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 <UnorderedList Mark="Bullet"> <ItemContent> Advanced denoising combined with data-driven classification improves the reliability of pediatric cardiac tele-auscultation in real-world noisy conditions. </ItemContent> <ItemContent> The study provides clinical evidence that signal quality enhancement is a critical prerequisite for accurate automated murmur detection in children. </ItemContent> <ItemContent> A multi-site machine-learning approach using digital stethoscope recordings is feasible in a pediatric population. </ItemContent> <ItemContent> This approach can support primary care physicians in clinical decision-making and help reduce unnecessary referrals to pediatric cardiology specialists. </ItemContent> </UnorderedList>

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

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发表日期：

2026年8月3日

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Raffaele Malvermi,
Savina Mannarino,
Vittoria Garella,
Gioele Greco,
Giulia Fini,
Beatrice Baj,
Fabio Antonacci,
Valeria Calcaterra,
Gianvincenzo Zuccotti

摘要

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.