In this work, classification of spirometric pulmonary function test data performed using two artificial neural network methods is compared and reported. The pulmonary function data (N=150) were obtained from volunteers, using commercially available Spirometer, and recorded by standard data acquisition protocol. The data were then used to train (N=100) as well as to test (N=50) the neural networks. The classification was carried out using back propagation and radial basis function neural networks. The results confirm that the artificial neural network methods are useful for the classification of spirometric pulmonary function data. Further, it appears that the Radial basis function neural network is more sensitive when compared to back propagation neural networks. In this paper, the methodology, data collection procedure and neural network based analysis are described in details.

Publication timeframe:
6 times per year
Journal Subjects:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing