Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern

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.

The effect of fractal-like mechanical ventilation on vital signs in a rat model of acute-on-chronic liver failure

“…Detrended fluctuation analysis of inter-breath intervals in group of rats with variable mechanical ventilation demonstrates a linear correlation between the log(scale) and log(fluctuations) as shown in figure 1. This indicated that variable mechanical ventilation has a fractal-like dynamic similar to healthy individuals (Raoufy et al 2016). Similar fractal-like pattern of fluctuation was observed in tidal volume fluctuations in the variable mechanical ventilation group as expected (data not shown).…”

Complexity Analysis of Respiratory Dynamics

“…Analyzing such nonlinear fluctuations may not only improve traditional assessments in diagnosing the onset of illness, its severity and prognosis (1), but also could provide new insights into the different pathophysiological characteristics of respiratory diseases (2). In addition to studies described by Thamrin and colleagues (1), our recent report (2) showed that both respiratory rhythm and volume fluctuate continuously under a delicate equilibrium to maintain adaptability to external or internal stimuli. These fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state.…”

“…A section of the article described previous studies that characterized the complex behavior of the lung in respiratory diseases using nonlinear analysis of the fluctuations of physiologic variables over time. Chronic lung diseases have variable clinical symptoms and represent complex behavior which may be associated with a shift in dynamics of respiratory system toward either too regular or too irregular (2). Analyzing such nonlinear fluctuations may not only improve traditional assessments in diagnosing the onset of illness, its severity and prognosis (1), but also could provide new insights into the different pathophysiological characteristics of respiratory diseases (2).…”

“…Chronic lung diseases have variable clinical symptoms and represent complex behavior which may be associated with a shift in dynamics of respiratory system toward either too regular or too irregular (2). Analyzing such nonlinear fluctuations may not only improve traditional assessments in diagnosing the onset of illness, its severity and prognosis (1), but also could provide new insights into the different pathophysiological characteristics of respiratory diseases (2). In addition to studies described by Thamrin and colleagues (1), our recent report (2) showed that both respiratory rhythm and volume fluctuate continuously under a delicate equilibrium to maintain adaptability to external or internal stimuli.…”

“…These fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. More importantly, receiver operating characteristic (ROC) curve analysis showed that respiratory variability analysis can be useful not only to aid in asthma diagnosis and in differentiating uncontrolled from controlled asthma but also for discriminating between nonatopic and atopic asthma (2). We found decreased long range correlation, irregularity and chaotic nature of respiratory dynamic in non-atopic asthma as compared with atopic asthma, possibly reflecting different pathophysiological mechanisms and maybe justifying a higher degree of disease severity in patients with non-atopic asthma (3).…”