{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T09:02:29Z","timestamp":1785488549319,"version":"3.56.0"},"reference-count":38,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety","award":["RS-2022-00141473"],"award-info":[{"award-number":["RS-2022-00141473"]}]},{"name":"Ministry of Trade Industry & Energy","award":["RS-2024-00423853"],"award-info":[{"award-number":["RS-2024-00423853"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    This study developed a multitask learning (MTL)-based denoising model to reconstruct phonocardiogram (PCG) signals while preserving heart murmur characteristics under both synthetic and diverse real-world clinical noise.\n                    <jats:italic>Approach.<\/jats:italic>\n                    The model was trained to jointly denoise PCG data and classify valvular heart disease (VHD). The original PCG dataset comprised recordings lasting up to 3 s, including normal heart sounds and four VHD classes, with 200 samples per class. To construct inputs, we synthesized contaminated PCGs by mixing synthetic and real-world clinical noise sources and converted them into spectrogram images. The model was evaluated at signal-to-noise ratios (SNRs) of \u22125, 0, 5, and 10 dB using 5 \u00d7 5 nested cross-validation. Denoising performance was measured by the scale-invariant signal-to-distortion ratio (SI-SDR), and VHD classification by accuracy.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Our model outperformed a single-task U-Net denoising baseline; even at a highly contaminated SNR of \u22125 dB, it achieved an absolute mean SI-SDR of 14.05 dB. For real-world noise, including hospital ambient noise and lung sounds, our model improved SI-SDR by up to 9.69 dB over previous work. For VHD classification, the model exceeded 98.5% accuracy across most conditions at SNRs of 5 and 10 dB, suggesting that heart murmur-specific features were preserved during denoising.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Overall, this MTL-based model demonstrated consistent and robust PCG reconstruction across diverse noise conditions, improving VHD classification on contaminated PCG signals.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae8caa","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:56:00Z","timestamp":1784328960000},"page":"075017","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multitask learning-based phonocardiogram denoising model for preserving valvular heart disease characteristics"],"prefix":"10.1088","volume":"47","author":[{"given":"Geon","family":"Lee","sequence":"first","affiliation":[{"name":"Asan Medical Center, University of Ulsan College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-0310","authenticated-orcid":true,"given":"Hangsik","family":"Shin","sequence":"additional","affiliation":[{"name":"Asan Medical Center, University of Ulsan College of Medicine"},{"name":"University of Ulsan College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"pmeaae8caabib1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s10194-024-01915-y","type":"conference-proceedings","article-title":"SSC-UNet: UNet with self-supervised contrastive learning for phonocardiography noise reduction","author":"Abraham","year":"2025"},{"key":"pmeaae8caabib2","doi-asserted-by":"publisher","first-page":"4482","DOI":"10.1007\/s00034-017-0524-7","type":"journal-article","article-title":"Denoising of heart sound signals using discrete wavelet transform","volume":"36","author":"Ali","year":"2017","journal-title":"Circuits Syst. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-05-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-17","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-31","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}