Data

PCG Heart Sound Dataset for Cardiovascular Disease Detection

Central Queensland University
Melvin Yashnil Ramkhelawan (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25946/32249484.v1&rft.title=PCG Heart Sound Dataset for Cardiovascular Disease Detection&rft.identifier=10.25946/32249484.v1&rft.publisher=Central Queensland University&rft.description=The PhysioNet/Computing in Cardiology Challenge 2016 heart sound dataset was developed to classify heart sound recordings as normal or abnormal and is widely used as a benchmark for automated phonocardiogram analysis. The training data consist of multiple subsets, commonly referred to as databases A to E, containing 3,126 heart sound recordings. These recordings were collected from several contributing sources across clinical and non-clinical environments, including recordings from both healthy subjects and patients with cardiac pathology.&rft.creator=Melvin Yashnil Ramkhelawan&rft.date=2026&rft_rights= https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Neural networks&rft_subject=Health systems&rft_subject=Preventative health care&rft_subject=quantum machine learning&rft_subject=cardiovascular disease&rft_subject=healthcare&rft_subject=early detection&rft_subject=predictive analytics&rft_subject=PCG&rft.type=dataset&rft.language=English Access the data
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The PhysioNet/Computing in Cardiology Challenge 2016 heart sound dataset was developed to classify heart sound recordings as normal or abnormal and is widely used as a benchmark for automated phonocardiogram analysis. The training data consist of multiple subsets, commonly referred to as databases A to E, containing 3,126 heart sound recordings. These recordings were collected from several contributing sources across clinical and non-clinical environments, including recordings from both healthy subjects and patients with cardiac pathology.

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Artificial intelligence not elsewhere classified | Health systems | Neural networks | PCG | Preventative health care | cardiovascular disease | early detection | healthcare | predictive analytics | quantum machine learning |

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Identifiers
ACN 633 798 857