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Analysis of cardiorespiratory coupling in athletes in rest using causal approach

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Kasprowisko 2017 Presentation - Abstract by Marcel Młyńczak, Hubert Krysztofiak, Marek Żyliński and Gerard Cybulski

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Analysis of cardiorespiratory coupling in athletes in rest using causal approach

  1. 1. Analysis of cardiorespiratory coupling 
 in athletes in rest using causal approach Marcel Młyńczak, MSc, Hubert Krysztofiak, PhD,
 Marek Żyliński, MSc, Gerard Cybulski, PhD Zakopane, March 4th, 2017
  2. 2. Introduction ECG-based analyses in sport medicine • Well established in the community • Relatively simple • Usually measure single „modality” • Do not consider the mutual cardiorespiratory activity Left figure adapted from medicalexpo.com 2
  3. 3. Introduction Traditional respiratory monitoring • The most reliable methods • Direct measurements of airflows • The need for using a face mask or mouthpiece with nose clip • Uncomfortable during exercises, natural functioning or sleep Figures adapted from chat.stackexchange.com and legio24.pl Mesh grid of 
 known pneumatic resistance 3
  4. 4. Introduction Impedance pneumography Basic idea Changes of transthoracic bioimpedance are 
 connected with changes of the amount of air in the lungs Method of measurements The nature of the IP signals Carried out using tetrapolar method Volume-related Calibration Simple linear model provides the best accuracy of volume parameters estimation for specific electrode configuration 4
  5. 5. Objectives • Conducting pilot studies using Pneumonitor 2 in athletes in rest. • Preliminary analysis of cardiorespiratory coupling using 
 Granger causality test and by calculating best shift between: ➡ RR intervals (tachograms), ➡ Tidal Volume (TV) curves. 5
  6. 6. Methodology Participants Min Avg Max Weight [kg] 49.1 78.6 151.0 Height [cm] 158.0 183.4 208.0 BMI 17.4 23.2 42.7 Age 16.0 24.6 40.0 6 Polish elite athletes (studied before Rio 2016): 32 females and 73 males
  7. 7. Methodology Pneumonitor 2 • ECG signal to estimate heart rate and tachogram • Impedance signal relating to 
 main breathing activity • Portable • Recording on SD card • Rechargeable battery • Motion signal from 3-axis accelerometer to indicate 
 subject’s activity and body position 7
  8. 8. Methodology 8 IP electrodes Electrode configuration ECG electrodes
  9. 9. Methodology Study protocol 9 5 minute free breathing supine body position
  10. 10. Methodology • RR intervals were established using: ➡ R peaks detection, ➡ intervals between consecutive R peaks determination, ➡ interpolation. • Tidal Volume ➡ No calibrations procedures were performed. ➡ Assuming, that changes of impedance relates linearly 
 with changes of the amount of air in the lungs. Signals 10
  11. 11. Methodology • Granger causality test ➡ Statistical test for determining whether one time series is ”useful” 
 in modeling/predicting another one. ➡ Assessment whether breathing or cardiac activity could be consider 
 as a cause for the second signal changes. ➡ Which approach gives higher test statistics? • Best shift ➡ What is the shift between signals? ➡ What is the determination coefficient of linear model between tachogram and tidal volume after applying the adjustment? • ANOVA ➡ Significance estimation of each demographic parameter 
 on Granger causality test statistics and best shift parameters. Analysis 11
  12. 12. Results Example signals 12
  13. 13. Results • Higher test statistics of Granger causality test obtained while assuming tachogram as the cause. 13 Granger causality test
  14. 14. Results • Concerning physiological intuition we changed the convention ➡ the beginning of inspiration was marked as a maximum of TV signal • The best adjustment was a positive tachogram's shift by 296 ± 264 ms ➡ taking into account the results only for arbitrarily set R2 > 0.2 14 Best shift
  15. 15. Results • No considered demographic parameter had significant effect on the differences between Granger causality test statistics and the best shift between RR intervals and TV signals. 15 ANOVA
  16. 16. Summary Depending on the interpretation, the causal analysis allows to assess synchronization between cardiac and respiratory function. Pneumonitor 2 provides the opportunity to assess cardiorespiratory coupling, minimally affecting the natural functioning of the subject. Further analyses will include approaches in spectral and information domain. 16
  17. 17. Marcel Młyńczak, MSc mlynczak@mchtr.pw.edu.pl Analysis of cardiorespiratory coupling 
 in athletes in rest using causal approach Zakopane, March 4th, 2017
  18. 18. Discussion Ambulatory respiratory monitoring Sleep Physiology Sport medicine • Hypo-, normo-, and hyperventilation monitoring in the obese and those with neuromuscular diseases • Cardiorespiratory coupling analysis • In-house diagnostics • Training control • Determining the level of exercice Figures adapted from ”Pulmonary Function Testing” Rolf M. Schlegelmilch, Rüdiger Kramme, Springer, 2011 • Monitoring of breathing disorders • Analysis of the effects of pharmacological treatment 18
  19. 19. Discussion Quantitative respiratory parameters Figures adapted from Clevend Clinic Medical & Wikicommons materials 19 Breathing frequency [ l/min ] Flow-volume parametersTidal volume [ l ] Minute ventilation [ l/min ]

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