Mobile stethoscope and signal processing algorithms for pulmonary screening and diagnostics

Abstract

Pulmonary diseases represent a large disease burden in terms of morbidity and mortality worldwide. For many reasons, including household air pollution and a shortage of trained doctors, this burden is concentrated in the developing world. The standard diagnostic pathway for pulmonary diseases is prohibitively expensive in developing countries, so these diseases are often misdiagnosed or underdiagnosed. To assist doctors and health workers, there is a need to create tools that can automatically recognize specific lung sounds and provide diagnostic guidance. As a first step towards this long-term goal, we have created a low-cost stethoscope and smartphone application to record lung sounds. We discuss problems we encountered with the initial design and demonstrate an improved design that is currently being used in the field. We also demonstrate an algorithm capable of automatic detection of wheeze sounds. The automatic wheeze detection algorithm uses time-frequency analysis and the Short Time Fourier Transform to identify sections of wheezing in recorded lung sound files. Unlike most published sound classification studies, we trained and tested our algorithms using sound data collected from 38 actual patients at a pulmonary clinic in Pune, India. Despite variability in the quality of the data, our algorithm demonstrated an accuracy of 86% for successfully detecting the presence of wheeze in a sound file. This mobile platform and detection algorithm demonstrates an important step in creating an automated platform for the diagnosis of pulmonary diseases in a real-world setting.