HMM Based Cough Sound Analysis for Classifying Pneumonia and Asthma in Pediatric Population

Separating pediatric asthma from pediatric pneumonia is one of the major issues in remote areas. These diseases have overlapping symptoms, but require drastically different treatments. Existing guidelines for pneumonia classification in resource poor regions from The World Health Organization call for the use of bronchodilator test to separate asthma from pneumonia. However, bronchodilator is an expensive test to conduct and not easily available in remote areas. In this study, we propose an innovative and novel technique using cough sound analysis to separate pneumonia cases from asthma. In the work of this paper we analyzed cough sound data from 20 subjects (10 pneumonia and 10 asthma patients). Using mathematical features of cough sounds, an HMM classifier was trained to identify pneumonic cough and asthmatic cough. Then by computing Pneumonic Cough Index each patient was classified as either into pneumonia or asthma. Proposed method achieved an accuracy of 90% (sensitivity = 100% and specificity = 80%) in classifying pneumonia and asthma patients. Our results indicate that cough sound carry critical information which can be used to separate asthma patients from pneumonia. Proposed technique in this paper shows potential to become an alternative for bronchodilator test in the resource poor areas of the world.