中文摘要：

利用两种基于熵的非线性复杂度测度：近似熵和样本熵,研究了专业射击运动员两种不同状态下（休息和练习赛）心率变异性信号的复杂度.计算结果表明：射击运动员休息时其心率变异性信号的熵值大于射击比赛时信号的熵值,这意味着运动员一旦进行射击比赛时,其心率变异性信号复杂度降低了,心跳变得更为规则了.为了更好地应用这两种基于熵的方法,进一步分析了算法中的两个重要影响因素：矢量匹配容差r和序列长度N对算法性能的影响.分析结果表明：只要参数选择在合适的范围内,近似熵和样本熵都能够正确地区分出两种不同状态的心率变异性信号,但样本熵测度更适合量化射击运动员短时心率变异性信号,尤其当心跳时间序列降为几百点时,这在实际应用中显得尤为重要.

英文摘要：

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.