abstract = {Goal: Chest auscultations offer a noninvasive and low-cost tool for monitoring lung disease. However, they present many shortcomings, including inter-listener variability, subjectivity, and vulnerability to noise and distortions. This work proposes a computer-aided approach to process lung signals acquired in the field under ad- verse noisy conditions, by improving the signal quality and offering automated identification of abnormal auscultations indicative of respiratory pathologies. Methods: The developed noise-suppression scheme eliminates ambient sounds, heart sounds, sensor artifacts, and crying contamination. The improved high-quality signal is then mapped onto a rich spectrotemporal feature space before being classified using a trained support-vector machine classifier. Individual signal frame decisions are then combined using an evaluation scheme, providing an overall patient-level decision for unseen patient records. Results: All methods are evaluated on a large dataset with {\textgreater}1000 children enrolled, 1–59 months old. The noise suppression scheme is shown to significantly improve signal quality, and the classification system achieves an accuracy of 86.7{\%} in distinguishing nor- mal from pathological sounds, far surpassing other state- of-the-art methods. Conclusion: Computerized lung sound processing can benefit from the enforcement of advanced noise suppression. A fairly short processing window size (less than 1 s) combined with detailed spectrotemporal features is recommended, in order to capture transient adventitious events without highlighting sharp noise occurrences. Significance: Unlike existing methodologies in the literature, the proposed work is not limited in scope or confined to laboratory settings: This work validates a practical method for fully automated chest sound processing applicable to realistic and noisy auscultation settings.},
author = {Emmanouilidou, Dimitra and McCollum, Eric D and Park, Daniel E and Elhilali, Mounya},
doi = {10.1109/TBME.2017.2717280},
issn = {0018-9294},
journal = {IEEE Transactions on Biomedical Engineering},
number = {7},
pages = {1564--1574},
title = {{Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments}},
url = {http://ieeexplore.ieee.org/document/7953509/},
volume = {65},
year = {2018}