Automatic lung health screening using respiratory sounds

Mukherjee, H, Sreerama, P, Dhar, A, Obaidullah, SM, Roy, K, Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348 and Santosh, KC, 2021. Automatic lung health screening using respiratory sounds. Journal of Medical Systems, 45 (2): 19. ISSN 0148-5598

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Abstract

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.

Item Type:	Journal article
Publication Title:	Journal of Medical Systems
Creators:	Mukherjee, H., Sreerama, P., Dhar, A., Obaidullah, S.M., Roy, K., Mahmud, M. and Santosh, K.C.
Publisher:	Springer Science and Business Media LLC
Date:	February 2021
Volume:	45
Number:	2
ISSN:	0148-5598
Identifiers:	Number Type 10.1007/s10916-020-01681-9 DOI 1399311 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	20 Jan 2021 12:31
Last Modified:	11 Jan 2022 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/42071

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