心率序列穷举运行的一种新随机过程及其分形参数估计
A new stochastic process to model Heart Rate series during exhaustive run and an estimator of its fractality parameter
摘要：
为了解释和解释生理信号行为，在所有努力期间或在比赛的不同阶段期间，在这些数据的波动中找到一些常数可能是有趣的（可以使用变化点检测方法来检测）。最近的几篇论文提出了长程相关（赫斯特）参数作为这样一个常数。然而，他们的研究结果引发了两个主要问题。首先，DFA方法通常用于估计该参数。显然，这种方法不能提供最有效的估计，而且即使在平滑趋势的情况下，它也根本不稳健。其次，该方法往往给出大于1的估计Hurst参数，这是长记忆平稳过程的较大可能值。在这篇文章中，我们提出了这些问题的解决方案，我们定义了一个新的模型，允许这样的估计参数。
英文摘要：
In order to interpret and explain the physiological signal behaviors, it can be interesting to find some constants among the fluctuations of these data during all the effort or during different stages of the race (which can be detected using a change points detection method). Several recent papers have proposed the long-range dependence (Hurst) parameter as such a constant. However, their results induce two main problems. Firstly, DFA method is usually applied for estimating this parameter. Clearly, such a method does not provide the most efficient estimator and moreover it is not at all robust even in the case of smooth trends. Secondly, this method often gives estimated Hurst parameters larger than 1, which is the larger possible value for long memory stationary processes. In this article we propose solutions for both these problems and we define a new model allowing such estimated parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
