Abstract
Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.
Highlights
Human breathing dynamics reveal complex pattern of variations that is related to multiple feedback loops that interact with the internal and external stimuli to optimize the efficiency of gas exchange [1]
controlled atopic asthma (CAA), uncontrolled atopic asthma (UAA), and uncontrolled non-atopic asthma (UNAA) subjects were enrolled in the study
Each group had 10 subjects, and there were no significant differences in age (27.6±5.3, 30.8±9.8, 31.1±7.2, and 32.7±8.1, respectively; p = 0.526) and body mass index (BMI) (22.7±1.6, 22.2±2.1, 22.6±1.5, and 22.9±1.7, respectively; p = 0.845) among the groups
Summary
Human breathing dynamics reveal complex pattern of variations that is related to multiple feedback loops that interact with the internal and external stimuli to optimize the efficiency of gas exchange [1]. Understanding such nonlinear behavior may provide physiological insight to the respiratory system and may be used as a tool for clinical assessment of respiratory disorders [2]. The pathophysiology of non-atopic asthma is poorly understood and may require a novel experimental approach [6] It seems that nonlinear dynamics is useful for explaining the complexity of breathing pattern in asthma [3, 7, 8]. There is evidence to show that respiratory variability analysis can distinguish patients with atopic from non-atopic asthma [8]
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