ArticleIEEE journal of biomedical and health informatics2022
The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 189 citations in OpenAlex.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- TriFusion-PCGNet: A Hybrid Multi-Branch Deep Learning Network for Energy-Aware Normal/Abnormal Heart Sound Classification.Life (Basel, Switzerland) · 2026Article
- HSBP-DB: A synchronised phonocardiography and arterial blood pressure dataset for multi-class hypertension staging under resting and exercise conditions.Data in brief · 2026Article
- Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection.IEEE transactions on bio-medical engineering · 2026Article
- DTF-STCANet: A Dual Time-Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification.Diagnostics (Basel, Switzerland) · 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
- Article
- Prior-aligned frequency-domain explanations for heart sound classification: a scale-consistent attribution approach.Frontiers in artificial intelligence · 2026Article
- An empirical comparison of deep CNN architectures for heart sound classification from phonocardiogram signals.Frontiers in physiology · 2026Article
- Development and validation of AI-Enhanced auscultation for valvular heart disease screening through a multi-centre study.NPJ cardiovascular health · 2026Article
- Explainable attention-based deep learning for classification and interpretation of heart murmurs using phonocardiograms.Scientific reports · 2025Article
- Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Bimodal ECG and PCG Cardiovascular Disease Detection: Exploring the Potential and Modality Contribution.Journal of medical systems · 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
- Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.Scientific reports · 2025Article
- Early heart disease prediction using LV-PSO and Fuzzy Inference Xception Convolution Neural Network on phonocardiogram signals.Frontiers in neuroinformatics · 2025Article
- Article
- Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence.Health information science and systems · 2024Article
- 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
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 4 countries.
Funding
Abstract
Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.
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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.