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.

Fig 1. Breathing pattern in a representative subject.

(a), An experimental tracing of abdominal and rib cage movement signals recorded continuously by pneumotrace bands (only a few seconds of tracing is presented for clarity). The plethysmography signals were calibrated to volume using an artificial neural network model. (b) and (c), Original (“raw”) inter-breath interval (b) and lung volume (c) time series during 60 min of resting breathing in a representative subject.

Fig 2. Detrended fluctuation analysis (DFA) plots for the inter-breath interval(a) and lung volume (b) time series in representative subjects.

A linear relationship between log(n) and log[f(n)] indicates the presence of fractal dynamics. The scaling exponent α quantifies the strength of long-range correlations within the time series. CAA, controlled atopic asthma; UAA, uncontrolled atopic asthma; UNAA, uncontrolled non-atopic asthma.

Fig 3. Differences of complexity indices between cases.

a, p < 0.05 comparing to Healthy; b, p < 0.05 comparing to CAA; c, p < 0.05 comparing to UAA. CAA, controlled atopic asthma; UAA, uncontrolled atopic asthma; UNAA, uncontrolled non-atopic asthma; DFA, Detrended fluctuation analysis; SampEn, sample entropy; LLE, Largest Lyapunov exponents; IBI, inter-breath interval; V, volume.

Fig 4. ROC curves for the ability of the complexity indices.

(a), discriminating asthma from healthy; (b), discriminating uncontrolled from controlled asthma; (c), discriminating non-atopic from atopic asthma. DFA, detrended fluctuation analysis; SampEn, sample entropy; LLE, largest Lyapunov exponents; IBI, inter-breath interval; LV, lung volume.