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Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements
Mohammad Reza Raoufy1, Sohrab Hajizadeh, Shahriar Gharibzadeh
1Department of Physiology, School of Medical Sciences, Tarbiat Modares University, London, UK.
Respirology (Carlton, Vic.)
|August 18, 2012
Summary
Nonlinear models, including artificial neural networks, significantly improve respiratory volume estimation using respiratory inductive plethysmography compared to linear methods, especially during breathing asynchrony.
Area of Science:
- Biomedical Engineering
- Respiratory Physiology
Background:
- Respiratory inductive plethysmography (RIP) is a non-invasive method for assessing respiratory function.
- Linear calibration methods for RIP face challenges, particularly with asynchronous breathing patterns.
Purpose of the Study:
- To develop and compare nonlinear models (artificial neural network, adaptive neuro-fuzzy inference system) against linear methods for RIP calibration.
- To estimate respiratory volume using thoracoabdominal movements.
Main Methods:
- Collected spirometry and RIP data from 10 normal and 10 asthmatic subjects during asynchronous breathing.
- Developed and validated nonlinear and linear models using the recorded data.
Main Results:
- Nonlinear models demonstrated significantly better fitting of spirometry volume curves than linear methods (P < 0.05).
- Artificial neural network model provided accurate breath-by-breath estimates for tidal volume and cycle time.
- Increased thoracoabdominal asynchrony correlated with higher error in linear calibration methods.
Conclusions:
- Nonlinear methods offer a superior approach for RIP calibration, closely simulating variable breathing conditions.
- These models are valuable for studying volume changes during both normal and asynchronous breathing patterns.
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