Analysis of the Complexity Pattems in Respiratory Data

Allergic asthma and/or rhinitis are common allergic diseases in the clinical, with gradually increase in incidence rates, overlapping prevalence and costs in quality of life. The conventional indicators such as respiratory function and specific immune marker cannot reflect the holistic nature of patient health while the continuous monitored data can reflect the conditions of patients more exactly. Complexity dynamics such as multiscale entropy (MSE) and multifractal detrended fluctuation analysis (MF-DFA) of continuous physiological parameters have shown great applicability in reflecting the conditions of patients, but their use is still in its infancy. Thus, to fill in the gap, we applied noninvasive wearable devices to continuously monitor physiological parameters (airflow) of healthy children and children suffering with allergic asthma and/or rhinitis. We explore the complexity pattern of the monitored data using MSE and MDFA in the two groups of children. Our results reveal that the respiration dynamic for healthy children is more complicated than patients. This may have the potential to be used in clinical practice to differentiate the healthy children and those with Allergic Asthma and Rhinitis.