Crackle separation and classification from normal Respiratory sounds using Gaussian Mixture Model

Abstract

Analysis of Respiratory sound signal is helpful in detection of adventitious lung sound which are an indication of disease. This helps in classification of normal respiratory sounds from abnormal respiratory sounds and this can be used to accurately diagnose respiratory diseases as is done by a medical practitioner via auscultation. This process has subjective nature and that is why simple auscultation cannot be relied upon. A computer aided diagnostic system which analyzes respiratory sounds can be very helpful in detection of various respiratory diseases such as pneumonia, asthma, bronchitis and tuberculosis. In this paper we present a novel method for automated detection of crackles which indicate severity of a respiratory disease. The proposed system consists of four modules i.e., pre-processing in which noise is filtered out, followed by feature extraction. The proposed system then performs feature selection based on rank tests and finally classification to separate crackles from normal breath sounds.