Key result
BUET dataset introduces 864 annotated heart sound recordings to facilitate machine learning for cardiovascular diagnosis.
Why the study?
Cardiac auscultation often relies on the subjective interpretation of clinicians, creating limitations in consistency and accuracy.
The BMD-HS dataset provides a publicly available, multi-label annotated collection of 864 heart sound recordings to support the development of machine learning models for automated cardiac auscultation.
Answers are grounded in this paper and cite their sources.
May support ML development for automated auscultation; leaves open prospective clinical validation.
Cardiac auscultation, an integral tool in diagnosing cardiovascular diseases (CVDs), often relies on the subjective interpretation of clinicians, presenting a limitation in consistency and accuracy. Addressing this, we introduce the BUET Multi-disease Heart Sound (BMD-HS) dataset— a comprehensive and meticulously curated collection of heart sound recordings. This dataset, encompassing 864 recordings across five distinct classes of common heart sounds, is representative of a broad spectrum of valvular heart diseases, with a focus on diagnostically challenging cases. The standout feature of the BMD-HS Dataset is its innovative multi-label annotation system, which captures a diverse range of diseases and unique disease states. This system significantly enhances the dataset’s utility for developing advanced machine learning models in automated heart sound classification and diagnosis. By bridging the gap between traditional auscultation practices and contemporary data-driven diagnostic methods, the BMD-HS Dataset is poised to revolutionize CVD diagnosis and management, providing an invaluable resource for the advancement of cardiac health research. The dataset is publicly available in this link: https://github.com/mHealthBuet/BMD-HS-Dataset .
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Ali et al. (2026) studied Valvular heart diseases (n=864). BUET Multi-disease Heart Sound (BMD-HS) dataset was evaluated. The BUET Multi-disease Heart Sound dataset introduces 864 annotated heart sound recordings across five classes to facilitate the development of machine learning models for cardiovascular diagnosis.
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