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In this work, prediction of forced expiratory volume in pulmonary function test, carried out using spirometry and neural networks is presented. The pulmonary function data were recorded from volunteers using commercial available flow volume spirometer in standard acquisition protocol. The Radial Basis Function neural networks were used to predict forced expiratory volume in 1 s (FEV1) from the recorded flow volume curves. The optimal centres of the hidden layer of radial basis function were determined by k-means clustering algorithm. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal, restrictive and obstructive cases. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases. Prediction accuracy was more in obstructive abnormality when compared to restrictive cases. It appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

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.

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This work presents a prediction of forced expiratory volume in pulmonary function testing, using spirometry and neural networks. The pulmonary function data were recorded (n = 110) from volunteers using flow-volume spirometer with a standard acquisition protocol. From the recorded flow-volume curves, the acquired data are then used to predict forced expiratory volume in one second (FEV1) using a self-organizing map (SOM) and radial basis function neural networks. The SOM is used to determine the cluster centres of the hidden layer of radial basis function neural networks. The optimal widths of the Gaussian function of radial basis function neural networks were obtained from these centres and this network is then used to predict FEV1. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal and abnormal cases. The correlation between measured and predicted values of FEV1 for normal subjects was found to be 0.9. The prediction error for normal subjects is lower than that of restrictive subjects. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases.