Młyńczak Marcel, Krysztofiak Hubert
Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw, Poland.
Department of Applied Physiology, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland.
Front Physiol. 2019 Feb 5;10:45. doi: 10.3389/fphys.2019.00045. eCollection 2019.
Fitness level, fatigue and adaptation are important factors for determining the optimal training schedule and predicting future performance. We think that adding analysis of the mutual relationships between cardiac and respiratory activity enables better athlete profiling and feedback for improving training. Therefore, the main objectives were (1) to apply several methods for temporal causality analysis to cardiorespiratory data; (2) to establish causal links between the signals; and (3) to determine how parameterized connections differed across various subgroups. One hundred elite athletes (31 female) and a control group of 20 healthy students (6 female) took part in the study. All were asked to follow a protocol comprising two 5-min sessions of free breathing - once supine, once standing. The data were collected using Pneumonitor 2. Respiratory-related curves were obtained through impedance pneumography, along with a single-lead ECG. Several signals (e.g., tidal volume, instantaneous respiratory rate, and instantaneous heart rate) were derived and stored as: (1) raw data down-sampled to 25; (2) further down-sampled to 2.5; and (3) beat-by-beat sequences. Granger causality frameworks (pairwise-conditional, spectral or extended), along with Time Series Models with Independent Noise (TiMINo), were studied. The connections enabling the best distinctions were found using recursive feature elimination with a random forest kernel. Temporal causal links are the most evident between tidal volume and instantaneous heart rate signals. Predictions of the "effect" variable were improved by adding preceding "cause" samples, by medians of 20.3% for supine and 14.2% for standing body positions. Parameterized causal link structures and directions distinguish athletes from non-athletes with 83.3% accuracy on average. They may also be used to supplement standard analysis and enable classification into groups exhibiting different static and dynamic components during performance. Physiological markers of training may be extended to include cardiorespiratory data, and causality analysis may improve the resolution of training profiling and the precision of outcome prediction.
体能水平、疲劳程度和适应性是确定最佳训练计划以及预测未来表现的重要因素。我们认为，增加对心脏和呼吸活动之间相互关系的分析能够更好地描绘运动员特征并为改进训练提供反馈。因此，主要目标是：（1）将几种时间因果关系分析方法应用于心肺数据；（2）建立信号之间的因果联系；（3）确定参数化连接在不同亚组之间如何不同。100名精英运动员（31名女性）和20名健康学生组成的对照组（6名女性）参与了该研究。所有人都被要求遵循一个方案，包括两次5分钟的自由呼吸环节——一次仰卧，一次站立。使用呼吸监测仪2收集数据。通过阻抗式肺量计获得与呼吸相关的曲线，同时还有单导联心电图。导出并存储了几个信号（例如潮气量、瞬时呼吸频率和瞬时心率），存储形式为：（1）下采样至25的原始数据；（2）进一步下采样至2.5的数据；（3）逐搏序列。研究了格兰杰因果关系框架（成对条件、频谱或扩展型）以及具有独立噪声的时间序列模型（TiMINo）。使用带有随机森林核的递归特征消除法找到了能够实现最佳区分的连接。潮气量和瞬时心率信号之间的时间因果联系最为明显。通过添加先前的“原因”样本，“效应”变量的预测得到了改善，仰卧位时中位数提高了20.3%，站立位时提高了14.2%。参数化的因果联系结构和方向平均以83.3%的准确率区分运动员和非运动员。它们还可用于补充标准分析，并在表现过程中实现对具有不同静态和动态成分的组进行分类。训练的生理标志物可能会扩展到包括心肺数据，因果关系分析可能会提高训练特征描绘的分辨率和结果预测的精度。
