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Predictability of breathing parameters during cardiopulmonary exercise testing using entropy measurements
Léon Genecand1,2, Cyril Jaksic3, Gaëtan Simian4
1Service de Pneumologie, Département de Médecine, Hôpitaux Universitaires de Genève, Genève, Switzerland.
Erratic breathing in patients with dysfunctional breathing (DB) can be assessed using sample entropy (SampEn) and approximate entropy (ApEn). A new method corrects for trends in exercise data, improving accuracy for these measurements.
Area of Science:
- Physiology
- Biomedical Engineering
- Data Science
Background:
- Dysfunctional breathing (DB) is characterized by erratic breathing patterns during rest and exercise.
- Sample entropy (SampEn) and approximate entropy (ApEn) are used to measure time series predictability.
- The application of SampEn and ApEn in cardiopulmonary exercise testing (CPET) data, which often contain trends, is uncertain.
Purpose of the Study:
- To evaluate the impact of trends, respiratory cycle variations, and tolerance interval coefficients on SampEn and ApEn calculations.
- To assess the effectiveness of the LOESS0.75 method for correcting trends in entropy calculations during exercise.
Main Methods:
- Simulations using real-life exercise data.
- Calculation of SampEn and ApEn with and without trend correction.
- Application of the LOESS0.75 trend correction method.
Main Results:
- Trends significantly underestimated SampEn and ApEn values.
- The LOESS0.75 method effectively corrected for trends, yielding results close to predicted values.
- Variations in the number of respiratory cycles and tolerance coefficients greatly influenced SampEn and ApEn outcomes.
Conclusions:
- Residuals from the LOESS0.75 method offer a reliable way to estimate SampEn and ApEn in exercise data.
- Reporting the number of respiratory cycles and tolerance coefficients is crucial for accurate interpretation of SampEn and ApEn results.
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