{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:59:42Z","timestamp":1783526382978,"version":"3.55.0"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"European Union under Grant","award":["101057821"],"award-info":[{"award-number":["101057821"]}]},{"name":"European Union or the granting authority (HaDEA). Neither the European Union nor the granting authority can be held responsible for them."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3519297","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T19:21:58Z","timestamp":1734463318000},"page":"193459-193472","source":"Crossref","is-referenced-by-count":1,"title":["Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1683-8433","authenticated-orcid":false,"given":"Aristotelis","family":"Ballas","sequence":"first","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University, Tavros, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6834-5548","authenticated-orcid":false,"given":"Vasileios","family":"Papapanagiotou","sequence":"additional","affiliation":[{"name":"Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2461-1928","authenticated-orcid":false,"given":"Christos","family":"Diou","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University, Tavros, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32203-7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2013-303896"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab8770"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3306253"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41102-8"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8857681"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s21217246"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-7615-5_65"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2022.3179633"},{"key":"ref10","first-page":"5389","article-title":"Do ImageNet classifiers generalize to ImageNet","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Recht"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00270"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/37\/12\/2181"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000324"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e21"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.22489\/cinc.2022.165"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.065"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.224"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.249"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.439"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3275039"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.035"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.072"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.310"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.298"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3134634"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref30","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-022-00245-6"},{"key":"ref33","first-page":"29848","article-title":"Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Zhang","year":"2021"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3190448"},{"key":"ref35","first-page":"238","article-title":"Contrastive representation learning for electroencephalogram classification","volume-title":"Proc. Mach. Learn. Health NeurIPS Workshop","author":"Mohsenvand"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2019.8918693"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abca18"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.653659"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105114"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.3014842"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956027"},{"key":"ref43","first-page":"5606","article-title":"CLOCS: Contrastive learning of cardiac signals across space, time, and patients","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Kiyasseh"},{"key":"ref44","first-page":"338","article-title":"Lead-agnostic self-supervised learning for local and global representations of electrocardiogram","volume-title":"Proc. Conf. Health, Inference, Learn., in Proceedings of Machine Learning Research","volume":"174","author":"Oh"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3114119"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20376"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2021.1003865"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3223600"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/IJCB54206.2022.10008005"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3377173"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-84374-8"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119711"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.017"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3241846"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3010780"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103555"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2018.2826840"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2020.3045720"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2020.445"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-36708-4_3"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataService55688.2022.00009"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref67","article-title":"EPHNOGRAM: A simultaneous electrocardiogram and phonocardiogram database","author":"Kazemnejad","year":"2021","journal-title":"PhysioNet"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.11.008"},{"key":"ref69","volume-title":"The PASCAL Classifying Heart Sounds Challenge 2011 (CHSC2011) Results","author":"Bentley","year":"2023"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8037060"},{"key":"ref72","article-title":"Large batch training of convolutional networks with layer-wise adaptive rate scaling","volume-title":"Proc. ICLR","author":"Ginsburg"},{"issue":"1","key":"ref73","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref74","volume-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","author":"Abadi","year":"2015"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/10968987_3"},{"key":"ref76","first-page":"156","article-title":"3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations","volume-title":"Proc. Mach. Learn. Health","author":"Gopal"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.81.2.730"},{"key":"ref78","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/B978-012437552-9\/50003-9","article-title":"EEG signal processing","volume-title":"Bioelectrical Signal Processing in Cardiac and Neurological Applications","author":"S\u00f6rnmo","year":"2005"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3320668"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10804781.pdf?arnumber=10804781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T05:19:24Z","timestamp":1735276764000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10804781\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":79,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3519297","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}