Estimation of airway smooth muscle stiffness changes due to length oscillation using artificial neural network.
This paper presents a novel approach to estimate stiffness changes in airway smooth muscles due to external oscillation. Artificial neural networks are used to model the stiffness changes due to cyclic stretches of the smooth muscles. The nonlinear relationship between stiffness ratios and oscillation frequencies is modeled by a feed-forward neural network (FNN) model. The structure of the FNN is selected through the training and validation using literature data from 11 experiments with different muscle lengths, muscle masses, oscillation frequencies and amplitudes. Data pre-processing methods are used to improve the robustness of the neural network model to match the non-linearity. The validation results show that the FNN model can predict the stiffness ratio changes with a mean square error of 0.0042.
Institute of Biomedical Technologies, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand. ahmed.al-jumaily@aut.ac.nz
Ahmed
A
Al-Jumaily
Leizhi
L
Chen
D000465:Algorithms; D001696:Biomechanical Phenomena / Q000502:physiology; D016001:Confidence Intervals; D008168:Lung / Q000502:physiology*; D009130:Muscle, Smooth / Q000502:physiology*; D016571:Neural Networks, Computer; D009991:Oscillometry