Ubilung: Multi-Modal Passive-Based Lung Health Assessment
Ebrahim Nemati, Xuhai Xu, Viswam Nathan, Korosh Vatanparvar, Tousif Ahmed, Md. Mahbubur Rahman, Dan McCaffrey, Jilong Kuang
Abstract
Lung health assessment is traditionally done mainly through X-ray images and spirometry tests which are time-consuming, cumbersome, and costly. In this paper, we investigate the potential of passively recordable contents such as speech, cough and heart signal for such an assessment. Our regression model is the first in the literature to achieve mean absolute error (MAE) of 7.47% for estimation of forced expiratory volume in 1 sec. (FEV1) over forced vital capacity (FVC) ratio using these contents. This is comparable to the state of the art active phone-based spirometry methods. Additionally our classification models achieve a F1-score of 0.982 for healthy v.s. diseased, 0.881 for obstructive v.s. non-obstructive, 0.854 for chronic obstructive pulmonary disease (COPD) v.s. asthma, and 0.892 for severe v.s. non-severe obstruction classification.
BibTeX
@inproceedings{icassp2022_ubilungmultimoda,
title = {Ubilung: Multi-Modal Passive-Based Lung Health Assessment},
author = {Ebrahim Nemati and Xuhai Xu and Viswam Nathan and Korosh Vatanparvar and Tousif Ahmed and Md. Mahbubur Rahman and Dan McCaffrey and Jilong Kuang and Alex Gao},
booktitle = {ICASSP 2022},
year = {2022}
}