Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks
Journal of Medical Systems
On the construction and training of reformulated radial basis function neural networks
IEEE Transactions on Neural Networks
A novel radial basis function neural network for discriminant analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Journal of Medical Systems
A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals
Journal of Medical Systems
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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驴=驴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.