Complexity of the Airflow in COPD

Recently, there has been a growing interest in the study of nonlinear dynamics as a methodology for complementary analysis to characterize the respiratory pattern. These methods are well established in studies of heart rate. The analyzes evaluate complex signals, including large-scale fractal correlations and distributions in time series, and can provide relevant clinical information. Measures such as approximate entropy and sample entropy have shown great potential in the evaluation of the complexity of the respiratory system, providing information relevant to the understanding of physiological and pathophysiological processes. These measures are based on the concept of non-linearity in the presence of a high number of interconnections, resulting in the complex behavior exhibited by physiological systems. The approximate entropy (ApEn) is related to the amount of clutter, complexity or unpredictability of a data series over time. In a complementary way, the sample entropy (SampEn), is a far more elaborate than the ApEn, to reduce possible biased estimates due to self-similarity. A study conducted by our group in asthma patients with different levels of bronchial obstruction demonstrated a significant reduction in airflow approximate entropy (ApEnV´) in asthmatic subjects. Investigators believe that in COPD, similar to that which occurs in asthma disorders that are associated with complex changes in the pathophysiology triggering breath control, possibly resulting in changes in air flow (V´). Considering the development "silent" changes of mechanical ventilation in COPD patients and its clinical relevance, as well as the difficulty of identifying such changes through conventional methods, we observed the need to obtain more detailed information, including the complexity of the system breathing for better understanding of factors that contribute to the illness. In this context, the objectives of this study were: (1) analyze the influence of airway obstruction in