乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T14:42:00Z","timestamp":1648824120542},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"Abstract<\/jats:title>\nBackground<\/jats:title>\nThe Elliot wave principle commonly characterizes the impulsive and corrective wave trends for both financial market trends and electrocardiograms. The impulsive wave trends of electrocardiograms can annotate several wave components of heart-beats including pathological heartbeat waveforms. The stopping time inquires which ordinal element satisfies the assumed mathematical condition within a numerical set. The proposed work constitutes several algorithmic states in reinforcement learning from the stopping time decision, which determines the impulsive wave trends. Each proposed algorithmic state is applicable to any relevant algorithmic state in reinforcement learning with fully numerical explanations. Because commercial electrocardiographs still misinterpret myocardial infarctions from extraordinary electrocardiograms, a novel algorithm needs to be developed to evaluate myocardial infarctions. Moreover, differential diagnosis for right ventricle infarction is required to contraindicate a medication such as nitroglycerin.<\/jats:p>\n<\/jats:sec>\nMethods<\/jats:title>\nThe proposed work implements the stopping time theory to impulsive wave trend distribution. The searching process of the stopping time theory is equivalent to the actions toward algorithmic states in reinforcement learning. The state value from each algorithmic state represents the numerically deterministic annotated results from the impulsive wave trend distribution. The shape of the impulsive waveform is evaluated from the interoperable algorithmic states via least-first-power approximation and approximate entropy. The annotated electrocardiograms from the impulsive wave trend distribution utilize a structure of neural networks to approximate the isoelectric baseline amplitude value of the electrocardiograms, and detect the conditions of myocardial infarction. The annotated results from the impulsive wave trend distribution consist of another reinforcement learning environment for the evaluation of impulsive waveform direction.<\/jats:p>\n<\/jats:sec>\nResults<\/jats:title>\nThe accuracy to discern myocardial infarction was found to be 99.2754% for the data from the comma-separated value format files, and 99.3579% for those containing representative beats. The clinical dataset included 276 electrocardiograms from the comma-separated value files and 623 representative beats.<\/jats:p>\n<\/jats:sec>\nConclusions<\/jats:title>\nOur study aims to support clinical interpretation on 12-channel electrocardiograms. The proposed work is suitable for a differential diagnosis under infarction in the right ventricle to avoid contraindicated medication during emergency. An impulsive waveform that is affected by myocardial infarction or the electrical direction of electrocardiography is represented as an inverse waveform.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12911-020-01133-x","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T18:05:26Z","timestamp":1591121126000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning"],"prefix":"10.1186","volume":"20","author":[{"given":"Jong-Rul","family":"Park","sequence":"first","affiliation":[]},{"given":"Sung Phil","family":"Chung","sequence":"additional","affiliation":[]},{"given":"Sung Yeon","family":"Hwang","sequence":"additional","affiliation":[]},{"given":"Tae Gun","family":"Shin","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1058-990X","authenticated-orcid":false,"given":"Jong Eun","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,1]]},"reference":[{"key":"1133_CR1","doi-asserted-by":"crossref","DOI":"10.4018\/978-1-5225-0565-5","volume-title":"Pattern recognition and classification in time series data","author":"E Volna","year":"2017","unstructured":"Volna E, Kotyrba M, Janosek M. Pattern recognition and classification in time series data. Hershey: IGI Global; 2017."},{"key":"1133_CR2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.resourpol.2018.06.010","volume":"59","author":"M Mara\u00f1on","year":"2018","unstructured":"Mara\u00f1on M, Kumral M. Exploring the Elliott wave principle to interpret metal commodity price cycles. Resources Policy. 2018;59:125\u201338.","journal-title":"Resources Policy"},{"issue":"4","key":"1133_CR3","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1590\/2446-4740.05815","volume":"32","author":"RF Dalvi","year":"2016","unstructured":"Dalvi RF, Zago GT, Andreao RV. Heartbeat classification system based on neural networks and dimensionality reduction. Res Biomed Eng. 2016;32(4):318\u201326.","journal-title":"Res Biomed Eng"},{"issue":"10","key":"1133_CR4","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.medengphy.2010.08.007","volume":"32","author":"S Dutta","year":"2010","unstructured":"Dutta S, Chatterjee A, Munshi S. Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification. Med Eng Phys. 2010;32(10):1161\u20139.","journal-title":"Med Eng Phys"},{"key":"1133_CR5","doi-asserted-by":"crossref","first-page":"41011","DOI":"10.1038\/srep41011","volume":"7","author":"H Li","year":"2017","unstructured":"Li H, Yuan D, Ma X, Cui D, Cao L. Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Sci Rep. 2017;7:41011.","journal-title":"Sci Rep"},{"key":"1133_CR6","doi-asserted-by":"publisher","unstructured":"Alarsan FI, Younes M. Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. J Big Data. 2019;6(81). https:\/\/doi.org\/10.1186\/s40537-019-0244-x.","DOI":"10.1186\/s40537-019-0244-x"},{"key":"1133_CR7","volume-title":"Regulated Clinical Research Information Management Technical Committee","author":"BD Brown","year":"2005","unstructured":"Brown BD, Badilini F. HL7 aECG Implementation Guide. In: Regulated Clinical Research Information Management Technical Committee; 2005. http:\/\/www.amps-llc.com\/uploads\/2017-12-7\/aECG_Implementation_Guide(1).pdf. Accessed 1 Dec 2019."},{"issue":"8","key":"1133_CR8","doi-asserted-by":"crossref","first-page":"744","DOI":"10.3390\/e21080744","volume":"21","author":"S Wang","year":"2019","unstructured":"Wang S, Xie X, Huang K, Zeng J, Cai Z. Deep reinforcement learning-based traffic signal control using high-resolution event-based data. Entropy. 2019;21(8):744.","journal-title":"Entropy."},{"key":"1133_CR9","volume-title":"Machine learning: an algorithmic perspective","author":"S Marsland","year":"2009","unstructured":"Marsland S. Machine learning: an algorithmic perspective. Chapman & Hall\/CRC: Boca Raton; 2009."},{"issue":"3","key":"1133_CR10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10288-016-0337-8","volume":"15","author":"B De Saporta","year":"2017","unstructured":"De Saporta B, Dufour F, Nivot C. Partially observed optimal stopping problem for discrete-time Markov processes. 4OR. 2017;15(3):277\u2013302.","journal-title":"4OR"},{"key":"1133_CR11","first-page":"10944","volume":"1912","author":"K Shao","year":"2019","unstructured":"Shao K, Tang Z, Zhu Y, Li N, Zhao D. A survey of deep reinforcement learning in video games. arXiv. 2019;1912:10944.","journal-title":"arXiv"},{"issue":"1","key":"1133_CR12","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.spl.2012.09.024","volume":"83","author":"T Fischer","year":"2013","unstructured":"Fischer T. On simple representations of stopping times and stopping time sigma-algebras. Stat Probabil Lett. 2013;83(1):345\u20139.","journal-title":"Stat Probabil Lett"},{"issue":"10","key":"1133_CR13","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/9.793723","volume":"44","author":"JN Tsitsiklis","year":"1999","unstructured":"Tsitsiklis JN, Van Roy B. Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives. IEEE Trans Autom Control. 1999;44(10):1840\u201351.","journal-title":"IEEE Trans Autom Control"},{"issue":"2","key":"1133_CR14","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.jmaa.2014.12.029","volume":"425","author":"E Chevalier","year":"2015","unstructured":"Chevalier E, Ly Vath V, Roch A, Scotti S. Optimal exit strategies for investment projects. J Math Anal Appl. 2015;425(2):666\u201394.","journal-title":"J Math Anal Appl"},{"issue":"3","key":"1133_CR15","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s00780-012-0173-4","volume":"16","author":"J Detemple","year":"2012","unstructured":"Detemple J, Tian W, Xiong J. An optimal stopping problem with a reward constraint. Finance Stochastics. 2012;16(3):423\u201348.","journal-title":"Finance Stochastics"},{"issue":"3","key":"1133_CR16","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1287\/opre.2013.1167","volume":"31","author":"L Li","year":"2013","unstructured":"Li L, Linetsky V. Optimal stopping and early exercise: an eigenfunction expansion approach. Oper Res. 2013;31(3):625\u201343.","journal-title":"Oper Res"},{"key":"1133_CR17","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.automatica.2018.06.013","volume":"95","author":"V Krishnamurthy","year":"2018","unstructured":"Krishnamurthy V, Aprem A, Bhatt S. Multiple stopping time POMDPs: structural results & application in interactive advertising on social media. Automatica. 2018;95:385\u201398.","journal-title":"Automatica."},{"key":"1133_CR18","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511574740","volume-title":"Stopping times and directed processes","author":"GA Edgar","year":"1992","unstructured":"Edgar GA, Sucheston L. Stopping times and directed processes. New York: Cambridge University Press; 1992."},{"key":"1133_CR19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijcard.2017.05.063","volume":"243","author":"MY Van der Ende","year":"2017","unstructured":"Van der Ende MY, Hartman MHT, Schurer RAJ, van der Werf HW, Lipsic E, Snieder H, van der Harst P. Prevalence of electrocardiographic unrecognized myocardial infarction and its association with mortality. Int J Cardiol. 2017;243:34\u20139.","journal-title":"Int J Cardiol"},{"issue":"8","key":"1133_CR20","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1111\/echo.13222","volume":"33","author":"A Kataoka","year":"2016","unstructured":"Kataoka A, Scherrer-Crosbie M, Senior R, Garceau P, Valbuena S, \u010celutkien\u0117 J, Hastings JL, Cheema AN, Lara A, Srbinovska-Kostovska E, Hessian R, Poggio D, Goldweit R, Saric M, Dajani KA, Kohn JA, Shaw LJ, Reynolds HR, Picard MH. Transient ischemic dilatation during stress echocardiography: an additional marker of significant myocardial ischemia. Echocardiography. 2016;33(8):1202\u20138.","journal-title":"Echocardiography."},{"issue":"3","key":"1133_CR21","first-page":"80","volume":"3","author":"JR Park","year":"2017","unstructured":"Park JR, Park JE. A T-wave variation characteristics evaluation algorithm for ischemic heart beats. Int J Health Med Sci. 2017;3(3):80\u20134.","journal-title":"Int J Health Med Sci"},{"issue":"3","key":"1133_CR22","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s11517-017-1696-9","volume":"56","author":"C S\u00e1nchez","year":"2018","unstructured":"S\u00e1nchez C, D\u2019Ambrosio G, Maffessanti F, Caiani EG, Prinzen FW, Krause R, Auricchio A, Potse M. Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients. Med Biol Eng Comput. 2018;56(3):491\u2013504.","journal-title":"Med Biol Eng Comput"},{"key":"1133_CR23","volume-title":"Reinforcement learning: an introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge: The MIT Press; 2018."},{"key":"1133_CR24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-1-4615-3618-5_4","volume-title":"Reinforcement learning","author":"CJCH Watkins","year":"1992","unstructured":"Watkins CJCH. Technical note: Q-learning. In: Sutton RS, editor. Reinforcement learning. Dordrecht: Kluwer Academic Publishers Group; 1992. p. 55\u201368."},{"issue":"13","key":"1133_CR25","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1016\/j.neucom.2007.11.040","volume":"71","author":"Y Achbany","year":"2008","unstructured":"Achbany Y, Fouss F, Yen L, Pirotte A, Saerens M. Tuning continual exploration in reinforcement learning: an optimality property of the Boltzmann strategy. Neurocomputing. 2008;71(13):2507\u201320.","journal-title":"Neurocomputing."},{"key":"1133_CR26","volume-title":"Bridging the gap between value and policy based reinforcement learning. Proceedings of 31st Conference on Neural Information Processing Systems","author":"O Nachum","year":"2017","unstructured":"Nachum O, Norouzi M, Xu K, Schuurmans D. Bridging the gap between value and policy based reinforcement learning. Proceedings of 31st Conference on Neural Information Processing Systems. 2017; arXiv:1702.08892."},{"key":"1133_CR27","series-title":"Proceedings of 35th International Conference on Machine Learning","volume-title":"Path consistency learning in Tsallis entropy regularized MDPs","author":"O Nachum","year":"2018","unstructured":"Nachum O, Chow Y, Ghavamzadeh M. Path consistency learning in Tsallis entropy regularized MDPs. Proceedings of 35th International Conference on Machine Learning. 2018; arXiv:1802.03501."},{"issue":"2","key":"1133_CR28","doi-asserted-by":"crossref","first-page":"288","DOI":"10.3758\/BF03194066","volume":"14","author":"ML Kalish","year":"2007","unstructured":"Kalish ML, Griffiths TL, Lewandowsky S. Iterated learning: intergenerational knowledge transmission reveals inductive biases. Psychon Bull Rev. 2007;14(2):288\u201394.","journal-title":"Psychon Bull Rev"},{"key":"1133_CR29","volume-title":"The influential mind","author":"T Sharot","year":"2017","unstructured":"Sharot T. The influential mind. London: Abacus An Imprint of Little, Brown Book Group; 2017."},{"key":"1133_CR30","volume-title":"2010 handbook of emergency cardiovascular Care for Healthcare Providers","author":"MF Hazinski","year":"2010","unstructured":"Hazinski MF, Samson R, Schexnayder S. 2010 handbook of emergency cardiovascular Care for Healthcare Providers. Dallas: American Heart Association; 2010."},{"key":"1133_CR31","volume-title":"ECG monitoring of myocardial ischemia for perioperative care","author":"P Friederich","year":"2015","unstructured":"Friederich P. ECG monitoring of myocardial ischemia for perioperative care. L\u00fcbeck: Dr\u00e4gerwerk AG & Co. KGaA; 2015."},{"issue":"4","key":"1133_CR32","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1053\/euhj.2001.2774","volume":"23","author":"CT Larsen","year":"2002","unstructured":"Larsen CT, Dahlin J, Blackburn H, Scharling H, Appleyard M, Sigurd B, Schnohr P. Prevalence and prognosis of electrocardiographic left ventricular hypertrophy, ST segment depression and negative T-wave; the Copenhagen City heart study. Eur Heart J. 2002;23(4):315\u201324.","journal-title":"Eur Heart J"},{"key":"1133_CR33","volume-title":"An introduction to the approximation of functions","author":"TJ Rivlin","year":"2003","unstructured":"Rivlin TJ. An introduction to the approximation of functions. Mineola: Dover Publications; 2003."},{"key":"1133_CR34","volume-title":"Contemporary Bayesian and frequentist statistical research methods for natural resource scientists","author":"HB Stauffer","year":"2008","unstructured":"Stauffer HB. Contemporary Bayesian and frequentist statistical research methods for natural resource scientists. Hoboken: Wiley; 2008."},{"key":"1133_CR35","volume-title":"Hello world: how to be human in the age of the machine","author":"H Fry","year":"2018","unstructured":"Fry H. Hello world: how to be human in the age of the machine. London: Transworld Publishers; 2018."},{"key":"1133_CR36","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.jelectrocard.2013.06.020","volume":"46","author":"EY Wei","year":"2013","unstructured":"Wei EY, Hira RS, Huang HD, Wilson JM, Elayda MA, Sherron SR, Birnbaum Y. Pitfalls in diagnosing ST elevation among patients with acute myocardial infarction. J Electrocardiol. 2013;46:653\u20139.","journal-title":"J Electrocardiol"},{"issue":"4","key":"1133_CR37","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.ihj.2013.06.013","volume":"65","author":"G Coppola","year":"2013","unstructured":"Coppola G, Carita P, Corrado E, Borrelli A, Rotolo A, Guglielmo M, Nugara C, Ajello L, Santomauro M, Novo S. On behalf of the Italian study Group of Cardiovascular Emergencies of the Italian Society of Cardiology. ST segment elevations: always a marker of acute myocardial infarction? Indian Heart J. 2013;65(4):412\u201323.","journal-title":"Indian Heart J"},{"issue":"6","key":"1133_CR38","doi-asserted-by":"crossref","first-page":"541","DOI":"10.3390\/e21060541","volume":"21","author":"A Delgado-Bonal","year":"2019","unstructured":"Delgado-Bonal A, Marshak A. Approximate entropy and sample entropy: a comprehensive tutorial. Entropy. 2019;21(6):541.","journal-title":"Entropy."},{"key":"1133_CR39","doi-asserted-by":"publisher","DOI":"10.13026\/C2ZW2H","volume-title":"S-T segment characterization for long-term automated ECG analysis. MS thesis, Massachusetts institute of technology: Department of Electrical Engineering and Computer Science","author":"P Albrecht","year":"1983","unstructured":"Albrecht P. S-T segment characterization for long-term automated ECG analysis. MS thesis, Massachusetts institute of technology: Department of Electrical Engineering and Computer Science; 1983. https:\/\/doi.org\/10.13026\/C2ZW2H."},{"issue":"23","key":"1133_CR40","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.13026\/C2ZW2H","volume":"101","author":"AL Goldberger","year":"2003","unstructured":"Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, IvanovPCh MRG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2003;101(23):e215\u201320. https:\/\/doi.org\/10.13026\/C2ZW2H.","journal-title":"Circulation."},{"key":"1133_CR41","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.cmpb.2017.09.015","volume":"152","author":"P Kora","year":"2017","unstructured":"Kora P. ECG based myocardial infarction detection using hybrid friendly algorithm. Comput Methods Prog Biomed. 2017;152:141\u20138.","journal-title":"Comput Methods Prog Biomed"},{"issue":"10","key":"1133_CR42","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1016\/j.asoc.2012.06.004","volume":"12","author":"P Chang","year":"2012","unstructured":"Chang P, Lin J, Hsieh J, Weng J. Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl Soft Comput. 2012;12(10):3165\u201375.","journal-title":"Appl Soft Comput"},{"issue":"9","key":"1133_CR43","doi-asserted-by":"crossref","first-page":"488","DOI":"10.3390\/e19090488","volume":"19","author":"M Kumar","year":"2017","unstructured":"Kumar M, Pachori RB, Acharya UR. Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy. 2017;19(9):488.","journal-title":"Entropy."},{"issue":"2","key":"1133_CR44","doi-asserted-by":"crossref","first-page":"51","DOI":"10.5530\/jcdr.2015.2.2","volume":"6","author":"J Kojuri","year":"2015","unstructured":"Kojuri J, Boostani R, Dehghani P, Nowroozipour F, Saki N. Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. J Cardiovasc Dis Res. 2015;6(2):51\u20139.","journal-title":"J Cardiovasc Dis Res"},{"issue":"7","key":"1133_CR45","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1007\/s11739-017-1699-4","volume":"12","author":"L Furlan","year":"2017","unstructured":"Furlan L, Rusconi AM, Ceriani E. Five steps for use and interpretation of troponin in the emergency department. Intern Emerg Med. 2017;12(7):1055\u20138.","journal-title":"Intern Emerg Med"},{"issue":"1","key":"1133_CR46","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1373\/clinchem.2016.255109","volume":"63","author":"FS Apple","year":"2017","unstructured":"Apple FS, Sandoval Y, Jaffe AS, Ordonez-Llanos J. Cardiac troponin assays: guide to understanding analytical characteristics and their impact on clinical care. Clin Chem. 2017;63(1):73\u201381.","journal-title":"Clin Chem"},{"key":"1133_CR47","doi-asserted-by":"publisher","unstructured":"McCarthy CP, Raber I, Chapman AR, Sandoval Y, Apple FS, Mills NL, Januzzi JL Jr. Myocardial injury in the era of high-sensitivity cardiac troponin assays: a practical approach for clinicians. Am Med Assoc Cardiol. 2019. https:\/\/doi.org\/10.1001\/jamacardio.2019.2724.","DOI":"10.1001\/jamacardio.2019.2724"},{"issue":"12","key":"1133_CR48","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1161\/CIRCULATIONAHA.117.031806","volume":"137","author":"AR Chapman","year":"2018","unstructured":"Chapman AR, Shah ASV, Lee KK, Anand A, Francis O, Adamson P, McAllister DA, Strachan FE, Newby DE, Mills NL. Long-term outcomes in patients with type 2 myocardial infarction and myocardial injury. Circulation. 2018;137(12):1236\u201345.","journal-title":"Circulation."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01133-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-020-01133-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01133-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T23:45:18Z","timestamp":1622504718000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01133-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,1]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1133"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01133-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,1]]},"assertion":[{"value":"5 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Institutional Review Board at Yonsei University Gangnam Severance Hospital, Seoul, Korea permitted research usage of the clinical electrocardiograms under Grant 3\u20132017-0379. Institutional Review Board at Yonsei University Gangnam Severance Hospital approves a research project that has omitted any personal identification information, and re-identified the personal identification information as a research subject number to allow a waiver of the consent for research participation including publication.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"99"}}