Automatic Lung Health Screening Using Respiratory Sounds

AbstractSignificant changes have been made on audio-based technologies over years in several different fields. Healthcare is no exception. One of such avenues is health screening based on respiratory sounds. In this paper, we developed a tool to detect respiratory sounds that come from respiratory infection carrying patients. Linear Predictive Cepstral Coefficient (LPCC)-based features were used to characterize such audio clips. With Multilayer Perceptron (MLP)-based classifier, in our experiment, we achieved the highest possible accuracy of 99.22% that was tested on a publicly available respiratory sounds dataset (ICBHI17) (Rocha et al.Physiol. Meas. 40(3):035,00120) of size 6800+ clips. In addition to other popular machine learning classifiers, our results outperformed common works that exist in the literature.
Source: Journal of Medical Systems - Category: Information Technology Source Type: research