Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements

ABSTRACT 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