A novel method for automatic identification of respiratory disease from acoustic recordings.

Abstract

This paper evaluates the use of breath sound recordings to automatically determine the respiratory health status of a subject. A number of features were investigated and Wilcoxon Rank Sum statistical test was used to determine the significance of the extracted features. The significant features were then passed to a feature selection algorithm based on mutual information, to determine the combination of features that provided minimal redundancy and maximum relevance. The algorithm was tested on a publicly accessible respiratory sounds database. With the testing dataset, the trained classifier achieved accuracy of 87.1%, sensitivity of 86.8% and specificity of 93.6%. These are promising results showing the possibility of determining the presence or absence of respiratory disease using breath sounds recordings.