Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements
Article first published online: 26 DEC 2012
© 2012 The Authors. Respirology © 2012 Asian Pacific Society of Respirology
Volume 18, Issue 1, pages 108–116, January 2013
How to Cite
RAOUFY, M. R., HAJIZADEH, S., GHARIBZADEH, S., MANI, A. R., EFTEKHARI, P. and MASJEDI, M. R. (2013), Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements. Respirology, 18: 108–116. doi: 10.1111/j.1440-1843.2012.02251.x
- Issue published online: 26 DEC 2012
- Article first published online: 26 DEC 2012
- Accepted manuscript online: 17 AUG 2012 04:37AM EST
- Received 14 March 2012; invited to revise 21 April 2012, 15 May 2012; revised 22 April 2012, 16 May 2012; accepted 8 June 2012 (Associate Editor: Chi Chiu Leung).
- artificial neural network;
- nonlinear model;
- qualitative diagnostic calibration;
- respiratory inductance plethysmography;
- respiratory volume
Background and objective: Respiratory inductive plethysmography is a non-invasive technique for measuring respiratory function. However, there are challenges associated with using linear methods for calibration of respiratory inductive plethysmography. In this study, we developed two nonlinear models, artificial neural network and adaptive neuro-fuzzy inference system, to estimate respiratory volume based on thoracoabdominal movements, and compared these models with routine linear approaches, including qualitative diagnostic calibration and multiple linear regression.
Methods: Recordings of spirometry volume and respiratory inductive plethysmography were obtained for 10 normal subjects and 10 asthmatic patients, during asynchronous breathing for 7 min. The first 5 min of recording were used to develop the models; the remaining data were used for subsequent validation of the results.
Results: The results from the nonlinear models fitted the spirometry volume curve significantly better than those obtained by linear methods, particularly during asynchrony (P < 0.05). On a breath-by-breath analysis, estimates of tidal volume, total cycle time and sigh values using the artificial neural network model were accurate by comparison with qualitative diagnostic calibration. In contrast to the artificial neural network model, there was a significant correlation between values for thoracoabdominal asynchrony and increased error of qualitative diagnostic calibration (P < 0.05).
Conclusions: These results indicate that the nonlinear methods can be adapted to closely simulate variable conditions and used to study the patterns of volume changes during normal and asynchronous breathing.