Detection of Obstructive Respiratory Abnormality Using Flow–Volume Spirometry and Radial Basis Function Neural Networks

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.

[1]  R. Mehra,et al.  Interpreting pulmonary function tests: recognize the pattern, and the diagnosis will follow. , 2003, Cleveland Clinic journal of medicine.

[2]  N. Gross.  Chronic obstructive pulmonary disease outcome measurements: what's important? What's useful? , 2005, Proceedings of the American Thoracic Society.

[3]  W. Zheng,et al.  Facial expression recognition using kernel canonical correlation analysis (KCCA) , 2006, IEEE Transactions on Neural Networks.

[4]  E. Maarsingh,et al.  Spirometry and forced oscillometry assisted optimal frequency band determination for the computerized analysis of tracheal lung sounds in asthma. , 2004, Physiological measurement.

[5]  Wei Qiu,et al.  Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter , 2002, IEEE Transactions on Biomedical Engineering.

[6]  H. Suga,et al.  Instantaneous Pressure‐Volume Relationships and Their Ratio in the Excised, Supported Canine Left Ventricle , 1974, Circulation research.

[7]  Rakesh Kumar Sinha,et al.  Backpropagation Artificial Neural Network Detects Changes in Electro-Encephalogram Power Spectra of Syncopic Patients , 2007, Journal of Medical Systems.

[8]  O. Barnea,et al.  Model-Based Prediction of Expiratory Resistance Index in Patients with Asthma , 2004, Journal of Clinical Monitoring and Computing.

[9]  Nicolaos B. Karayiannis,et al.  On the construction and training of reformulated radial basis function neural networks , 2003, IEEE Trans. Neural Networks.

[10]  T Botsis,et al.  Neural networks for the prediction of spirometric reference values , 2003, Medical informatics and the Internet in medicine.

[11]  Zsolt Tulassay,et al.  Application of neural networks in medicine - a review , 1998 .

[12]  C. Eswaran,et al.  Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks , 2006, Journal of Medical Systems.

[13]  Zheng Rong Yang,et al.  A novel radial basis function neural network for discriminant analysis , 2006, IEEE Transactions on Neural Networks.

[14]  P. Enright,et al.  Spirometry to detect and manage chronic obstructive pulmonary disease and asthma in the primary care setting , 2005 .

[15]  Eddie Yin-Kwee Ng,et al.  A Framework for Early Discovery of Breast Tumor Using Thermography with Artificial Neural Network , 2003, The breast journal.

[16]  S Ramakrishnan,et al.  Assessment and classification of normal and restrictive respiratory conditions through pulmonary function test and neural network , 2007, Journal of medical engineering & technology.

[17]  Ulmer Wt.  Lung function--clinical importance, problems, and new results. , 2003 .

[18]  N. Shime,et al.  TLR4 signaling is essential for survival in acute lung injury induced by virulent Pseudomonas aeruginosa secreting type III secretory toxins , 2004, Respiratory research.

[19]  P. Paré,et al.  The nature of small-airway obstruction in chronic obstructive pulmonary disease. , 2004, The New England journal of medicine.

[20]  R. Pierce,et al.  Spirometry: an essential clinical measurement. , 2005, Australian family physician.

[21]  O Barnea,et al.  Maximum expiratory flow-volume curve: mathematical model and experimental results. , 1995, Medical engineering & physics.

[22]  K. Chung,et al.  Models of chronic obstructive pulmonary disease , 2004, Respiratory research.

[23]  Tianping Chen,et al.  Approximation capability to functions of several variables, nonlinear functionals and operators by radial basis function neural networks , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[24]  Tom Chau,et al.  A radial basis classifier for the automatic detection of aspiration in children with dysphagia , 2006, Journal of NeuroEngineering and Rehabilitation.

[25]  B. Juroszek.  The Influence of Gas Parameters on the Results of Spirometric Test , 2005 .

[26]  M. Mintchev,et al.  Centre-specific multichannel electrogastrographic testing utilizing wavelet-based decomposition , 2006, Physiological measurement.