  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 HR, the RMSSD, its
natural logarithm, the mean respiratory rate, the breathing activity
coefficients, and the resulting breathing regularity were estimated. Several
causal discovery frameworks were applied: generalized correlations, CAM, FGES,
GFCI, and two Bayesian network learning algorithms: Hill-Climbing and Tabu. The
main, still mild, rules best supported by data are: for supine - tidal volume
causes heart activity variation, which causes HR, 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.
