用于生理数据分析的时序和因果向量
Temporal orders and causal vector for physiological data analysis
摘要：
除了全球参数和时间序列为基础的方法，生理分析应构成一个本地的时间，特别是当分析数据的协议段。因此，我们介绍了R包实现时间顺序的因果向量（CV）的估计。它可以使用线性建模或时间序列距离。该算法进行了测试的心肺数据，包括潮气量和速度描记图曲线，从优秀运动员（仰卧和站立，在静态条件下）和对照组（不同的速率和呼吸深度，而仰卧）。我们检查了CV与体位或呼吸方式之间的关系。呼吸频率对CV的影响大于深度。当呼吸减慢时，血流速度描记图曲线相对更早于潮气量。临床相关性-该方法可以评估（1）两个信号之间的关系，两个信号中的一个在给定范围内时移，（2）最佳信号间偏移的曲线，以及（c）它们随时间的稳定性;也用于其他生理研究。
英文摘要：
In addition to the global parameter- and time-series-based approaches, physiological analyses should constitute a local temporal one, particularly when analyzing data within protocol segments. Hence, we introduce the R package implementing the estimation of temporal orders with a causal vector (CV). It may use linear modeling or time series distance. The algorithm was tested on cardiorespiratory data comprising tidal volume and tachogram curves, obtained from elite athletes (supine and standing, in static conditions) and a control group (different rates and depths of breathing, while supine). We checked the relation between CV and body position or breathing style. The rate of breathing had a greater impact on the CV than does the depth. The tachogram curve preceded the tidal volume relatively more when breathing was slower. Clinical relevance - The method can assess (1) relationships between two signals, having one of the two time-shifted in a given range, (2) curves of most optimal inter-signal shift, and (c) their stability across time; also for other physiological studies.
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