Impact Analysis of Meditation on Physiological Signals

A classifier that combines respiration and EEG signals can distinguish Vipassana meditation from a control condition with 92% accuracy, compared to 85% accuracy using EEG signals alone. Forty novice meditators underwent a three-week meditation intervention, and their respiration and EEG data were analyzed using wavelet transforms and a support vector machine. Differences between meditation and control states appeared across alpha, beta, theta, delta, and gamma frequency bands. The combined respiration-EEG classifier offers a feasible objective marker for verifying meditation ability and may help advance mind-body medicine by providing a quantitative measure of meditative state.

Study at a glance

Abstract

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.