Analysis of adventitious lung sounds originating from pulmonary tuberculosis

Tuberculosis is a common and potentially deadly infectious disease, usually affecting the respiratory system and causing the sound properties of symptomatic infected lungs to differ from non-infected lungs. Auscultation is often ruled out as a reliable diagnostic technique for TB due to the random distribution of the infection and the varying severity of damage to the lungs. However, advancements in signal processing techniques for respiratory sounds can improve the potential of auscultation far beyond the capabilities of the conventional mechanical stethoscope. Though computer-based signal analysis of respiratory sounds has produced a significant body of research, there have not been any recent investigations into the computer-aided analysis of lung sounds associated with pulmonary Tuberculosis (TB), despite the severity of the disease in many countries. In this paper, respiratory sounds were recorded from 14 locations around the posterior and anterior chest walls of healthy volunteers and patients infected with pulmonary TB. The most significant signal features in both the time and frequency domains associated with the presence of TB, were identified by using the statistical overlap factor (SOF). These features were then employed to train a neural network to automatically classify the auscultation recordings into their respective healthy or TB-origin categories. The neural network yielded a diagnostic accuracy of 73%, but it is believed that automated filtering of the noise in the clinics, more training samples and perhaps other signal processing methods can improve the results of future studies. This work demonstrates the potential of computer-aided auscultation as an aid for the diagnosis and treatment of TB.