ABSTRACT
Obstructive Lung Disease (OLD) is a major respiratory disease with airflow limitation due to an obstruction in the airway. Lung function gradually decreases in patients with OLD if the disease remains untreated. Early detection and continuous treatment with a change in lifestyle might improve the quality of life and also prevent the possibility of disease progression. Conventionally, OLD is detected through a cumbersome procedure using a spirometer. In this work, a new method is proposed in which the respiration signal from normal subjects and OLD patients were analyzed and morphological changes were studied. Features from the respiration signal were extracted and used for classification using the two-class Support Vector Machine (SVM) classifier. The present study showed up to 100% accuracy with a specified set of features for 22 subjects as discussed in the result section.
