SynthesisFrontiers in artificial intelligence2024
A review on deep learning methods for heart sound signal analysis.
Synthesis in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multi-feature fusion with bidirectional cross-modal attention for joint multi-task classification of heart sounds.Medical & biological engineering & computing · 2026Article
- TriFusion-PCGNet: A Hybrid Multi-Branch Deep Learning Network for Energy-Aware Normal/Abnormal Heart Sound Classification.Life (Basel, Switzerland) · 2026Article
- Acoustic Cardiography Captures Stress-Dependent Ventricular Dysfunction and Distinguishes Heart Failure Phenotypes in a Porcine Model.Biomedicines · 2026Article
- Entropy-Based Phonocardiogram Classification Using Continuous and Synchrosqueezed Wavelet Transforms: A Systematic Comparison.Diagnostics (Basel, Switzerland) · 2026Article
- DTF-STCANet: A Dual Time-Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Advancing cardiovascular screening: deep learning-based heart-sound classification using SMOTE and temporal modeling.Scientific reports · 2026Article
- Coronary artery disease diagnosis with signal processing and machine learning of heart sound signals: a systematic review.NPJ digital medicine · 2026Article
- Electronic Stethoscope Auscultation and Echocardiography in ARDS: Correlation and Prognostic Value for Mortality and ICU Length of Stay: A Prospective Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Article
- Low-latency stage-adaptive cascade architecture for real time non-stationary noise filtering.PloS one · 2026Article
- An empirical comparison of deep CNN architectures for heart sound classification from phonocardiogram signals.Frontiers in physiology · 2026Article
- Heart Sound Classification with MFCCs and Wavelet Daubechies Analysis Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable attention-based deep learning for classification and interpretation of heart murmurs using phonocardiograms.Scientific reports · 2025Article
- Unobtrusive continuous hemodynamic monitoring method using processed heart sound signals in patients undergoing surgery: a proof of concept study.Biomedical engineering letters · 2025Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Introduction: Application of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. Diversity in methodology, results, and complexity causes uncertainties in obtaining a realistic picture of the methodological performance from the reported methods. Methods: This survey paper provides the results of a broad retrospective study on the recent advances in heart sound analysis using DL methods. Representation of the results is performed according to both methodological and applicative taxonomies. The study method covers a wide span of related keywords using well-known search engines. Implementation of the observed methods along with the related results is pervasively represented and compared. Results and discussion: It is observed that convolutional neural networks and recurrent neural networks are the most commonly used ones for discriminating abnormal heart sounds and localization of heart sounds with 67.97% and 33.33% of the related papers, respectively. The convolutional neural network and the autoencoder network show a perfect accuracy of 100% in the case studies on the classification of abnormal from normal heart sounds. Nevertheless, this superiority against other methods with lower accuracy is not conclusive due to the inconsistency in evaluation.
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