Classification and detection of wheezes in respiratory sounds

Abstract

Analyzing respiratory sounds and detecting anomalies in them with intelligent computer algorithms has opened a new era for auscultation that has 250 years of history. These algorithms can overcome the drawbacks of conventional stethoscopes and support medics about auscultation. In this thesis, a new intelligent algorithm to detect wheezes superimposed on vesicular sounds is developed and presented. Detection of wheezes with intelligent algorithms is one of the hot topics currently being researched by many researchers. They are continuous musical adventitious respiratory sounds. Their duration, intensity, and phase in respiratory sounds give essential information for the diagnosis and prognosis of respiratory diseases. In this study, one of the aims is to determine the best discriminative features among nine features which are mostly used in other researches. The other aim is to find the best-performed machine learning classifier to classify wheezes and normal respiratory sounds. Last, we created a novel detection algorithm is presented to detect correctly the wheeze interval in recorded respiratory sounds by employing selected machine learning model to respiratory sounds.