Analysis of Heart Rate Variability in Meditation Using Normalized Shannon Entropy

Abstract

This paper is an attempt in search of human physiology in meditation. Heart rate variability (HRV) signal has been captured and
analyzed for this purpose. Two types of data sources have been used, the Physionet1 and our own data bank, collected by our own instrument. The popular Shannon entropy has been used to measure the complexity of each signal collected from each subject in two different conditions, one in pre-meditative state and the other in meditative state. The Shannon entropy has been normalized by dividing it with the length of the signal because the signals are of different lengths. The results show that the complexity of HRV signal decreases in meditation in compared to its premeditative state and that this low complexity is always found in case of advanced meditators but in case of novices the result is mixed. These results satisfy our natural intuition that meditative state has a completely
different physiology and that it can be achieved by deep and prolonged meditation only.