ArticleIEEE access : practical innovations, open solutions2024
A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.
Article in IEEE access : practical innovations, open solutions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Advancing Point-of-Care Still's Murmur Identification: Evaluating the Efficacy of ConvNets and Transformers Using the StethAid Multicenter Heart Sound Database.IEEE transactions on bio-medical engineering · 2026Article
- Heart Sound Classification for Early Detection of Cardiovascular Diseases Using XGBoost and Engineered Acoustic Features.Sensors (Basel, Switzerland) · 2026Article
- Unobtrusive continuous hemodynamic monitoring method using processed heart sound signals in patients undergoing surgery: a proof of concept study.Biomedical engineering letters · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease.Sensors (Basel, Switzerland) · 2025Article
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence.Health information science and systems · 2024Article
- StethAid: A Digital Auscultation Platform for Pediatrics.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Heart sound segmentation has been shown to improve the performance of artificial intelligence (AI)-based auscultation decision support systems increasingly viewed as a solution to compensate for eroding auscultatory skills and the associated subjectivity. Various segmentation approaches with demonstrated performance can be utilized for this task, but their robustness can suffer in the presence of noise. A noise-robust heart sound segmentation algorithm was developed and its accuracy was tested using two datasets: the CirCor DigiScope Phonocardiogram dataset and an in-house dataset - a heart murmur library collected at the Children's National Hospital (CNH). On the CirCor dataset, our segmentation algorithm marked the boundaries of the primary heart sounds S1 and S2 with an accuracy of 0.28 ms and 0.29 ms, respectively, and correctly identified the actual positive segments with a sensitivity of 97.44%. The algorithm also executed four times faster than a logistic regression hidden semi-Markov model. On the CNH dataset, the algorithm succeeded in 87.4% cases, achieving a 6% increase in segmentation success rate demonstrated by our original Shannon energy-based algorithm. Accurate heart sound segmentation is critical to supporting and accelerating AI research in cardiovascular diseases. The proposed algorithm increases the robustness of heart sound segmentation to noise and viability for clinical use.
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