Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments

<italic>Goal:</italic> Chest auscultations offer a non-invasive 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 adverse noisy conditions, by improving the signal quality and offering automated identification of abnormal auscultations indicative of respiratory pathologies. <italic>Methods:</italic> 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. <italic>Results:</italic> All methods are evaluated on a large dataset with <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>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 normal from pathological sounds, far surpassing other state-of-the-art methods. <italic>Conclusion:</italic> Computerized lung sound processing can benefit from the enforcement of advanced noise suppression. A fairly short processing window size (<inline-formula> <tex-math notation="LaTeX">$<1$</tex-math></inline-formula> s) combined with detailed spectrotemporal features is recommended, in order to capture transient adventitious events without highlighting sharp noise occurrences. <italic>Significance:</italic> 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.