EXPERIMENTAL ANALYSIS ON HUMAN RESPIRATORY DYNAMICS USING FLOW-VOLUME SPIROMETRY AND COMBINED NEURAL NETWORKS

Abstract

In this work, detection of pulmonary abnormalities carried out using flow-volume spirometry and combined neural network is presented. The respiratory data (N = 225) were obtained from volunteers using a commercially available spirometer recorded by a standard acquisition protocol. The spirometric data were used for classification of normal, restrictive, and obstructive abnormalities using combined neural networks. The first level of the networks was implemented to assess the primary abnormality, whereas the second level of the networks further classified the degree of abnormality. The results were validated with the measured values of accuracy, sensitivity, specificity, and adjusted accuracy. Combined neural networks using a radial basis algorithm were found to be effective in classifying various degrees of abnormalities as normal, restrictive, or obstructive. Furthermore, combined neural networks achieved significant improvement in accuracy compared to stand-alone neural networks. It appears that this method of integrated assessment is useful in understanding pulmonary function dynamics with incomplete data and/or data with poor recording.