systematic review," Complementary Therapies in Medicine, vol. 51, p. 102412, 2020.
[40] S. G. Hofmann and A. F. Gómez, "Mindfulness-based interventions for anxiety and depression," Psychiatric clinics, vol. 40, no. 4, pp. 739-749, 2017.
[41] J. M. Landry, "Physiological and psychological effects of a Himalayan singing bowl in meditation practice: a quantitative analysis," American Journal of Health Promotion, vol. 28, no. 5, pp. 306-309, 2014.
[42] T. L. Goldsby, M. E. Goldsby, M. McWalters, and P. J. Mills, "Effects of singing bowl sound meditation on mood, tension, and well-being: an observational study," Journal of evidence-based complementary alternative medicine, vol. 22, no. 3, pp. 401-406, 2017.
[43] A. L. Goldberger, Z. D. Goldberger, and A. Shvilkin, Clinical Electrocardiography: A Simplified Approach E-Book: A Simplified Approach. Elsevier Health Sciences, 2017.
[44] M. Jaderberg, A. Vedaldi, and A. Zisserman, "Deep features for text spotting," in European conference on computer vision, 2014, pp. 512-528: Springer.
[45] P. Kaur and R. Sharma, "LabVIEW based design of heart disease detection system," in International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), 2014, pp. 1-5: IEEE.
[46] S. Jain and P. Kumar, "LABVIEW based expert system for Detection of heart abnormalities," in 2014 International Conference on Advances in Electrical Engineering (ICAEE), 2014, pp. 1-5: IEEE.
[47] A. M. A. Zaidi, M. J. Ahmed, and A. Bakibillah, "Feature extraction and characterization of cardiovascular arrhythmia and normal sinus rhythm from ECG signals using LabVIEW," in 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2017, pp. 1-6: IEEE.
[48] W. Khong, M. Mariappan, and N. K. Rao, "National Instruments LabVIEW Biomedical Toolkit for Measuring Heart Beat Rate and ECG LEAD II Features," in IOP Conference Series: Materials Science and Engineering, 2019, vol. 705, no. 1, p. 012020: IOP Publishing.
[49] A. J. Camm et al., "Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology," 1996.
[50] F. Shaffer and J. Ginsberg, "An overview of heart rate variability metrics and norms," Frontiers in public health, vol. 5, p. 258, 2017.
[51] M. Nardelli, A. Greco, M. Bianchi, E. P. Scilingo, and G. Valenza, "Classifying affective haptic stimuli through gender-specific heart rate variability nonlinear analysis," IEEE Transactions on Affective Computing, 2018.
[52] F. X. Cepeda, M. Lapointe, C. O. Tan, and J. A. Taylor, "Inconsistent relation of nonlinear heart rate variability indices to increasing vagal tone in healthy humans," Autonomic Neuroscience, vol. 213, pp. 1-7, 2018.
[53] R. A. Hoshi et al., "Linear and nonlinear analyses of heart rate variability following orthostatism in subclinical hypothyroidism," Medicine, vol. 98, no. 4, 2019.
[54] R. E. Kleiger, P. K. Stein, and J. T. Bigger Jr, "Heart rate variability: measurement and clinical utility," Annals of Noninvasive Electrocardiology, vol. 10, no. 1, pp. 88-101, 2005.
[55] M. P. Tarvainen, J. A. Lipponen, and P. Kuoppa, "Analysis and Preprocessing of HRV—Kubios HRV Software," in ECG Time Series Variability Analysis: CRC Press, 2017, pp. 159-186.
[56] H. Nagendra, V. Kumar, and S. Mukherjee, "Cognitive behavior evaluation based on physiological parameters among young healthy subjects with yoga as intervention," Computational and Mathematical Methods in Medicine, vol. 2015, 2015.
[57] M. M. Corrales, B. de la Cruz Torres, A. G. Esquivel, M. A. G. Salazar, and J. N. Orellana, "Normal values of heart rate variability at rest in a young, healthy and active Mexican population," health, vol. 4, no. 7, pp. 720-726, 2012.
[58] M. P. Tarvainen, J.-P. Niskanen, J. A. Lipponen, P. O. Ranta-Aho, and P. A. Karjalainen, "Kubios HRV–heart rate variability analysis software," Computer Methods and Programs in Biomedicine, vol. 113, no. 1, pp. 210-220, 2014.
[59] C. K. Peng, S. Havlin, H. E. Stanley, and A. L. Goldberger, "Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 5, no. 1, pp. 82-87, 1995.
[60] D. Semenick, "Tests and measurements: The T-test," Strength Conditioning Journal, vol. 12, no. 1, pp. 36-37, 1990.
[61] C. Sammut and G. I. Webb, Encyclopedia of machine learning and data mining. Springer Publishing Company, Incorporated, 2017.
[62] V. Kotu and B. Deshpande, Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann, 2014.
[63] A. Mohanty. (2019). Multi layer Perceptron (MLP) Models on Real World Banking Data. Available: https://becominghuman.ai/multi-layer-perceptron-mlp-models-on-real-world-banking-data-f6dd3d7e998f
[64] D. G. Rees, Essential statistics. Chapman and Hall/CRC, 2018.
[65] S.-D. Wu and P.-C. Lo, "Inward-attention meditation increases parasympathetic activity: a study based on heart rate variability," Biomedical Research, vol. 29, no. 5, pp. 245-250, 2008.
[66] P. W. Kamen, H. Krum, and A. M. Tonkin, "Poincare plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans," Clinical science, vol. 91, no. 2, pp. 201-208, 1996.
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| Files | ||
| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Meditation Heart rate variability Autonomic nervous system Machine learning RBF GBT | ||
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How to Cite
1.
Upadhyay R, Champaty B, Nayak S. Study of Heart Rate Variability to Comprehend the Significance of Singing Bowl Meditation on the Functioning of the Autonomic Nervous System. Frontiers Biomed Technol. 2025;.
