Abstract

Identifying the presence of sputum in the lung is essential in detection of diseases such as lung infection, pneumonia and cancer. Cough type classification (dry/wet) is an effective way of examining presence of lung sputum. This is traditionally done through physical exam in a clinical visit which is subjective and inaccurate. This work proposes an objective approach relying on the acoustic features of the cough sound. A total number of 5971 coughs (5242 dry and 729 wet) were collected from 131 subjects using Smartphone. The data was reviewed and annotated by a novel multi-layer labeling platform. The annotation kappa inter-rater agreement score is measured to be 0.81 and 0.37 for 1st and 2nd layer respectively. Sensitivity and specificity values of 88% and 86% are measured for classification between wet and dry coughs (highest across the literature).

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