Automatic Lung Health Screening Using Respiratory Sounds

• Published in: Journal of medical systems Vol. 45; no. 2; p. 19
• Main Authors: Mukherjee, Himadri, Sreerama, Priyanka, Dhar, Ankita, Obaidullah, Sk. Md, Roy, Kaushik, Mahmud, Mufti, Santosh, K.C.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2021; Springer Nature B.V
• Subjects: Acoustics; Classifiers; Clips; Health Informatics; Health Sciences; Image & Signal Processing; Learning algorithms; Machine learning; Medical screening; Medicine; Medicine & Public Health; Multilayer perceptrons; Statistics for Life Sciences; Lung health; Healthcare; Respiratory infection; Respiratory sound
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-020-01681-9

Significant 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):035,001 20 ) of size 6800+ clips. In addition to other popular machine learning classifiers, our results outperformed common works that exist in the literature.