{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T06:35:10Z","timestamp":1793946910174,"version":"4.8.0"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009096","name":"Firat University","doi-asserted-by":"crossref","award":["MF.24.95"],"award-info":[{"award-number":["MF.24.95"]}],"id":[{"id":"10.13039\/100009096","id-type":"DOI","asserted-by":"crossref"}]},{"award":["MF.24.95"],"award-info":[{"award-number":["MF.24.95"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background\/Objectives: Cardiovascular diseases remain a leading cause of death worldwide, and early diagnosis is essential. Phonocardiogram (PCG) signals provide a non-invasive and inexpensive means of cardiac assessment. However, accurate interpretation of PCG signals requires clinical expertise. In this paper, we propose TriFusion-PCGNet, a hybrid multi-branch deep learning framework for automated heart sound classification. Methods: The proposed method was evaluated on the PhysioNet\/Computing in Cardiology Challenge 2016 dataset. Three complementary PCG representations\u2014the raw signal, band-pass filtered signal, and Hilbert envelope signal\u2014were processed through dedicated feature extraction branches using residual convolutional networks, dilated residual convolutions, and a BiGRU with a temporal attention mechanism. The extracted features were fused and classified using fully connected layers. Performance was evaluated using the original train\u2013validation split and stratified 10-fold cross-validation. Results: Under a strictly separated record-level 10-fold cross-validation protocol, TriFusion-PCGNet achieved 90.87% mean accuracy and 95.34% AUC, with outer test folds isolated from training and model selection. Subject-level analysis showed substantially lower performance, emphasizing the importance of subject-disjoint evaluation for patient-level generalization. Conclusions: TriFusion-PCGNet integrates complementary PCG representations for automated normal\/abnormal heart sound classification. However, the substantially lower subject-disjoint performance indicates that the record-level results should not be interpreted as evidence of patient-independent generalization, and further validation on independent subject-annotated datasets is required.<\/jats:p>","DOI":"10.3390\/life16091417","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T11:53:42Z","timestamp":1787745222000},"page":"1417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TriFusion-PCGNet: A Hybrid Multi-Branch Deep Learning Network for Energy-Aware Normal\/Abnormal Heart Sound Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-5521","authenticated-orcid":false,"given":"Tolga","family":"\u00c7akmak","sequence":"first","affiliation":[{"name":"Department of Cardiology, Bal\u0131kesir Atat\u00fcrk City Hospital, University of Health Sciences Turkiye, Bal\u0131kesir 10050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9956-1029","authenticated-orcid":false,"given":"\u00c7etin","family":"Mirzao\u011flu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Elazig Fethi Sekin City Hospital, University of Health Sciences, Elazig 23280, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melike","family":"Demir","sequence":"additional","affiliation":[{"name":"Eco-Informatics Department, F\u0131rat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3210-3664","authenticated-orcid":false,"given":"Fatih","family":"Demir","sequence":"additional","affiliation":[{"name":"INCISOFT Software Information Technologies, F\u0131rat Teknokent, F\u0131rat University, Elazig 23000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s11739-026-04271-8","article-title":"Global Trends and Projections in Cardiovascular Disease-Related Heart Failure, 1990\u20132050: An Analysis of the Global Burden of Disease 2021 Data","volume":"21","author":"Dong","year":"2026","journal-title":"Intern. 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