Automatic lung health screening using respiratory sounds

Mukherjee, H., Sreerama, P., Dhar, A., Obaidullah, S.M., Roy, K., Mahmud, M. ORCID: 0000-0002-2037-8348 and Santosh, K.C., 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:
NumberType
10.1007/s10916-020-01681-9DOI
1399311Other
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 20 Jan 2021 12:31
Last Modified: 20 Jan 2021 12:31
URI: http://irep.ntu.ac.uk/id/eprint/42071

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