Detection of Respiratory Abnormalities Using Artificial Neural Networks
Mahdi J. Baemani, Amirhasan Monadjemi and Payman Moallem
DOI : 10.3844/jcssp.2008.663.667
Journal of Computer Science
Volume 4, Issue 8
Problem Statement: Lung disease is a major threat to the human health regarding the industrial life, air pollution, smoking, and infections. Lung function tests are often performed using spirometry. Approach: The present study aims at detecting obstructive and restrictive pulmonary abnormalities. Lung function tests are often performed using spirometry. In this study, the data were obtained from 250 volunteers with standard recording protocol in order to detect and classify pulmonary diseases into normal, obstructive and restrictive. Firstly, spirometric data was statistically analyzed concerning its significance for neural networks. Then, such parameters were presented as input to MLP and recurrent networks. Results: These two networks detected normal and abnormal disorders as well as obstructive and restrictive patterns, respectively. Moreover, the output data was confirmed by measuring accuracy and sensitivity. Conclusion: The results show that the proposed method could be useful for detecting the function of respiratory system.
© 2008 Mahdi J. Baemani, Amirhasan Monadjemi and Payman Moallem. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.