{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:58:42Z","timestamp":1790099922994,"version":"4.0.1"},"reference-count":73,"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-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013365","name":"Center of Excellence-International Collaboration Initiative Grant, West China Hospital, Sichuan University","doi-asserted-by":"publisher","award":["139170052"],"award-info":[{"award-number":["139170052"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004912","name":"the 1.3.5 Project for Disciplines of Excellence\u2013Clinical Research Incubation Project, West China Hospital, Sichuan University","doi-asserted-by":"publisher","award":["2019HXFH022"],"award-info":[{"award-number":["2019HXFH022"]}],"id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3325896","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T14:16:54Z","timestamp":1697725014000},"page":"117788-117799","source":"Crossref","is-referenced-by-count":43,"title":["Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5846-5319","authenticated-orcid":false,"given":"Xue","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Pancreatic Surgery, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejun","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Xihua University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7191-6495","authenticated-orcid":false,"given":"Xiaobo","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10062-8"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref12","first-page":"205","article-title":"Anomaly detection in electroencephalography signal using deep learning model","author":"tahura","year":"2021","journal-title":"Proc Int Conf Trends Comput Cognit Eng (TCCE)"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3114119"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2901672"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106542"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2984238"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101568"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33778-0_33"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICABME47164.2019.8940307"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101789"},{"key":"ref55","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume":"33","author":"tian","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30107-2"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM49941.2020.9313163"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3689-5"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403289"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991456"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac40a0"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.026"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403092"},{"key":"ref46","first-page":"6074","article-title":"Semisupervised anomaly detection of multivariate time series based on a variational autoencoder","volume":"53","author":"chen","year":"2023","journal-title":"Appl Intell"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103228"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105472"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3100509"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3078192"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.110791"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105964"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbsr.2021.100398"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2021.3137646"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"455","DOI":"10.34768\/amcs-2022-0033","article-title":"A hybrid approach of a deep learning technique for real&#x2013;time ECG beat detection","volume":"32","author":"patro","year":"2022","journal-title":"Int J Appl Math Comput Sci"},{"key":"ref4","article-title":"Deep learning for time series anomaly detection: A survey","author":"darban","year":"2022","journal-title":"arXiv 2211 05244"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3107975"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3159583"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.10.008"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2020.102282"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3140058"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09743-2"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103957"},{"key":"ref36","first-page":"83","article-title":"Medical time series classification with hierarchical attention-based temporal convolutional networks: A case study of myotonic dystrophy diagnosis","author":"lin","year":"2019","journal-title":"Proc CVPR Workshops"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/app11157149"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2018.02.003"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2995193"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/s22176634"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3439950"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3230361"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3164087"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3077803"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2975227"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115736"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104393"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-020-00503-2"},{"key":"ref68","first-page":"1","article-title":"Incremental learning algorithms and applications","author":"gepperth","year":"2016","journal-title":"Proc Eur Symp Artif Neural Netw (ESANN)"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCEP.119.007988","article-title":"Assessing and mitigating bias in medical artificial intelligence: The effects of race and ethnicity on a deep learning model for ECG analysis","volume":"13","author":"noseworthy","year":"2020","journal-title":"Circulat Arrhythmia Electrophysiol"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00689-4"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review","volume":"25","author":"xiao","year":"2018","journal-title":"J Amer Med Inform Assoc"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1108726"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.02.023"},{"key":"ref20","first-page":"1283","article-title":"Sleeping abnormalities detection using deep learning techniques","author":"murugesan","year":"2023","journal-title":"Proc 2nd Int Conf Electron Renew Syst (ICEARS)"},{"key":"ref64","article-title":"Sleep-EDF database expanded","author":"kemp","year":"2018"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/51.932728"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"15697","DOI":"10.1109\/JIOT.2022.3150147","article-title":"Graph-based denoising for respiration and heart rate estimation during sleep in thermal video","volume":"9","author":"yang","year":"2022","journal-title":"IEEE Internet of Things Journal"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-019-00062-3"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2019.2957977"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02696-6"},{"key":"ref28","article-title":"Finding the smoke signal: Smoking status classification with a weakly supervised paradigm in sparsely labelled Dutch free text in electronic medical records","author":"reuver","year":"2020"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1038\/s41596-019-0227-6","article-title":"High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)","volume":"14","author":"zhang","year":"2019","journal-title":"Nature Protocols"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.04.002"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref62","first-page":"1","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"johnson","year":"2016","journal-title":"Data Science Journal"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3280931"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10287948.pdf?arnumber=10287948","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T01:33:34Z","timestamp":1730338414000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10287948\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3325896","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}