Key result
Automated murmur detection identifies pediatric pathological murmurs with ~77% accuracy.
Why the study?
Pediatric cardiac tele-auscultation is limited by poor signal quality, and distinguishing pathological from innocent murmurs remains challenging and operator-dependent, often leading to unnecessary referrals.
Does advanced denoising and machine learning classification of digital stethoscope recordings improve the detection of pathological murmurs in children?
Cross-Sectional (n=90)
No
Does advanced denoising and machine learning classification of digital stethoscope recordings improve the detection of pathological murmurs in children?
Advanced denoising combined with machine learning classification of digital stethoscope recordings achieves 77.4% accuracy in detecting pathological murmurs in children, demonstrating feasibility for supporting primary care tele-consultation.
Answers are grounded in this paper and cite their sources.
May support pediatric tele-auscultation screening; leaves open prospective validation before clinical adoption.
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.
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Malvermi et al. (2026) conducted a cross-sectional in Pediatric cardiac murmurs (n=90). Automated murmur detection using NMF denoising and SVM classifiers vs. Echocardiography and expert clinical auscultation was evaluated on Combined diagnostic accuracy for pathological murmur detection across five auscultation sites. An automated murmur detection system using advanced denoising and machine learning achieved a combined specificity of 81.8%, sensitivity of 66.7%, and accuracy of 77.4% in pediatric patients.
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