{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:45:51Z","timestamp":1740181551920,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["20014967"],"award-info":[{"award-number":["20014967"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-022-01309-4","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T15:02:32Z","timestamp":1659798152000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explanation of HRV Features for Detecting Atrial Fibrillation"],"prefix":"10.1007","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-6104","authenticated-orcid":false,"given":"Yongho","family":"Lee","sequence":"first","affiliation":[]},{"given":"Vinh","family":"Pham","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-8114","authenticated-orcid":false,"given":"Tai-Myoung","family":"Chung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"1309_CR1","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1038\/415219a","volume":"415","author":"S Nattel","year":"2002","unstructured":"Nattel S. New ideas about atrial fibrillation 50 years on. Nature. 2002;415:219\u201326.","journal-title":"Nature"},{"key":"1309_CR2","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1038\/nrcardio.2014.118","volume":"11","author":"F Rahman","year":"2014","unstructured":"Rahman F, Kwan GF, Benjamin EJ. Global epidemiology of atrial fibrillation. Nat Rev Cardiol. 2014;11:639\u201354.","journal-title":"Nat Rev Cardiol"},{"key":"1309_CR3","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1023\/A:1009823001707","volume":"4","author":"I Savelieva","year":"2000","unstructured":"Savelieva I, Camm AJ. Clinical relevance of silent atrial fibrillation: prevalence, prognosis, quality of life, and management. J Interv Card Electrophysiol. 2000;4:369\u201382.","journal-title":"J Interv Card Electrophysiol"},{"key":"1309_CR4","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1161\/CIRCEP.112.970749","volume":"5","author":"CE Chiang","year":"2012","unstructured":"Chiang CE, Naditch-Br\u00fbl\u00e9 L, Murin J, Goethals M, Inoue M, O\u2019Neill J, Silva-Cardoso J, Zharinov O, Gamra H, Alam S, Ponikowski P, Lewalter T, Rosenqvist M, Steg PG. Distribution and risk profile of paroxysmal. Persistent, and permanent atrial fibrillation in routine clinical practice: insight from the real-life global survey evaluating patients with atrial fibrillation international registry. Circ Arrhythm Electrophysiol. 2012;5:632\u20139.","journal-title":"Circ Arrhythm Electrophysiol"},{"key":"1309_CR5","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1093\/eurheartj\/ehn139","volume":"29","author":"R Nieuwlaat","year":"2008","unstructured":"Nieuwlaat R, Prins MH, Le Heuzey J-Y, Vardas PE, Aliot E, Santini M, Cobbe SM, Widdershoven JW, Baur LH, L\u00e9vy S, Crijns HJ. Prognosis, disease progression, and treatment of atrial fibrillation patients during 1 year: follow-up of the Euro Heart Survey on Atrial Fibrillation. Eur Heart J. 2008;29:1181\u20139.","journal-title":"Eur Heart J"},{"key":"1309_CR6","unstructured":"Atrial fibrillation - living with | NHLBI, NIH. https:\/\/www.nhlbi.nih.gov\/health\/atrial-fibrillation\/living-with. Accessed 22 Apr 2022."},{"issue":"1","key":"1309_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1146\/annurev.me.39.020188.000353","volume":"39","author":"JS Alpert","year":"1988","unstructured":"Alpert JS, Petersen P, Godtfredsen J. Atrial fibrillation: natural history, complications, and management. Annu Rev Med. 1988;39(1):41\u201352.","journal-title":"Annu Rev Med"},{"key":"1309_CR8","first-page":"363","volume-title":"Future data and security engineering. Big data, security and privacy smart city and industry 40 applications","author":"Y Lee","year":"2021","unstructured":"Lee Y, Pham V, Chung TM. Innovative way of detecting atrial fibrillation based on HRV features using AI-techniques. In: Dang TK, K\u00fcng J, Chung TM, Takizawa M, editors. Future data and security engineering. Big data, security and privacy smart city and industry 40 applications. Singapore: Springer; 2021. p. 363\u201374."},{"key":"1309_CR9","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0169-2607(03)00079-8","volume":"74","author":"MG Tsipouras","year":"2004","unstructured":"Tsipouras MG, Fotiadis DI. Automatic arrhythmia detection based on time and time\u2013frequency analysis of heart rate variability. Comput Methods Programs Biomed. 2004;74:95\u2013108.","journal-title":"Comput Methods Programs Biomed"},{"key":"1309_CR10","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, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet. 2019;394:861\u20137.","journal-title":"Lancet"},{"key":"1309_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114452","volume":"169","author":"G Hirsch","year":"2021","unstructured":"Hirsch G, Jensen SH, Poulsen ES, Puthusserypady S. Atrial fibrillation detection using heart rate variability and atrial activity: a hybrid approach. Expert Syst Appl. 2021;169: 114452.","journal-title":"Expert Syst Appl"},{"key":"1309_CR12","doi-asserted-by":"publisher","first-page":"549","DOI":"10.3390\/info11120549","volume":"11","author":"S Liaqat","year":"2020","unstructured":"Liaqat S, Dashtipour K, Zahid A, Assaleh K, Arshad K, Ramzan N. Detection of atrial fibrillation using a machine learning approach. Information. 2020;11:549.","journal-title":"Information"},{"key":"1309_CR13","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","volume":"5","author":"F Shaffer","year":"2017","unstructured":"Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258.","journal-title":"Front Public Health"},{"key":"1309_CR14","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:E215-220.","journal-title":"Circulation"},{"key":"1309_CR15","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2017.065-469","author":"GD Clifford","year":"2017","unstructured":"Clifford GD, Liu C, Moody B, Lehman LwH LehmaLwH, Silva I, Li Q, Johnson AE. AF classification from a short single lead ECG recording: the physionet\/computing in cardiology challenge 2017. Comput Cardiol. 2017. https:\/\/doi.org\/10.22489\/CinC.2017.065-469.","journal-title":"Comput Cardiol"},{"issue":"17","key":"1309_CR16","first-page":"1","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre G, Nogueira F, Aridas CK. Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res. 2017;18(17):1\u20135.","journal-title":"J Mach Learn Res"},{"key":"1309_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"1309_CR18","unstructured":"Gomes P, Margaritoff P, Silva H. pyHRV: \u201cDevelopment and evaluation of an open-source python toolbox for heart rate variability (HRV)\u201d in Proc. Electronic and Computing Engineering (IcETRAN): Int\u2019l Conf. on Electrical; 2019. p. 822\u20138."},{"key":"1309_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"1309_CR20","unstructured":"Highlights\u2014pyHRV - OpenSource python toolbox for heart rate variability 0.4 Documentation. https:\/\/pyhrv.readthedocs.io\/en\/latest\/. Accessed 13 Apr 2022."},{"issue":"86","key":"1309_CR21","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(86):2579\u2013605.","journal-title":"J Mach Learn Res"},{"issue":"3","key":"1309_CR22","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1111\/1467-9868.00196","volume":"61","author":"ME Tipping","year":"1999","unstructured":"Tipping ME, Bishop CM. Probabilistic principal component analysis. J R Stat Soc. 1999;61(3):611\u201322.","journal-title":"J R Stat Soc"},{"key":"1309_CR23","doi-asserted-by":"publisher","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","volume":"374","author":"IT Jolliffe","year":"2016","unstructured":"Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans R Soc A. 2016;374:20150202.","journal-title":"Philos Trans R Soc A"},{"issue":"1","key":"1309_CR24","first-page":"281","volume":"5","author":"J MacQueen","year":"1967","unstructured":"MacQueen J. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. 1967;5(1):281\u201398.","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics"},{"key":"1309_CR25","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1088\/0967-3334\/36\/9\/1873","volume":"36","author":"M Carrara","year":"2015","unstructured":"Carrara M, Carozzi L, Moss TJ, de Pasquale M, Cerutti S, Ferrario M, Lake DE, Moorman JR. Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy. Physiol Meas. 2015;36:1873\u201388.","journal-title":"Physiol Meas"},{"key":"1309_CR26","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1161\/01.CIR.100.18.1879","volume":"100","author":"S-A Chen","year":"1999","unstructured":"Chen S-A, Hsieh M-H, Tai C-T, Tsai C-F, Prakash VS, Yu W-C, Hsu T-L, Ding Y-A, Chang M-S. Initiation of atrial fibrillation by ectopic beats originating from the pulmonary veins. Circulation. 1999;100:1879\u201386.","journal-title":"Circulation"},{"key":"1309_CR27","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1136\/hrt.52.4.396","volume":"52","author":"DJ Ewing","year":"1984","unstructured":"Ewing DJ, Neilson JM, Travis P. New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms. Heart. 1984;52:396\u2013402.","journal-title":"Heart"},{"key":"1309_CR28","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/0002-9149(88)90917-4","volume":"61","author":"JT Bigger","year":"1988","unstructured":"Bigger JT, Kleiger RE, Fleiss JL, Rolnitzky LM, Steinman RC, Miller JP. Components of heart rate variability measured during healing of acute myocardial infarction. Am J Cardiol. 1988;61:208\u201315.","journal-title":"Am J Cardiol"},{"key":"1309_CR29","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1016\/0002-8703(93)90535-H","volume":"126","author":"S Chakko","year":"1993","unstructured":"Chakko S, Mulingtapang RF, Huikuri HV, Kessler KM, Materson BJ, Myerburg RJ. Alterations in heart rate variability and its circadian rhythm in hypertensive patients with left ventricular hypertrophy free of coronary artery disease. Am Heart J. 1993;126:1364\u201372.","journal-title":"Am Heart J"},{"key":"1309_CR30","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1136\/heart.88.4.378","volume":"88","author":"JE Mietus","year":"2002","unstructured":"Mietus JE, Peng C-K, Henry I, Goldsmith RL, Goldberger AL. The pNNx files: re-examining a widely used heart rate variability measure. Heart. 2002;88:378\u201380.","journal-title":"Heart"},{"issue":"1","key":"1309_CR31","first-page":"50","volume":"4","author":"S Behbahani","year":"2013","unstructured":"Behbahani S, Dabanloo NJ, Nasrabadi AM. Ictal heart rate variability assessment with focus on secondary generalized and complex partial epileptic seizures. Adv Biores. 2013;4(1):50\u20138.","journal-title":"Adv Biores"},{"key":"1309_CR32","doi-asserted-by":"publisher","DOI":"10.2170\/physiolsci.RP005506","author":"P Guzik","year":"2007","unstructured":"Guzik P, Piskorski J, Krauze T, Schneider R, Wesseling KH, Wykretowicz A, Wysocki H. Correlations between poincar\u00e9 plot and conventional heart rate variability parameters assessed during paced breathing. J Physiol Sci. 2007. https:\/\/doi.org\/10.2170\/physiolsci.RP005506.","journal-title":"J Physiol Sci"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01309-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01309-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01309-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T21:49:30Z","timestamp":1668030570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01309-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,6]]},"references-count":32,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["1309"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01309-4","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2022,8,6]]},"assertion":[{"value":"6 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Yongho Lee is an employee of Hippo T&C, Inc. And Dr. Tai-Myoung Chung is the Chief Executive Officer of Hippo T&C, Inc. Business interest did not influence this research, and neither financial nor material gains were made as a result of it.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"As the authors are not the entity which engaged in the direct collection or distribution of human originating data, used to conduct experiments in this research, they have nothing to declare concerning informed consent. The authors are authorized per the Open Data Commons Attribution License (ODC-By) v1.0 to use the relevant dataset for this research. The authors, in good faith, believe that they are in compliance with the relevant ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Data originating from human participants, used in this research, were acquired from PhysioNet. PhysioNet, in turn, explicitly authorizes anyone to utilize the relevant datasets under the condition that the terms specified under the Open Data Commons Attribution License (ODC-By) v1.0 is complied with. The authors, in good faith, believe that they are in compliance with the relevant ethical standards.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human Participants and\/or Animals"}}],"article-number":"424"}}