ReviewEntropy (Basel, Switzerland)2021
Deep Learning Methods for Heart Sounds Classification: A Systematic Review.
Review in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review.PloS one · 2026Pooled it
- A review on deep learning methods for heart sound signal analysis.Frontiers in artificial intelligence · 2024Pooled it
- [MS-DTNet: an efficient model combining multi-scale convolution and dual-tower attention for detecting abnormal heart sounds].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- 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
- 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
- Assessing arterial stiffness using characteristics of Korotkoff sounds.Frontiers in cardiovascular medicine · 2026Article
- An empirical comparison of deep CNN architectures for heart sound classification from phonocardiogram signals.Frontiers in physiology · 2026Article
- Spine Sound Classification: An Explainable Machine Learning Approach.IEEE open journal of engineering in medicine and biology · 2026Article
- Smartphone for heart sound measurement in hospital: feasibility and influencing factors.European heart journal. Digital health · 2025Article
- Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.Scientific reports · 2025Article
- Abnormal heart sound recognition using SVM and LSTM models in real-time mode.Scientific reports · 2025Article
- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.PLOS digital health · 2024Article
- Synergistic use of handcrafted and deep learning features for tomato leaf disease classification.Scientific reports · 2024Article
- Segmentation of Heart Sound Signal Based on Multi-Scale Feature Fusion and Multi-Classification of Congenital Heart Disease.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.Sensors (Basel, Switzerland) · 2024Article
- Article
- Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals.Sensors (Basel, Switzerland) · 2024Article
- Article
- A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.IEEE access : practical innovations, open solutions · 2024Article
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.