Automatic Lung Health Screening Using Respiratory Sounds. Mukherjee, H., Sreerama, P., Dhar, A., Obaidullah, S. M., Roy, K., Mahmud, M., & Santosh, K. Journal of Medical Systems, 45(2):19, January, 2021. ![Automatic Lung Health Screening Using Respiratory Sounds [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{mukherjee_automatic_2021,
title = {Automatic {Lung} {Health} {Screening} {Using} {Respiratory} {Sounds}},
volume = {45},
issn = {1573-689X},
url = {https://doi.org/10.1007/s10916-020-01681-9},
doi = {10.1007/s10916-020-01681-9},
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.},
language = {en},
number = {2},
urldate = {2023-10-31},
journal = {Journal of Medical Systems},
author = {Mukherjee, Himadri and Sreerama, Priyanka and Dhar, Ankita and Obaidullah, Sk. Md. and Roy, Kaushik and Mahmud, Mufti and Santosh, K.C.},
month = jan,
year = {2021},
keywords = {Healthcare, Lung health, Respiratory infection, Respiratory sound},
pages = {19},
}
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