Based on the method of multi-scale space (Pm, Gm) and data surrogating test, time-irreversibility analysis is applied to the heart rate variabilities (HRVs) from different crowds and different states, awake and asleep respectively, of healthy youths. The results show that i) the HRVs of healthy crowed have irreversible dynamics prevailingly, while the irreversibility decreases but does not disappear with aging or heart disease appearing. For example, most (more than 75%) of the congestive heart failure (CHF) patients still have irreversible dynamics; ii) for HRVs of healthy crowd, irreversible dynamics presents the daytime/nighttime rhythms and their significant difference between in daytime and in nighttime. And a stronger irreversibility is detected in nighttime. HRV is generated by the cardiac dynamic system, in which regulations usually perform via multiple feedback loops with different delays. Therefore, in order to arrive at a reliable conclusion, multi-scale strategy and data surrogating test are suggested to serve as the two elements for the detection of time irreversibility in HRV. The proposed method combines these two elements and reaches a conclusion consistent with the conclusions in previous reports.

Using two entropy-based measures, namely the approximate entropy and sample entropy measures, we studied the complexity of heart rate variability signals obtained from professional shooting athletes in the situations of rest and practice match. The results demonstrate that the values of two measures calculated from the resting signals are both greater than those calculated from the training signals, which means that the signals collected during the match are more regular compared to those acquired in a resting state. For a better application of the two methods, we further investigated the influences of two factors: threshold r and data length N, on the performance of the algorithms. Although both approaches have the ability to discriminate the complexity of heart beat interval series from different states of the shooters, provided that the parameters required by the algorithms are chosen within a proper range, it still seems that sample entropy method is more appropriate in quantifying the short-term heart rate variability signals for shooting athletes, especially when the time series are only several hundred points long.

Poincaré plot is an important method in nonlinear analysis of heart rate variability(HRV). Based on the modified Poincaré plot, two arguments-the regional distribution entropy and regional distribution coefficient are put forward for the quantitative description of the scatter distribution trends in the studied area. And the distributions of the Poincaré plot in the four quadrants are calculated separately. Through the analysis of the HRV sample data from healthy young people, older people and congestive heart failure(CHF) sufferers in MIT-BIH database, we find that the two parameters show a significant difference between the groups. Meanwhile, the analysis results in different quadrants show that the sensitivities of the four quadrants are different, and especially in the first quadrant, the sensitivity is best. This phenomenon shows that the changes of vagal control function are most significant between healthy people and CHF sufferers, which is consistent with previous physiological research conclusion. Experimental results also show that the method can be used for short-term data, and thus is easier to extend to clinical applications.