Complexity Analysis Of Heartbeat Time Series

Heart rate variability (HRV) contains important information about the modulation of the cardiovascular system. Various methods of nonlinear dynamics and complexity measures have been applied to HRV analysis, among which entropy analysis is a hot topic that attracts many researchers due to its simplicity and validity.Thus, this paper mainly focuses on the entropy analysis of heartbeat interval time series. We test if some exercises like meditation can affect the heartbeat dynamics of human. Two public databases about Chinese CHI and Kundalini yoga are analyzed using permutation entropy algorithm and results show that these meditations can reduce the complexity of heart rate variability. Besides, a modified permutation entropy algorithm taking equal values into account is proposed and it allows for a more accurate characterization of system states. In addition, a new sign series entropy analysis (SSEA) method is proposed, which characterizes heartbeat series with three symbols. This method tries to eliminate the trivial details of heartbeat series and keep the most important variation trends. This method has strong resistance to noise and can better distinguish the heart rate variability under different physiological and pathological conditions.