{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:09Z","timestamp":1740108069468,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T00:00:00Z","timestamp":1702080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T00:00:00Z","timestamp":1702080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102008"],"award-info":[{"award-number":["62102008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09267-5","type":"journal-article","created":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T15:02:11Z","timestamp":1702134131000},"page":"4047-4058","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardiac arrhythmia classification with rejection of ECG recordings based on uncertainty estimation from deep neural networks"],"prefix":"10.1007","volume":"36","author":[{"given":"Wenrui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Di","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijia","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoji","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenda","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,9]]},"reference":[{"key":"9267_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103801","volume":"122","author":"S Hong","year":"2020","unstructured":"Hong S, Zhou Y, Shang J, Xiao C, Sun J (2020) Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med 122:103801","journal-title":"Comput Biol Med"},{"key":"9267_CR2","doi-asserted-by":"crossref","unstructured":"Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y (2021) Meeting the unmet needs of clinicians from ai systems showcased for cardiology with deep-learning\u2013based ecg analysis. In: Proceedings of the National Academy of Sciences 118(24)","DOI":"10.1073\/pnas.2020620118"},{"issue":"10","key":"9267_CR3","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.119.015138","volume":"9","author":"RR van de Leur","year":"2020","unstructured":"van de Leur RR, Blom LJ, Gavves E, Hof IE, van der Heijden JF, Clappers NC, Doevendans PA, Hassink RJ, van Es R (2020) Automatic triage of 12-lead ecgs using deep convolutional neural networks. J Am Heart Assoc 9(10):015138","journal-title":"J Am Heart Assoc"},{"issue":"1","key":"9267_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-13993-7","volume":"11","author":"AH Ribeiro","year":"2020","unstructured":"Ribeiro AH, Ribeiro MH, Paix\u00e3o GM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MP, Andersson CR, Macfarlane PW, Wagner M Jr (2020) Automatic diagnosis of the 12-lead ecg using a deep neural network. Nat Commun 11(1):1\u20139","journal-title":"Nat Commun"},{"key":"9267_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jelectrocard.2019.08.004","volume":"57","author":"S Parvaneh","year":"2019","unstructured":"Parvaneh S, Rubin J, Babaeizadeh S, Xu-Wilson M (2019) Cardiac arrhythmia detection using deep learning: a review. J Electrocardiol 57:70\u201374. https:\/\/doi.org\/10.1016\/j.jelectrocard.2019.08.004","journal-title":"J Electrocardiol"},{"key":"9267_CR6","doi-asserted-by":"crossref","unstructured":"Clifford GD, Liu C, Moody B, Li-wei HL, Silva I, Li Q, Johnson A, Mark RG (2017) Af classification from a short single lead ecg recording: the physionet\/computing in cardiology challenge 2017. In: 2017 Computing in Cardiology (CinC), pp 1\u20134. IEEE","DOI":"10.22489\/CinC.2017.065-469"},{"key":"9267_CR7","doi-asserted-by":"crossref","unstructured":"Hong S, Fu Z, Zhou R, Yu J, Li Y, Wang K, Cheng G (2020) Cardiolearn: A cloud deep learning service for cardiac disease detection from electrocardiogram. In: Companion proceedings of the web conference 2020, pp 148\u2013152","DOI":"10.1145\/3366424.3383529"},{"issue":"5","key":"9267_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab15a2","volume":"40","author":"S Hong","year":"2019","unstructured":"Hong S, Zhou Y, Wu M, Shang J, Wang Q, Li H, Xie J (2019) Combining deep neural networks and engineered features for cardiac arrhythmia detection from ecg recordings. Physiol Measure 40(5):054009","journal-title":"Physiol Measure"},{"key":"9267_CR9","doi-asserted-by":"crossref","unstructured":"Hong S, Xiao C, Ma T, Li H, Sun J (2019) Mina: multilevel knowledge-guided attention for modeling electrocardiography signals. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 5888\u20135894. AAAI Press","DOI":"10.24963\/ijcai.2019\/816"},{"key":"9267_CR10","doi-asserted-by":"crossref","unstructured":"Zhou Y, Hong S, Shang J, Wu M, Wang Q, Li H, Xie J (2019) K-margin-based residual-convolution-recurrent neural network for atrial fibrillation detection. In: IJCAI","DOI":"10.24963\/ijcai.2019\/839"},{"issue":"10201","key":"9267_CR11","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","volume":"394","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ (2019) An artificial intelligence-enabled ecg algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394(10201):861\u2013867","journal-title":"Lancet"},{"key":"9267_CR12","doi-asserted-by":"crossref","unstructured":"Raghunath S, Cerna AEU, Jing L, Stough J, Hartzel DN, Leader JB, Kirchner HL, Stumpe MC, Hafez A, Nemani A, et al (2020) Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med, pp 1\u20136","DOI":"10.1038\/s41591-020-0870-z"},{"key":"9267_CR13","doi-asserted-by":"crossref","unstructured":"Hong S, Xu Y, Khare A, Priambada S, Maher K, Aljiffry A, Sun J, Tumanov A (2020) Holmes: health online model ensemble serving for deep learning models in intensive care units. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1614\u20131624","DOI":"10.1145\/3394486.3403212"},{"key":"9267_CR14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","volume":"294","author":"K Li","year":"2018","unstructured":"Li K, Pan W, Li Y, Jiang Q, Liu G (2018) A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ecg signal. Neurocomputing 294:94\u2013101","journal-title":"Neurocomputing"},{"key":"9267_CR15","doi-asserted-by":"crossref","unstructured":"Sun C, Hong S, Wang J, Dong X, Han F, Li H (2022) A systematic review of deep learning methods for modeling electrocardiograms during sleep. Physiol Measure","DOI":"10.1088\/1361-6579\/ac826e"},{"key":"9267_CR16","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.patrec.2018.03.028","volume":"126","author":"RD Labati","year":"2019","unstructured":"Labati RD, Mu\u00f1oz E, Piuri V, Sassi R, Scotti F (2019) Deep-ecg: convolutional neural networks for ecg biometric recognition. Pattern Recogn Lett 126:78\u201385","journal-title":"Pattern Recogn Lett"},{"key":"9267_CR17","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2020.05.099","volume":"412","author":"S Hong","year":"2020","unstructured":"Hong S, Wang C, Fu Z (2020) Cardioid: learning to identification from electrocardiogram data. Neurocomputing 412:11\u201318","journal-title":"Neurocomputing"},{"issue":"7","key":"9267_CR18","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","volume":"18","author":"KC Siontis","year":"2021","unstructured":"Siontis KC, Noseworthy PA, Attia ZI, Friedman PA (2021) Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol 18(7):465\u2013478","journal-title":"Nat Rev Cardiol"},{"issue":"3","key":"9267_CR19","doi-asserted-by":"publisher","first-page":"773","DOI":"10.3390\/s21030773","volume":"21","author":"Z Fu","year":"2021","unstructured":"Fu Z, Hong S, Zhang R, Du S (2021) Artificial-intelligence-enhanced mobile system for cardiovascular health management. Sensors 21(3):773","journal-title":"Sensors"},{"issue":"1","key":"9267_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24\u201329","journal-title":"Nat Med"},{"issue":"1","key":"9267_CR21","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","volume":"25","author":"AY Hannun","year":"2019","unstructured":"Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY (2019) Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 25(1):65\u201369","journal-title":"Nat Med"},{"issue":"2","key":"9267_CR22","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.amjmed.2018.08.025","volume":"132","author":"H Smulyan","year":"2019","unstructured":"Smulyan H (2019) The computerized ecg: friend and foe. Am J Med 132(2):153\u2013160","journal-title":"Am J Med"},{"key":"9267_CR23","doi-asserted-by":"crossref","unstructured":"Musa N, Gital AY, Aljojo N, Chiroma H, Adewole KS, Mojeed HA, Faruk N, Abdulkarim A, Emmanuel I, Folawiyo YY, et al (2022) A systematic review and meta-data analysis on the applications of deep learning in electrocardiogram. J Ambient Intell Human Comput, pp 1\u201374","DOI":"10.1007\/s12652-022-03868-z"},{"issue":"8","key":"9267_CR24","doi-asserted-by":"publisher","first-page":"38454","DOI":"10.2196\/38454","volume":"10","author":"G Petmezas","year":"2022","unstructured":"Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N (2022) State-of-the-art deep learning methods on electrocardiogram data: systematic review. JMIR Med Inf 10(8):38454","journal-title":"JMIR Med Inf"},{"issue":"1","key":"9267_CR25","doi-asserted-by":"publisher","first-page":"32939","DOI":"10.2196\/32939","volume":"24","author":"HSJ Chew","year":"2022","unstructured":"Chew HSJ, Achananuparp P (2022) Perceptions and needs of artificial intelligence in health care to increase adoption: scoping review. J Med Internet Res 24(1):32939","journal-title":"J Med Internet Res"},{"issue":"8","key":"9267_CR26","doi-asserted-by":"publisher","first-page":"0000085","DOI":"10.1371\/journal.pdig.0000085","volume":"1","author":"TJ Loftus","year":"2022","unstructured":"Loftus TJ, Shickel B, Ruppert MM, Balch JA, Ozrazgat-Baslanti T, Tighe PJ, Efron PA, Hogan WR, Rashidi P, Upchurch GR Jr (2022) Uncertainty-aware deep learning in healthcare: a scoping review. PLOS Digital Health 1(8):0000085","journal-title":"PLOS Digital Health"},{"issue":"1","key":"9267_CR27","doi-asserted-by":"publisher","first-page":"19","DOI":"10.4258\/hir.2021.27.1.19","volume":"27","author":"J-H Jang","year":"2021","unstructured":"Jang J-H, Kim TY, Yoon D (2021) Effectiveness of transfer learning for deep learning-based electrocardiogram analysis. Healthcare Inf Res 27(1):19\u201328","journal-title":"Healthcare Inf Res"},{"issue":"6","key":"9267_CR28","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.jelectrocard.2018.08.007","volume":"51","author":"RR Bond","year":"2018","unstructured":"Bond RR, Novotny T, Andrsova I, Koc L, Sisakova M, Finlay D, Guldenring D, McLaughlin J, Peace A, McGilligan V (2018) Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol 51(6):6\u201311","journal-title":"J Electrocardiol"},{"key":"9267_CR29","unstructured":"Charoenphakdee N, Cui Z, Zhang Y, Sugiyama M (2021) Classification with rejection based on cost-sensitive classification. In: Proceedings of machine learning research in international conference on machine learning, pp 1507\u20131517"},{"key":"9267_CR30","unstructured":"Geifman Y, El-Yaniv R (2019) Selectivenet: a deep neural network with an integrated reject option. In: Proceedings of machine learning research international conference on machine learning, pp 2151\u20132159"},{"key":"9267_CR31","unstructured":"Louizos C, Welling M (2017) Multiplicative normalizing flows for variational bayesian neural networks. In: Proceedings of machine learning research international conference on machine learning, pp 2218\u20132227"},{"key":"9267_CR32","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Proceedings of machine learning research international conference on machine learning, pp 1050\u20131059"},{"key":"9267_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108102","volume":"120","author":"X Bai","year":"2021","unstructured":"Bai X, Wang X, Liu X, Liu Q, Song J, Sebe N, Kim B (2021) Explainable deep learning for efficient and robust pattern recognition: a survey of recent developments. Pattern Recogn 120:108102","journal-title":"Pattern Recogn"},{"key":"9267_CR34","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Adv Neural Inf Process Syst 30"},{"issue":"6","key":"9267_CR35","doi-asserted-by":"publisher","first-page":"82","DOI":"10.3390\/computers10060082","volume":"10","author":"AO Aseeri","year":"2021","unstructured":"Aseeri AO (2021) Uncertainty-aware deep learning-based cardiac arrhythmias classification model of electrocardiogram signals. Computers 10(6):82","journal-title":"Computers"},{"key":"9267_CR36","unstructured":"Malinin A, Gales M (2018) Predictive uncertainty estimation via prior networks. In: Proceedings of the 32nd international conference on neural information processing systems. NIPS\u201918, pp 7047\u20137058. Curran Associates Inc., Red Hook, NY, USA"},{"issue":"7","key":"9267_CR37","first-page":"1368","volume":"8","author":"F Liu","year":"2018","unstructured":"Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, Liu Y, Ma C, Wei S, He Z (2018) An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imag Health Inf 8(7):1368\u20131373","journal-title":"J Med Imag Health Inf"},{"key":"9267_CR38","doi-asserted-by":"crossref","unstructured":"Radosavovic I, Kosaraju RP, Girshick R, He K, Doll\u00e1r P (2020) Designing network design spaces. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10428\u201310436","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"9267_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9267_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision, pp 630\u2013645. Springer","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"9267_CR41","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"9267_CR42","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167"},{"key":"9267_CR43","unstructured":"Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. arXiv preprint arXiv:1710.05941"},{"issue":"1","key":"9267_CR44","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"9267_CR45","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"9267_CR46","unstructured":"Damianou A, Lawrence ND (2013) Deep Gaussian processes. In: Carvalho CM, Ravikumar P (eds) Proceedings of the sixteenth international conference on artificial intelligence and statistics. Proceedings of machine learning research, vol 31, pp 207\u2013215. Proceedings of machine learning research, Scottsdale, Arizona, USA"},{"key":"9267_CR47","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"9267_CR48","doi-asserted-by":"crossref","unstructured":"Murat F, Yildirim O, Talo M, Baloglu UB, Demir Y, Acharya UR (2020) Application of deep learning techniques for heartbeats detection using ecg signals-analysis and review. Comput Biol Med, 103726","DOI":"10.1016\/j.compbiomed.2020.103726"},{"key":"9267_CR49","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.compbiomed.2018.05.013","volume":"99","author":"SM Mathews","year":"2018","unstructured":"Mathews SM, Kambhamettu C, Barner KE (2018) A novel application of deep learning for single-lead ecg classification. Comput Biol Med 99:53\u201362","journal-title":"Comput Biol Med"},{"key":"9267_CR50","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","volume":"102","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2018","unstructured":"Y\u0131ld\u0131r\u0131m \u00d6, P\u0142awiak P, Tan R-S, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ecg signals. Comput Biol Med 102:411\u2013420","journal-title":"Comput Biol Med"},{"key":"9267_CR51","doi-asserted-by":"crossref","unstructured":"Moskalenko V, Zolotykh N, Osipov G (2019) Deep learning for ecg segmentation. In: International conference on neuroinformatics, pp 246\u2013254. Springer","DOI":"10.1007\/978-3-030-30425-6_29"},{"key":"9267_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104077","volume":"127","author":"Y Li","year":"2020","unstructured":"Li Y, Qu Q, Wang M, Yu L, Wang J, Shen L, He K (2020) Deep learning for digitizing highly noisy paper-based ecg records. Comput Biol Med 127:104077","journal-title":"Comput Biol Med"},{"issue":"1","key":"9267_CR53","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.cjca.2020.03.035","volume":"37","author":"S Zhou","year":"2021","unstructured":"Zhou S, Sapp JL, AbdelWahab A, Trayanova N (2021) Deep learning applied to electrocardiogram interpretation. Can J Cardiol 37(1):17\u201318. https:\/\/doi.org\/10.1016\/j.cjca.2020.03.035","journal-title":"Can J Cardiol"},{"key":"9267_CR54","doi-asserted-by":"publisher","unstructured":"Cai W, Hu D (2020) ECG interpretation with deep learning, pp 143\u2013156. https:\/\/doi.org\/10.1007\/978-981-15-3824-7_8","DOI":"10.1007\/978-981-15-3824-7_8"},{"key":"9267_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104194","volume":"79","author":"W Zhang","year":"2023","unstructured":"Zhang W, Geng S, Hong S (2023) A simple self-supervised ecg representation learning method via manipulated temporal-spatial reverse detection. Biomed Signal Process Control 79:104194","journal-title":"Biomed Signal Process Control"},{"key":"9267_CR56","doi-asserted-by":"crossref","unstructured":"Hong S, Zhang W, Sun C, Zhou Y, Li H (2022) Practical lessons on 12-lead ecg classification: meta-analysis of methods from physionet\/computing in cardiology challenge 2020. Front Physiol, 2505","DOI":"10.3389\/fphys.2021.811661"},{"key":"9267_CR57","doi-asserted-by":"publisher","unstructured":"Bae MH, Lee JH, Yang DH, Park HS, Cho Y, Chae SC, Jun JE (2012) Erroneous computer electrocardiogram interpretation of atrial fibrillation and its clinical consequences. Clin Cardiol 35(6):48\u2013353 https:\/\/arxiv.org\/abs\/https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/clc.22000. https:\/\/doi.org\/10.1002\/clc.22000","DOI":"10.1002\/clc.22000"},{"issue":"9","key":"9267_CR58","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1016\/j.jacc.2017.07.723","volume":"70","author":"J Schl\u00e4pfer","year":"2017","unstructured":"Schl\u00e4pfer J, Wellens HJ (2017) Computer-interpreted electrocardiograms: benefits and limitations. J Am College Cardiol 70(9):1183\u20131192. https:\/\/doi.org\/10.1016\/j.jacc.2017.07.723","journal-title":"J Am College Cardiol"},{"key":"9267_CR59","unstructured":"Yang L, Zhang Z, Hong S, Xu R, Zhao Y, Shao Y, Zhang W, Yang MH, Cui B (2022) Diffusion models: a comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796"},{"key":"9267_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108966","volume":"351","author":"W Ge","year":"2021","unstructured":"Ge W, Jing J, An S, Herlopian A, Ng M, Struck AF, Appavu B, Johnson EL, Osman G, Haider HA (2021) Deep active learning for interictal ictal injury continuum eeg patterns. J Heurosci Methods 351:108966","journal-title":"J Heurosci Methods"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09267-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09267-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09267-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:08:53Z","timestamp":1707732533000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09267-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,9]]},"references-count":60,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9267"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09267-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,12,9]]},"assertion":[{"value":"25 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical standard"}}]}}