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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.
Two kinds of entropy-based nonlinear complexity measure, approximate entropy and sample entropy, are used to study the complexity of heart rate variability signal in two different states of professional shooting athletes (rest and practice). The results show that when the shooting athlete rests The entropy of the heart rate variability signal is greater than the entropy value of the signal during the shooting game, which means that once the athlete performs shooting competition, the heart rate variability signal complexity is reduced and the heartbeat becomes more regular. In order to better apply These two kinds of entropy-based methods further analyze two important factors in the algorithm: the effect of vector matching tolerance r and sequence length N on the performance of the algorithm.The analysis results show that as long as the parameters are selected within the appropriate range, the approximate entropy And sample entropy can correctly distinguish the heart rate variability signal of two different states, but the sample entropy measure is more suitable for quantifying the short-term heart rate variability signal of the shooting athlete, especially when the heartbeat time series is reduced to hundreds of points, Application is particularly important.