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Training of elite athletes requires regular physiological and medicalmonitoring to plan the schedule, intensity and volume of training, andsubsequent recovery. In sports medicine, ECG-based analyses are wellestablished. However, they rarely consider the correspondence of respiratoryand cardiac activity. Given such mutual influence, we hypothesize that athletemonitoring might be developed with causal inference and that detailed,time-related techniques should be preceded by a more general, time-independentapproach that considers the whole group of participants and parametersdescribing whole signals. The aim of this study was to discover general causalpaths among cardiac and respiratory variables in elite athletes in two bodypositions (supine and standing), at rest. ECG and impedance pneumographysignals were obtained from 100 elite athletes. The mean HR, the RMSSD, itsnatural logarithm, the mean respiratory rate, the breathing activitycoefficients, and the resulting breathing regularity were estimated. Severalcausal discovery frameworks were applied: generalized correlations, CAM, FGES,GFCI, and two Bayesian network learning algorithms: Hill-Climbing and Tabu. Themain, still mild, rules best supported by data are: for supine - tidal volumecauses heart activity variation, which causes HR, which causes respiratorytiming; and for standing - normalized respiratory activity variation causesaverage heart activity. The presented approach allows data-driven andtime-independent analysis of elite athletes as a particular population, withoutconsidering prior knowledge. However, the results seem to be consistent withthe medical background. Causality inference is an interesting mathematicalapproach to the analysis of biological responses, which are complex. One canuse it to profile athletes and plan appropriate training.

Discovery of causal paths in cardiorespiratory parameters: a time-independent approach in elite athletes