Raoufy, MR;
Ghafari, T;
Darooei, R;
Nazari, M;
Mahdaviani, SA;
Eslaminejad, AR;
Almasnia, M;
... Hajizadeh, S; + view all
(2016)
Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern.
PLoS One
, 11
(1)
, Article e0147976. 10.1371/journal.pone.0147976.
Preview |
Text
Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern.pdf - Published Version Download (1MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0147976 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pone.0147976 |
| Language: | English |
| Additional information: | Copyright: © 2016 Raoufy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Education |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1478250 |
Archive Staff Only
 |
View Item |
