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Selection of Heart Sound Segments for Synchronous Classification of Multi-channel Heart Sounds — arXiv:2608.21499

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21499
License (as archived): CC0-1.0
Archived: 2026-10-18T07:42:17.739Z
SHA-256 of the archived text: 06d5d39fa8055ffc212af9061217379feb40fa980fb7b6b5d1b562e981d11b74
Stable URL of this page: https://metraipolis.com/documento/dc_add714df8bfc

How to cite

Selection of Heart Sound Segments for Synchronous Classification of Multi-channel Heart Sounds — arXiv:2608.21499 — arXiv (en), https://arxiv.org/abs/2608.21499 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_add714df8bfc (Metraipolis archive dc_add714df8bfc, sha256 06d5d39fa8055ffc212af9061217379feb40fa980fb7b6b5d1b562e981d11b74)

BibTeX

@misc{dcadd714df8bfc,
  title = {Selection of Heart Sound Segments for Synchronous Classification of Multi-channel Heart Sounds — arXiv:2608.21499},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21499},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_add714df8bfc; sha256 06d5d39fa8055ffc212af9061217379feb40fa980fb7b6b5d1b562e981d11b74},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21499. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cardiac auscultation remains the most cost-effective screening procedure for cardiovascular diseases, and requires listening at the four main auscultation spots. Despite this, automatic heart sound analysis algorithms mostly classify patients using a single heart sound (single-channel), or, when using more than one (multi-channel), analyze each channel individually. To our knowledge, no prior work classifies patients through the synchronous analysis of multi-channel heart sounds, following the procedure used by physicians. This motivates us to study whether synchronous multi-channel analysis outperforms single-channel approaches, and whether it holds an advantage over asynchronous multi-channel methods that analyze channels one by one, potentially by capturing inter-channel interference phenomena. To answer these questions, we introduce a selection algorithm that identifies optimal heart sound segments from each of the four auscultation spots, which are then fed into a multi-input CNN that classifies patients by analyzing the four selected sounds simultaneously. Our synchronous approach, combining the proposed selection algorithm with a multi-input CNN, achieves a superior overall accuracy of 96.5\%, a 9.1\% gain over the best-performing single-channel and asynchronous multi-channel methods. The benefit of the proposed segment selection strategy over random selection is confirmed by a paired statistical significance test ($p = 0.003$). These results were obtained on 735 patients from the CirCor DigiScope dataset with complete recordings from all four spots, and their scope and generalizability are discussed in light of this and other methodological considerations.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-25 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.21499 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T07:37:52.817Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).