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 project we implemented a novel method for automated detection of crackles and bronchial
breath sounds, which when coupled together with breath count per minute and patient history
indicate presence and severity of Pneumonia. After feature extraction and selection the system
performs classification to separate crackles and bronchial breath sounds from normal breath
sounds. At the same time the system applies an algorithm on the breath sounds to calculate
breaths per minute and score the history questions answered by the subject, some of which also
indicate presence of pneumonia.
