Predictability of breathing parameters during cardiopulmonary exercise testing using entropy measurements

Erratic breathing at rest and during exercise has been described in patients with dysfunctional breathing (DB). Sample entropy (SampEn) and approximate entropy (ApEn) were developed to assess the predictability of parameters in time series data. However, SampEn and ApEn were developed for data without trends. Their potential role in interpreting datasets during cardiopulmonary exercise testing (CPET) remains uncertain. We used simulations based on real-life exercise data to evaluate the effects of trends, varying numbers of analyzed respiratory cycles, and different coefficients used to calculate the tolerance interval on ApEn and SampEn measurements. We tested the LOESS<sub>0.75</sub> method to correct the trend in the calculation of SampEn and ApEn. Trends led to a significant underestimation of ApEn and SampEn values. The LOESS<sub>0.75</sub> method yielded values very close to the predicted ApEn and SampEn. Modifying the number of respiratory cycles and the coefficient used to calculate the tolerance interval had major implications on ApEn and SampEn. In conclusion, the residuals from the LOESS<sub>0.75</sub> method can provide a corrected estimate of ApEn and SampEn in exercise data. The number of respiratory cycles analyzed and the coefficient used to calculate SampEn or ApEn should always be reported.

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Publication Details

Journal
Physiological Reports
Published
2025-09-29
DOI
https://doi.org/10.14814/phy2.70556
Citations
1
Primary Topic
Heart Rate Variability and Autonomic Control
Type
article
Field-Weighted Citation Impact
1.39
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article

Predictability of breathing parameters during cardiopulmonary exercise testing using entropy measurements

Pierantonio Laveneziana, Isabelle Frésard, Pierre‐Olivier Bridevaux, Ivan Guerreiro et al.
1 citations
Physiological Reports
Heart Rate Variability and Autonomic Control
1.39
article

Predictability of breathing parameters during cardiopulmonary exercise testing using entropy measurements

Pierantonio Laveneziana, Isabelle Frésard, Pierre‐Olivier Bridevaux, Ivan Guerreiro, Roberto Desponds, Marco Altarelli, Antoine Beurnier, Frédéric Lador, Cyril Jaksic, Anne Bergeron, Léon Genecand, Chloé Cantero, David Montani, Gaëtan Simian
article en
1 citations

Abstract

Erratic breathing at rest and during exercise has been described in patients with dysfunctional breathing (DB). Sample entropy (SampEn) and approximate entropy (ApEn) were developed to assess the predictability of parameters in time series data. However, SampEn and ApEn were developed for data without trends. Their potential role in interpreting datasets during cardiopulmonary exercise testing (CPET) remains uncertain. We used simulations based on real-life exercise data to evaluate the effects of trends, varying numbers of analyzed respiratory cycles, and different coefficients used to calculate the tolerance interval on ApEn and SampEn measurements. We tested the LOESS<sub>0.75</sub> method to correct the trend in the calculation of SampEn and ApEn. Trends led to a significant underestimation of ApEn and SampEn values. The LOESS<sub>0.75</sub> method yielded values very close to the predicted ApEn and SampEn. Modifying the number of respiratory cycles and the coefficient used to calculate the tolerance interval had major implications on ApEn and SampEn. In conclusion, the residuals from the LOESS<sub>0.75</sub> method can provide a corrected estimate of ApEn and SampEn in exercise data. The number of respiratory cycles analyzed and the coefficient used to calculate SampEn or ApEn should always be reported.

Physiological ReportsVol. 13(19)
University of Geneva (CH), Inserm (FR), Université Paris-Saclay (FR), Sorbonne Université (FR), Hôpital Beau-Séjour (CH), Assistance Publique – Hôpitaux de Paris (FR), Hypertension pulmonaire : physiopathologie et innovation thérapeutique (FR), Hôpital du Valais (CH), Bicêtre Hospital (FR), Neurophysiologie respiratoire expérimentale et clinique
Openalex Percentile: Top 14%
Heart Rate Variability and Autonomic Control
1.39
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