Impact Analysis of Meditation on Physiological Signals

Rahul Ingle


Vipassana meditation is a type of mindfulness meditation technique mostly practices in southwest part of the globe, where relaxing but highly awake and alert mind state is achieved. Vipassana Meditation involvement was carried out for a group of mid-aged people. These people constantly dealing with high level of stress. This research evaluates advance signal processing methodologies of respiration and electroencephalographic (EEG) signals during Vipassana meditation and control condition to assist in quantification of the meditative state. EEG of respiration and Vipassana Meditation data were collected and analyzed on 40 novice meditators after a 3-weeks meditation intervention. Collected data were analyzed with an advanced mathematical tool such as Wavelet Transform for spectral analysis.  The Support Vector Machine is used as a classifier for classification of EEG signals to evaluate an objective marker for meditation. We analyzed and observed Vipassana meditation and control condition differences in the different frequency bands such as (alpha, beta, theta, delta, and gamma) for EEG signals of subjects. Moreover, we confirmed a classifier with a higher accuracy (92%) during respiration and EEG signals for discrimination between meditation and control conditions, rather than EEG signal alone (85%). A classifier based on respiration and EEG signal is the feasible objective marker for verifying the ability of meditation. Different level of meditation depth and experience can be studied using this classifier for future studies. The main objective of this work is to develop a physiological meditation marker as a medication (mind-body medicine field) to advance by nourishing severity of methods.


Meditation; EEG signal; Stockwell Transform; SVM.


Barnes PM, Bloom B., “Complementary and alternative medicine use among adults and children: United States”. 2007:2008.

Ospina MB, Bond K, Karkhaneh M, Tjosvold L, Vandermeer B, Liang Y, et al., “ Meditation practices for health: state of the research”, Evid Rep Technol Assess (Full Rep) 2007 Number 155, Publication No. 07-E010:1-263.

Lau MA, Bishop SR, Segal ZV, Buis T, Anderson ND, Carlson L, et al., “The Toronto Mindfulness Scale: development and validation”, Journal of Clinical Psychology. 2006;62:1445–1467.

Cahn BR, Polich J., “Meditation states and traits. EEG, ERP, and neuroimaging studies”, Psychological Bulletin. 2006;132:180–211.

Farb NA, Anderson A, Mayberg H, Bean J, McKeon D, Segal ZV. “Minding one's emotions: mindfulness training alters the neural expression of sadness. Emotion”. 2010;10:25–33.

Brefczynski-Lewis JA, Lutz A, Schaefer HS, Levinson DB, Davidson RJ., “Neural correlates of attentional expertise in long-term meditation practitioners”, Proc Natl Acad Sci U S A. 2007;104:11483–11488.

Lutz A, Greischar LL, Rawlings NB, Ricard M, Davidson RJ., “Long-term meditators self-induce high-amplitude gamma synchrony during mental practice”. Proc Natl Acad Sci U S A. 2004;101: 16369–16373.

Ditto B, Eclache M, Goldman N., “Short-term autonomic and cardiovascular effects of mindfulness body scan meditation”. Ann Behav Med. 2006;32(3):227–234.

Wolkove N, Kreisman H, Darragh D, Cohen C, Frank H., “Effect of transcendental meditation on breathing and respiratory control”, J Appl Physiol. 1984;56(3):607–612.

Jerath R, Edry JW, Barnes VA, Jerath V., “Physiology of long pranayamic breathing: neural respiratory elements may provide a mechanism that explains how slow deep breathing shifts the autonomic nervous system”, Med Hypotheses. 2006;67(3):566–571.

Cohen S, Karmarck T, Mermelstein R., “A global measure of perceived stress”, J Health Soc Behav. 1983;24:385–396.

Whitham EM, Pope KJ, Fitzgibbon SP, Lewis T, Clark CR, Loveless S, “Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG”, Clin Neurophysiol. 2007; 118(8):1877–88.

Stockwell RG, Mansinha L, Lowe RP., “Localization of the complex spectrum: the S transforms”, IEEE Transactions on Signal Processing. 1996; 44(4):998–1001.

Cortes C, Vapnik V., “Support vector networks. Machine Learning”. 1995;20:273297.

Benson H.,”The relaxation response. Morrow”, New York: 1976.

Lazar SW, Bush G, Gollub RL, Fricchione GL Khalsa G, Benson H., “Functional brain mapping of the relaxation response and meditation”, Neuroreport. 2000;11:1–5.

Grossman P, Niemann L, Schmidt S, Walach H., “Mindfulness-based stress reduction and health benefits. A meta-analysis”, J Psychosom Res. 2004;57(1):35–43.

Chiesa A, Serretti A., “Mindfulness-based stress reduction for stress management in healthy people: a review and meta-analysis”, J Altern Complement Med. 2009;15(5):593–600.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOIV : International Journal on Informatics Visualization
Published by Information Technology Department
Politeknik Negeri Padang, Indonesia

© JOIV - ISSN : 2549-9610 | e-ISSN : 2549-9904 

Phone : +62-82386434344
Email  :

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats