Interacting Minds Centre, Department of Culture and Society, Aarhus University Aarhus, Denmark.
Front Physiol. 2013 Aug 13;4:211. doi: 10.3389/fphys.2013.00211. eCollection 2013.
Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-min (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics-fractal (DFA) and recurrence (RQA) analyses-reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to non-linear analyses, the success of non-linear methods for BPM data critically depends on their construction. Generally, "oversampled" BPM time-series can be recommended as they retain most of the information about non-linear aspects of heart beat dynamics.
最近，健康科学领域越来越关注心率的动态变化，将其作为即将发生故障和预后的指标。同样，在社会和认知科学中，心率也越来越多地被用作唤醒、情感投入的衡量标准，以及人际协调的标志。然而，对于映射心率的时间动态，哪种测量和分析工具最合适，尚未达成共识，文献中报告了相当不同的指标。由于心率变异性的复杂性指标严重依赖于数据的可变性，因此关于度量类型的不同选择可能会对结果产生实质性影响。在本文中，我们比较了两种突出的心跳数据类型的线性和非线性统计，即心跳间隔（RR 间隔）和每分钟心跳（BPM）。作为概念验证，我们采用了一种简单的休息-运动-休息任务，并表明非线性统计（分形（DFA）和递归（RQA）分析）揭示了心率活动的信息，超越了心率的简单水平。非线性统计揭示了运动后心率动态的持续影响，但它们成功做到这一点的能力严重依赖于所使用的数据类型：虽然 RR 间隔非常容易受到非线性分析的影响，但 BPM 数据的非线性方法的成功与否取决于其构建方式。一般来说，可以推荐“过采样”的 BPM 时间序列，因为它们保留了心率动态非线性方面的大部分信息。
