{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:37:07Z","timestamp":1774629427886,"version":"3.50.1"},"reference-count":203,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"European Union (EU)-NextGenerationEU Program"},{"name":"Stars@UNIPD-Project: MedMax"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3344531","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T19:49:47Z","timestamp":1702928987000},"page":"144180-144203","source":"Crossref","is-referenced-by-count":24,"title":["Applications of Self-Supervised Learning to Biomedical Signals: A Survey"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0698-962X","authenticated-orcid":false,"given":"Federico Del","family":"Pup","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padua, Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-2063","authenticated-orcid":false,"given":"Manfredo","family":"Atzori","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, University of Padua, Padua, Italy"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref3","article-title":"Mastering chess and shogi by self-play with a general reinforcement learning algorithm","author":"Silver","year":"2017","journal-title":"arXiv:1712.01815"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baq036"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2020.06.040"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106874"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9175634"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2018.7117"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0217-0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103982"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3207050"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3057023"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/s23094221"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100198"},{"key":"ref17","first-page":"18158","article-title":"3D self-supervised methods for medical imaging","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Taleb"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1045"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5815\/ijigsp.2021.04.03"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2871638"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3190448"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0495-6"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/BioRob.2012.6290287"},{"key":"ref25","volume-title":"Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation-A MATLAB-Based Approach","author":"Bansal","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.2196\/18907"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2015.0417"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-88880-4"},{"key":"ref29","volume-title":"Electroencephalography: Basic Principles, Clinical Applications, and Related Fields","author":"Niedermeyer","year":"2005"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.53"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1177\/2055668318773991"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.607471"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100383"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICASS.2018.8651980"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3134634"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.9790\/3021-0204719725"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.4236\/jsip.2013.43B031"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0202"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_46"},{"key":"ref42","article-title":"Beyond just vision: A review on self-supervised representation learning on multimodal and temporal data","author":"Deldari","year":"2022","journal-title":"arXiv:2206.02353"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-71278-5_6"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16452-1_19"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC46164.2021.9630364"},{"key":"ref46","article-title":"A survey on masked autoencoder for self-supervised learning in vision and beyond","author":"Zhang","year":"2022","journal-title":"arXiv:2208.00173"},{"key":"ref47","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Lang. Technol.","volume":"1","author":"Devlin"},{"key":"ref48","article-title":"Autoencoders","author":"Bank","year":"2020","journal-title":"arXiv:2003.05991"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053541"},{"key":"ref52","article-title":"Improving self-supervised pretraining models for epileptic seizure detection from EEG data","author":"Das","year":"2022","journal-title":"arXiv:2207.06911"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3170369"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref55","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML","volume":"2","author":"Koch"},{"key":"ref56","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref57","first-page":"297","article-title":"Noise-contrastive estimation: A new estimation principle for unnormalized statistical models","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Gutmann"},{"key":"ref58","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref60","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04297"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref62","first-page":"21271","article-title":"Bootstrap your own latent\u2014A new approach to self-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Grill"},{"key":"ref63","article-title":"Understanding dimensional collapse in contrastive self-supervised learning","author":"Jing","year":"2021","journal-title":"arXiv:2110.09348"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref65","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Caron"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref67","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zbontar"},{"key":"ref68","article-title":"VICReg: Variance-invariance-covariance regularization for self-supervised learning","author":"Bardes","year":"2021","journal-title":"arXiv:2105.04906"},{"key":"ref69","first-page":"3015","article-title":"Whitening for self-supervised representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ermolov"},{"key":"ref70","article-title":"Unsupervised representation learning for time series with temporal neighborhood coding","author":"Tonekaboni","year":"2021","journal-title":"arXiv:2106.00750"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref73","article-title":"A cookbook of self-supervised learning","author":"Balestriero","year":"2023","journal-title":"arXiv:2304.12210"},{"key":"ref74","article-title":"Subject-aware contrastive learning for biosignals","author":"Cheng","year":"2020","journal-title":"arXiv:2007.04871"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3331506"},{"key":"ref76","article-title":"Self-supervised contrastive pre-training for time series via time-frequency consistency","author":"Zhang","year":"2022","journal-title":"arXiv:2206.08496"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2022.02.007"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2018.2442"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0386-x"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC46164.2021.9630616"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2017.065-469"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3114119"},{"key":"ref83","first-page":"5606","article-title":"CLOCS: Contrastive learning of cardiac signals across space, time, and patients","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kiyasseh"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.22489\/cinc.2018.049"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/abc960"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746887"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20376"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/BHI56158.2022.9926925"},{"key":"ref91","article-title":"Classification and self-supervised regression of arrhythmic ecg signals using convolutional neural networks","author":"Grabowski","year":"2022","journal-title":"arXiv:2210.14253"},{"key":"ref92","first-page":"338","article-title":"Lead-agnostic self-supervised learning for local and global representations of electrocardiogram","volume-title":"Proc. Conf. Health, Inference, Learn.","author":"Oh"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.23919\/CinC53138.2021.9662687"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104194"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053985"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1145\/2663204.2663257"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2018.2884461"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-03376-8"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2688239"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1145\/3242969.3242985"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956027"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2016.2625250"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering9080374"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.23919\/CinC53138.2021.9662748"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15432-4"},{"key":"ref106","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":"ref107","article-title":"Computer vision self-supervised learning methods on time series","author":"Lee","year":"2021","journal-title":"arXiv:2109.00783"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105114"},{"key":"ref109","first-page":"1","article-title":"Self-supervised contrastive learning for electrocardiograms to detect left ventricular systolic dysfunction","volume-title":"Proc. Annu. Conf. JSAI","author":"Nakamoto"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892600"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ICIST55546.2022.9926900"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106390"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59821-7"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-39472-8"},{"key":"ref115","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Baevski"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ac6049"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1016\/j.seizure.2016.06.008"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3130826"},{"key":"ref119","first-page":"130","article-title":"Domain-guided self-supervision of EEG data improves downstream classification performance and generalizability","volume-title":"Proc. Mach. Learn. Health","author":"Wagh"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5649253"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533305"},{"key":"ref122","article-title":"Self-supervised EEG representation learning for automatic sleep staging","author":"Yang","year":"2021","journal-title":"arXiv:2110.15278"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414752"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00186"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/537"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/10.867928"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM49941.2020.9313163"},{"key":"ref129","article-title":"Self-supervised graph neural networks for improved electroencephalographic seizure analysis","author":"Tang","year":"2021","journal-title":"arXiv:2104.08336"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.220374"},{"key":"ref131","first-page":"238","article-title":"Contrastive representation learning for electroencephalogram classification","volume-title":"Proc. Mach. Learn. Health","author":"Mohsenvand"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac5c8d"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00196"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abca18"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.653659"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.308"},{"key":"ref137","article-title":"Application of machine learning to epileptic seizure onset detection and treatment","author":"Shoeb","year":"2009"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2015.7319901"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/ICCT52962.2021.9657922"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04971-0"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/T-AFFC.2011.15"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3164516"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118819"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956291"},{"key":"ref145","volume-title":"BCI competition 2008\u2014Graz data set A","author":"Brunner","year":"2008"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3199363"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0535-2"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2022325"},{"key":"ref149","volume-title":"Bci competition 2008\u2014Graz data set B","author":"Leeb","year":"2008"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac8b38"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1111\/j.1528-1167.2012.03564.x"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2020.3011181"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy064"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy131"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2015.10.013"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2012.00055"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-007-0763-z"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3197419"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3223600"},{"key":"ref160","article-title":"Neuro2vec: Masked Fourier spectrum prediction for neurophysiological representation learning","author":"Wu","year":"2022","journal-title":"arXiv:2204.12440"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1145\/3328932"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.110112.00192"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3004686"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2022.298"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.3390\/app7101101"},{"key":"ref167","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume-title":"Proc. ESANN","volume":"3","author":"Anguita"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-62704-5_7"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1145\/3195258.3195260"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2016.179-154"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000324"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1145\/3450439.3451863"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1145\/3550316"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100152"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00536-y"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-86891-y"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.07.085"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2005.848368"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700082"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2021.720464"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/CYBConf.2013.6617456"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59259-6_11"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.912798"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871056"},{"key":"ref188","volume-title":"St. Vincent\u2019s University Hospital\/University College Dublin sleep apnea database","author":"Heneghan","year":"2008"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-9457(02)00003-5"},{"key":"ref190","first-page":"152","article-title":"Learning unsupervised representations for ICU timeseries","volume-title":"Proc. Conf. Health, Inference, Learn.","author":"Weatherhead"},{"key":"ref191","volume-title":"HiRID, a high time-resolution ICU dataset (version 1.1. 1)","volume":"10","author":"Faltys","year":"2021"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1145\/3516367"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref194","first-page":"245","article-title":"Predicting in-hospital mortality of ICU patients: The physionet\/computing in cardiology challenge 2012","volume-title":"Proc. Comput. Cardiology","author":"Silva"},{"key":"ref195","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1515\/bmt-2013-4182"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.5334\/jors.86"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3117407"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1002\/humu.22080"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00635-4"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104163"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445923"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.3013489"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10365170.pdf?arnumber=10365170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T19:58:28Z","timestamp":1705089508000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10365170\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":203,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3344531","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}