Discovery of causal paths in cardiorespiratory parameters: a time-independent approach in elite athletes
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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 basic 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 to reveal cardiac and respiratory activity, respectively. The RR intervals and tidal volumes were established, and heart rate, the root-mean-square difference of successive R-R intervals, respiratory rate, breathing regularity, and best shift between signals were estimated. The causal discovery framework comprised an absolute residuals criterion, generalized correlations, and causal additive modeling. We found that the average activity rate seems to affect the indicator responsible for diversity (effect of HR on RMSSD, or RR on BR). There are different graphical structures and directions in the two body positions. In several cases, causal mediation analysis supports those findings only for supine body position. The presented approach allows data-driven and time-independent analysis of various groups of athletes, without considering prior knowledge. However, the results seem to be consistent with the medical background. Therefore, in the next step, we plan to expand the study using time-related causality analyses.
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