Wearable stethoscope for lung disease diagnosis

Abstract

Lung disease is one of the most widespread types of disease, especially in the era of COVID-19. Its diagnosis is of great importance, as different types have diverse treatments and prognoses. The most popular methods are computed tomography scanning, ultrasonogram, and bioimpedance sensors, but they are not suitable for wearable applications. Here, we developed a wearable stethoscope with an accompanying algorithm for lung disease diagnosis. It was demonstrated on 18 patients in hospital with three types of lung disease. After collecting and pre-processing lung sound signals, several machine learning methods with optimized features were applied and achieved high classification metrics. The features of the low-frequency wavelets decomposed from the lung sound signals were found to be important, serving as potential biomarkers for different types of lung disease. Overall, it was proven that our wearable stethoscope could provide a more user-friendly method and find greater application scenarios for lung disease diagnosis.