Impact of Meditation on Mental Health: A Case Study with Two Meditation Practices using Heart Rate Variability

Nonlinear analysis of heart rate variability (HRV) signals reveals characteristic changes during meditation that indicate enhanced parasympathetic activity and reduced stress levels. The study examined HRV signals from publicly available databases containing recordings before and after Chi meditation and Kundalini Yoga practice. Features analyzed included detrended fluctuation analysis, Hurst exponent, approximation entropy, degree of distribution, and multiscale permutation entropy. These changes underscore the potential of HRV analysis to quantify the physiological benefits of meditative practices and their impact on mental health and well-being.

Study at a glance

Abstract

Heart rate variability (HRV) signals provide a non-invasive way of analyzing the functioning of the autonomic nervous system (ANS), providing important information on how mental states affect the body. This study focused on how HRV features change during meditation compared to pre-meditation states. In this work, we propose analysis of HRV signals using nonlinear features including the detrended fluctuation analysis, the Hurst exponent, the approximation entropy, the degree of distribution, and the multiscale permutation entropy. The analysis was performed on the publicly available databases [1] which consists of HRV signals of subjects before and after practice of Chi meditation and Kundalini Yoga. The proposed study shows that there is a characteristic variation in both meditation states, which indicates enhanced parasympathetic activity and reduced stress levels during meditation. These changes underscore the potential of HRV analysis to quantify the physiological benefits of meditative practices and their impact on mental health and well-being.