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Published on: April 7, 2023
Enhancing pediatric cardiac auscultation with data-driven murmur detection: toward tele-consultation applications
Raffaele Malvermi1, Savina Mannarino2, Vittoria Garella2
1Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy.
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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