Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof

Marcel Młyńczak , Krysztofiak Hubert


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 Hz; (2) further down-sampled to 2.5 Hz; 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.
Author Marcel Młyńczak (FM / IMBE)
Marcel Młyńczak,,
- The Institute of Metrology and Biomedical Engineering
, Krysztofiak Hubert
Krysztofiak Hubert,,
Journal seriesFrontiers in Physiology, ISSN 1664-042X
Issue year2019
Publication size in sheets0.65
Keywords in Englishgranger causality framework, athlete training adaptation biomarker, cardiac function, tidal volume, elite athletes
ASJC Classification2737 Physiology (medical); 1314 Physiology
Languageen angielski
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 29-11-2019, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus Citations = 2; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.986; WoS Impact Factor: 2018 = 3.201 (2) - 2018=3.921 (5)
Citation count*2 (2020-05-23)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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