Proper parameters can improve performance of entropy methods for discerning electrocardiogram (ECG) signals. So, we tried to determine proper parameters of three entropy methods i.e., a novel permutation ratio entropy (PRE), sample entropy (SmpE) and permutation entropy (PE) for discerning several typical ECG RR interval recordings i.e., normal sinus rhythm (NSR), congestive heart failure (CHF) and NSR and arrhythmia RR (ARR) interval recordings. The three entropy methods were first calculated for a logistic sequence to evaluate their sensitivity to dynamic property changes within a time series. Their capabilities of distinguishing complexity between NSR and CHF, NSR and ARR, and CHF and ARR RR interval recordings were compared. Statistical differences between the three entropy values for normal (i.e., NSR) and abnormal RR interval recordings (i.e., CHF and ARR) were analysed respectively. Performance of the entropy methods in simultaneously discerning the three groups (i.e., NSR, CHF and ARR groups) were also compared. PRE more accurately reflected logistic sequence changes from period doublings to chaos than SmpE or PE did. In experiments with real data, PRE correctly yielded higher values on NSR RR recordings than on CHF and ARR recordings and exhibited significant differences (p < 0.01) on more parameter pairs than SmpE and PE did.

适当的参数可以提高用于识别心电图（ECG）信号的熵方法的性能。因此，我们尝试确定三种熵方法的适当参数，即新颖的置换比率熵（PRE），样本熵（SmpE）和置换熵（PE），以区分几种典型的ECG RR间隔记录，即正常窦性心律（NSR） ，充血性心力衰竭（CHF），NSR和心律失常RR（ARR）间隔记录。首先为逻辑序列计算这三种熵方法，以评估它们对时间序列中动态属性变化的敏感性。比较了它们区分NSR和CHF，NSR和ARR以及CHF和ARR RR间隔记录的复杂性的能力。正常（即NSR）和异常RR间隔记录（即NSR）的三个熵值之间的统计差异 ，CHF和ARR）。还比较了熵方法在同时识别三个组（即NSR，CHF和ARR组）中的性能。与SmpE或PE相比，PRE更准确地反映了逻辑序列从周期加倍到混乱的变化。在具有实际数据的实验中，PRE在NSR RR记录上正确地产生了比CHF和ARR记录更高的值，并且表现出显着差异（p <0.01）的参数对要比SmpE和PE多。