Separation and classification of crackles and bronchial breath sounds from normal breath sounds using Gaussian mixture model

Abstract

A computer aided diagnostic system capable of analyzing respiratory sounds can be very helpful in detection of pneumonia, asthma and tuberculosis as the Respiratory sound signal carries information about the underlying physiology of the lungs and is used to detect presence of adventitious lung sounds which are an indication of disease. Respiratory sound analysis helps in distinguishing normal respiratory sounds from abnormal respiratory sounds and this can be used to accurately diagnose respiratory diseases as is done by a medical specialist via auscultation. This process has subjective nature and that is why simple auscultation cannot be relied upon.In this paper we present a novel method for automated detection of crackles and bronchial breath sounds which when coupled together indicate presence and severity of Pneumonia. 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 classification to separate crackles and bronchial breath sounds from normal breath sounds.

Keywords Positive Predictive Value, Gaussian Mixture Model, Wavelet Packet, Breath Sound, Lung Sound