Sign series entropy analysis of short-term heart rate variability

Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physio-logical essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis (SSEA) is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.