Heart Sound Segmentation Using Deep Learning Techniques
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911910740361216 |
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| author | Madine, Manas |
| author_facet | Madine, Manas |
| contents | Heart disease remains a leading cause of mortality worldwide. Auscultation, the process of listening to heart sounds, can be enhanced through computer-aided analysis using Phonocardiogram (PCG) signals. This paper presents a novel approach for heart sound segmentation and classification into S1 (LUB) and S2 (DUB) sounds. We employ FFT-based filtering, dynamic programming for event detection, and a Siamese network for robust classification. Our method demonstrates superior performance on the PASCAL heart sound dataset compared to existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Heart Sound Segmentation Using Deep Learning Techniques Madine, Manas Sound Artificial Intelligence Audio and Speech Processing Heart disease remains a leading cause of mortality worldwide. Auscultation, the process of listening to heart sounds, can be enhanced through computer-aided analysis using Phonocardiogram (PCG) signals. This paper presents a novel approach for heart sound segmentation and classification into S1 (LUB) and S2 (DUB) sounds. We employ FFT-based filtering, dynamic programming for event detection, and a Siamese network for robust classification. Our method demonstrates superior performance on the PASCAL heart sound dataset compared to existing approaches. |
| title | Heart Sound Segmentation Using Deep Learning Techniques |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.05653 |