ArticleData in brief2021
A dataset of lung sounds recorded from the chest wall using an electronic stethoscope.
Article in Data in brief, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 123 citations in OpenAlex.
- Respiratory Disease Classification Using NMF-Enhanced Log-Mel Spectrograms and Convolutional Recurrent Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Wearable technologies in clinical trials for drug development: trends and emerging opportunities.Nature reviews. Drug discovery · 2026Review
- Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.BMC medical informatics and decision making · 2026Article
- LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.Scientific reports · 2026Article
- Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model.PLOS digital health · 2026Article
- Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds.Journal of medical systems · 2025Article
- Streamlining heart failure patient care with machine learning of thoracic cavity sound data.World journal of cardiology · 2025Review
- Neural network based AI model for lung health assessment.Scientific reports · 2025Article
- TriSpectraKAN: a novel approach for COPD detection via lung sound analysis.Scientific reports · 2025Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- Evaluation of Deep Learning Methods for Pulmonary Disease Classification.Current medical imaging · 2025Article
- High-accuracy lung sound classification for healthy versus unhealthy diagnosis using artificial neural network.Frontiers in bioengineering and biotechnology · 2025Article
- E-RespiNet: An LLM-ELECTRA driven triple-stream CNN with feature fusion for asthma classification.PloS one · 2025Article
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking.Medical & biological engineering & computing · 2024Article
- Current Diagnostic Techniques for Pneumonia: A Scoping Review.Sensors (Basel, Switzerland) · 2024Article
- Shortcut learning in medical AI hinders generalization: method for estimating AI model generalization without external data.NPJ digital medicine · 2024Article
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices.Tuberculosis and respiratory diseases · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advancement of stethoscope technology has enabled high quality recording of patient sounds. We used an electronic stethoscope to record lung sounds from healthy and unhealthy subjects. The dataset includes sounds from seven ailments (i.e., asthma, heart failure, pneumonia, bronchitis, pleural effusion, lung fibrosis, and chronic obstructive pulmonary disease (COPD)) as well as normal breathing sounds. The dataset presented in this article contains the audio recordings from the examination of the chest wall at various vantage points. The stethoscope placement on the subject was determined by the specialist physician performing the diagnosis. Each recording was replicated three times corresponding to various frequency filters that emphasize certain bodily sounds. The dataset can be used for the development of automated methods that detect pulmonary diseases from lung sounds or identify the correct type of lung sound. The same methods can also be applied to the study of heart sounds.
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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.