Enhancing pediatric cardiac auscultation with data-driven murmur detection: toward tele-consultation applications

BACKGROUND: Pediatric cardiac tele-auscultation is often limited by poor signal quality due to respiratory sounds, motion artifacts, and environmental noise. Distinguishing pathological from innocent murmurs remains challenging and operator-dependent, often leading to unnecessary referrals. Advanced signal processing and machine learning may improve the reliability of remote auscultation. METHODS: A total of 135 children underwent cardiac auscultation using a digital stethoscope and echocardiography; 90 patients with confirmed absence or presence of pathological murmur were included. Phonocardiograms were segmented and denoised using permutation-enhanced Non-negative Matrix Factorization. Time-frequency features were extracted and used to train Support Vector Machine classifiers for each auscultation site. RESULTS: Across five sites, specificity ranged from 70.4 to 100.0%, sensitivity from 25.0 to 75.0%, and accuracy from 71.0 to 95.7%. Specificity was ≥88.2% at all sites except the upper right sternal border. Sensitivity reached 75.0% at three sites but was lower at the apex. Combined results yielded specificity, sensitivity, and accuracy of 81.8, 66.7, and 77.4%, respectively. CONCLUSION: Improving signal quality is crucial for reliable automated murmur detection in children. The combination of advanced denoising and machine learning can enhance tele-auscultation, support primary care physicians, and reduce unnecessary referrals. IMPACT: Advanced denoising combined with data-driven classification improves the reliability of pediatric cardiac tele-auscultation in real-world noisy conditions. The study provides clinical evidence that signal quality enhancement is a critical prerequisite for accurate automated murmur detection in children. A multi-site machine-learning approach using digital stethoscope recordings is feasible in a pediatric population. This approach can support primary care physicians in clinical decision-making and help reduce unnecessary referrals to pediatric cardiology specialists.