Młyńczak Marcel, Krysztofiak Hubert
Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland.
Department of Applied Physiology, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland.
Front Physiol. 2018 Oct 30;9:1455. doi: 10.3389/fphys.2018.01455. eCollection 2018.
Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well-established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean heart rate, the root-mean-square difference of successive RR intervals (RMSSD), its natural logarithm (lnRMSSD), the mean respiratory rate (RR), the breathing activity coefficients, and the resulting breathing regularity (BR) were estimated. Several causal discovery frameworks were applied, comprising Generalized Correlations (GC), Causal Additive Modeling (CAM), Fast Greedy Equivalence Search (FGES), Greedy Fast Causal Inference (GFCI), and two score-based Bayesian network learning algorithms: Hill-Climbing (HC) and Tabu Search. The discovery of cardiorespiratory paths appears ambiguous. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes average heart activity, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training. In the next step, we plan to expand the study using time-related causality analyses.
精英运动员的训练需要定期进行生理和医学监测，以规划训练计划、强度和量，以及后续的恢复。在运动医学中，基于心电图的分析已经很成熟。然而，它们很少考虑呼吸和心脏活动的对应关系。考虑到这种相互影响，我们假设可以通过因果推断来开展运动员监测，并且在采用详细的、与时间相关的技术之前，应该先采用一种更通用的、与时间无关的方法，该方法要考虑整个参与者群体以及描述整个信号的参数。本研究的目的是在100名精英运动员处于仰卧和站立两种体位休息时，发现心脏和呼吸变量之间的一般因果路径。从100名精英运动员身上获取了心电图和阻抗式肺量图信号。估计了平均心率、连续RR间期的均方根差（RMSSD）、其自然对数（lnRMSSD）、平均呼吸频率（RR）、呼吸活动系数以及由此产生的呼吸规律性（BR）。应用了几种因果发现框架，包括广义相关性（GC）、因果加法建模（CAM）、快速贪婪等价搜索（FGES）、贪婪快速因果推断（GFCI），以及两种基于分数的贝叶斯网络学习算法：爬山法（HC）和禁忌搜索。心肺路径的发现似乎并不明确。数据最有力支持的主要但仍较微弱的规则是：对于仰卧位——潮气量导致心脏活动变化，进而导致平均心脏活动，平均心脏活动又导致呼吸时间；对于站立位——标准化呼吸活动变化导致平均心脏活动。所提出的方法允许对精英运动员这一特定群体进行数据驱动且与时间无关的分析，而无需考虑先验知识。然而，结果似乎与医学背景一致。因果推断是一种用于分析复杂生物反应的有趣数学方法。人们可以用它来描绘运动员特征并规划适当的训练。下一步，我们计划使用与时间相关的因果分析来扩展这项研究。
