Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2409.00724 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914932069498880 |
|---|---|
| author | Ali, Shams Nafisa Zahin, Afia Shuvo, Samiul Based Nizam, Nusrat Binta Nuhash, Shoyad Ibn Sabur Khan Razin, Sayeed Sajjad Sani, S. M. Sakeef Rahman, Farihin Nizam, Nawshad Binta Azam, Farhat Binte Hossen, Rakib Ohab, Sumaiya Noor, Nawsabah Hasan, Taufiq |
| author_facet | Ali, Shams Nafisa Zahin, Afia Shuvo, Samiul Based Nizam, Nusrat Binta Nuhash, Shoyad Ibn Sabur Khan Razin, Sayeed Sajjad Sani, S. M. Sakeef Rahman, Farihin Nizam, Nawshad Binta Azam, Farhat Binte Hossen, Rakib Ohab, Sumaiya Noor, Nawsabah Hasan, Taufiq |
| contents | 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, represents 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 at this link: https://github.com/mHealthBuet/BMD-HS-Dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BUET Multi-disease Heart Sound Dataset: A Comprehensive Auscultation Dataset for Developing Computer-Aided Diagnostic Systems Ali, Shams Nafisa Zahin, Afia Shuvo, Samiul Based Nizam, Nusrat Binta Nuhash, Shoyad Ibn Sabur Khan Razin, Sayeed Sajjad Sani, S. M. Sakeef Rahman, Farihin Nizam, Nawshad Binta Azam, Farhat Binte Hossen, Rakib Ohab, Sumaiya Noor, Nawsabah Hasan, Taufiq Signal Processing Artificial Intelligence Machine Learning Sound Audio and Speech Processing 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, represents 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 at this link: https://github.com/mHealthBuet/BMD-HS-Dataset. |
| title | BUET Multi-disease Heart Sound Dataset: A Comprehensive Auscultation Dataset for Developing Computer-Aided Diagnostic Systems |
| topic | Signal Processing Artificial Intelligence Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.00724 |