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Multiscale Entropy Reveals Reduced Airflow Complexity in Chronic Obstructive Pulmonary Disease: Associations with
Eduarda Martins de Faria1,2, Cíntia Moraes de Sá Sousa1,2, Caroline de Oliveira Ribeiro1,2
1Biomedical Instrumentation Laboratory, Institute of Biology, Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
Annals of Biomedical Engineering
|August 13, 2026
Summary
Multiscale entropy (MSE) analysis reveals reduced airflow complexity in chronic obstructive pulmonary disease (COPD). These noninvasive methods accurately detect respiratory dysfunction and correlate with lung function, aiding COPD diagnosis.
Area of Science:
- Pulmonary Medicine
- Biomedical Engineering
- Non-invasive Diagnostics
Background:
- Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation and respiratory dysfunction.
- Current diagnostic methods for COPD primarily rely on spirometry and imaging, which may not fully capture dynamic airflow alterations.
- Multiscale entropy (MSE) analysis offers a novel approach to quantify signal complexity and has shown potential in characterizing physiological systems.
Purpose of the Study:
- To investigate the efficacy of multiscale entropy (MSE) methods in characterizing airflow dynamics in COPD patients.
- To assess the association between MSE-derived airflow complexity and standard lung function parameters.
- To evaluate the diagnostic performance of MSE methods for COPD detection.
Main Methods:
- Spontaneous airflow signals were recorded from 29 controls and 25 COPD patients.
- Signal complexity was quantified using MSE, refined composite MSE (RCMSE), refined composite multiscale fuzzy entropy (RCMFE), and multiscale permutation entropy (MPE).
- Entropy indices were analyzed across different scale ranges and at a specific time scale (τ=1), and correlated with spirometry and respiratory resistance (R6). Diagnostic performance was assessed using ROC analysis.
Main Results:
- COPD patients exhibited significantly reduced airflow complexity across all entropy measures compared to controls.
- Entropy indices showed positive correlations with spirometric parameters and inverse correlations with respiratory resistance (R6).
- MSE and RCMSE, particularly over short time scales (S0-50), demonstrated high diagnostic accuracy (AUC > 0.90) for COPD detection.
Conclusions:
- Multiscale entropy analysis of spontaneous airflow is a sensitive and noninvasive method for detecting and characterizing respiratory dysfunction in COPD.
- MSE methods provide complementary information to conventional lung function tests, reflecting clinically relevant airflow alterations.
- These findings support the potential clinical applicability of MSE analysis in COPD management and diagnosis.
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