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It appears there is no holistic framework ( Heidari et al., 2018). Adaptation and recovery can be estimated, but usually only regarding a specific parameter. Fitness level and fatigue are largely subjective. The problem is in quantifying and analyzing the input information. The training schedule may be established objectively, and competitions produce a wealth of performance metrics. Training load indicators may come from the training set-up itself, from training equipment, accelerometers, etc. The advance of medical devices and even wearable sensors makes it easy to quantify outputs. 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.Д¬omprehensive monitoring and testing of homeostatic processes, fitness level, fatigue, adaptation and recovery appears crucial for sports medicine practitioners to identify optimal training schedules, establish sufficient training loads and promote desirable progress and competitive performance ( Meeusen et al., 2013 Halson, 2014 Coutts et al., 2017 Kellmann et al., 2018 Schneider et al., 2018). They may also be used to supplement standard analysis and enable classification into groups exhibiting different static and dynamic components during performance. Parameterized causal link structures and directions distinguish athletes from non-athletes with 83.3% accuracy on average. Predictions of the вЂњeffectвЂќ variable were improved by adding preceding вЂњcauseвЂќ samples, by medians of 20.3% for supine and 14.2% for standing body positions. Temporal causal links are the most evident between tidal volume and instantaneous heart rate signals. The connections enabling the best distinctions were found using recursive feature elimination with a random forest kernel.

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Granger causality frameworks (pairwise-conditional, spectral or extended), along with Time Series Models with Independent Noise (TiMINo), were studied. 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 Hz (2) further down-sampled to 2.5 Hz and (3) beat-by-beat sequences.

Respiratory-related curves were obtained through impedance pneumography, along with a single-lead ECG. The data were collected using Pneumonitor 2.

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All were asked to follow a protocol comprising two 5-min sessions of free breathing - once supine, once standing.

One hundred elite athletes (31 female) and a control group of 20 healthy students (6 female) took part in the study. 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. We think that adding analysis of the mutual relationships between cardiac and respiratory activity enables better athlete profiling and feedback for improving training. 2Department of Applied Physiology, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, PolandДЇitness level, fatigue and adaptation are important factors for determining the optimal training schedule and predicting future performance.1Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw, Poland.Marcel MЕ‚yЕ„czak 1 * and Hubert Krysztofiak 2

Temporal causality loop