Pulmonary and respiratory diseases (e.g. asthma, COPD, allergies, pneumonia, tuberculosis, etc.) represent a large proportion of the global disease burden, mortality, and disability. In this context of creating automated diagnostic tools, we explore how the analysis of voluntary cough sounds may be used to screen for pulmonary disease. As a clinical study, voluntary coughs were recorded using a custom mobile phone stethoscope from 54 patients, of which 7 had COPD, 15 had asthma, 11 had allergic rhinitis, 17 had both asthma and allergic rhinitis, and four had both COPD and allergic rhinitis. Data were also collected from 33 healthy subjects. These patients also received full auscultation at 11 sites, given a clinical questionnaire, and underwent full pulmonary function testing (spirometer, body plethysmograph, DLCO) which culminated in a diagnosis provided by an experienced pulmonologist. From machine learning analysis of these data, we show that it is possible to achieve good classification of cough sounds in terms of Wet vs Dry, yielding an ROC curve with AUC of 0.94, and show that voluntary coughs can serve as an effective test for determining Healthy vs Unhealthy (sensitivity=35.7% specificity=100%). We also show that the use of cough sounds can enhance the performance of other diagnostic tools such as a patient questionnaire and peak flow meter; however voluntary coughs alone provide relatively little value in determining specific disease diagnosis.
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