In this work detection of pulmonary abnormalities carried out using flow-volume spirometer and Radial Basis Function Neural Network (RBFNN) is presented. The spirometric data were obtained from adult volunteers (N=100) with standard recording protocol. The pressure and resistance parameters were derived using the theoretical approximation of the activation function representing pressure-volume relationship of the lung. The pressure-time and resistance-expiration volume curves were obtained during maximum expiration. The derived values together with spirometric data were used for classification of normal and obstructive abnormality using RBFNN. The results revealed that the proposed method is useful for detecting the pulmonary functions into normal and obstructive conditions. RBFNN was found to be effective in differentiating the pulmonary data and it was confirmed by measuring accuracy, sensitivity, specificity and adjusted accuracy. As spirometry still remains central in the observations of pulmonary function abnormalities these studies seems to be clinically relevant.
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.
In this work, the classification of pulmonary function into normal and abnormal conditions is attempted using neural networks and spirometric measurements. The pulmonary function data (N = 229) for this study were obtained from volunteers using commercially available spirometers by adopting standard data acquisition protocol and recording conditions. The data were then subjected to neural network–based training (N = 159) and analysis (N = 70). The classification was carried out using standard feed-forward neural network and backpropagation algorithm. The outputs were then validated through sensitivity and specificity measurements together with clinical observation. The results confirmed the effectiveness of the neural network–based classification of spirometric data into normal and abnormal conditions. The sensitivity and specificity were found to be 89.25% and 82.25%, respectively. Furthermore, it seems that this method is useful in assessing the pulmonary function dynamics in cases with incomplete data and data with poor recordings. In this paper, the methodology, data collection procedure, and neural network–based analysis and results are described in detail.
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