Classification of sounds indicative of respiratory diseases

Abstract

This work presents a system achieving classification of respiratory sounds directly related to various diseases of the human respiratory system, such as asthma, COPD, and pneumonia. We designed a feature set based on wavelet packet analysis characterizing data coming from four sound classes, i.e. crack, wheeze, normal, crack+wheeze. Subsequently, the captured temporal patterns are learned by hidden Markov models (HMMs). Finally, classification is achieved via a directed acyclic graph scheme limiting the problem space while based on decisions made by the available HMMs. Thorough experiments following a well-established protocol demonstrate the efficacy of the proposed solution.