{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:46:06Z","timestamp":1778823966044,"version":"3.51.4"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2021-128525OB- I00"],"award-info":[{"award-number":["PID2021-128525OB- I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["PID2021-123804OB-I00"],"award-info":[{"award-number":["PID2021-123804OB-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006280","name":"Ministerio de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["TED2021-130935B-I00"],"award-info":[{"award-number":["TED2021-130935B-I00"]}],"id":[{"id":"10.13039\/501100006280","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006280","name":"Ministerio de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["10.13039\/501100011033"],"award-info":[{"award-number":["10.13039\/501100011033"]}],"id":[{"id":"10.13039\/501100006280","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011698","name":"Junta de Comunidades de Castilla-La Mancha","doi-asserted-by":"publisher","award":["SBPLY\/21\/180501\/000186"],"award-info":[{"award-number":["SBPLY\/21\/180501\/000186"]}],"id":[{"id":"10.13039\/501100011698","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003359","name":"Generalitat Valenciana","doi-asserted-by":"publisher","award":["SBPLY\/21\/ 180501\/000186"],"award-info":[{"award-number":["SBPLY\/21\/ 180501\/000186"]}],"id":[{"id":"10.13039\/501100003359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>Persistent atrial fibrillation is the most common sustained cardiac arrhythmia, frequently linked with increased mortality and morbidity. Electrical cardioversion (ECV) remains the gold standard for sinus rhythm (SR) restoration, even though presenting potential adverse effects and a high relapsing rate. Predicting ECV outcome from the 12-lead ECG could reduce healthcare costs while preventing complications in patients unlikely to maintain SR. To this end, atrial activity (AA) organization has been traditionally evaluated through the amplitude and dominant frequency of the fibrillatory waves at lead II. However, physiological systems are known to exhibit complex dynamics across multiple time-scales, making multiscale (MSE) entropy measures a more suitable tool, as they can incorporate relevant information that may have been previously overlooked. Here, the predictive power of different MSE-based indices for the ECV outcome in 58 patients is evaluated. AA was estimated using a QT segment cancellation algorithm. Patients were classified based on SR maintenance after a 30-day follow-up. Results show that traditionally used indices report the highest predictive rate over the limb leads (79%). However, they are outperformed by Refined MSE over precordial leads (87%). Moreover, when considering statistical modeling techniques such as support vector machines, the prediction accuracy is increased (98%). In conclusion, MSE-based indices computed from precordial leads can robustly predict ECV outcome with higher accuracy than traditional approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphic abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s11517-025-03449-0","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T05:24:18Z","timestamp":1760073858000},"page":"335-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sinus rhythm maintenance in persistent atrial fibrillation: 12-lead ECG multiscale entropy characterization"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0623-3652","authenticated-orcid":false,"given":"Eva M.","family":"Cirugeda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eva","family":"Plancha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00edctor M.","family":"Hidalgo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sof\u00eda","family":"Calero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 J.","family":"Rieta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ra\u00fal","family":"Alcaraz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"issue":"4","key":"3449_CR1","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jacc.2006.07.018","volume":"48","author":"V Fuster","year":"2006","unstructured":"Fuster V, Ryd\u00e9n L et al (2006) ACC\/AHA\/ESC 2006 guidelines for the management of patients with atrial fibrillation. J Am Coll Cardiol 48(4):149\u2013246. https:\/\/doi.org\/10.1016\/j.jacc.2006.07.018","journal-title":"J Am Coll Cardiol"},{"key":"3449_CR2","doi-asserted-by":"publisher","unstructured":"January C, Wann L, et al (2014) 2014 AHA\/ACC\/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology\/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. Circ 130(23). https:\/\/doi.org\/10.1161\/CIR.0000000000000041","DOI":"10.1161\/CIR.0000000000000041"},{"issue":"8","key":"3449_CR3","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1093\/europace\/eul072","volume":"8","author":"F Holmqvist","year":"2006","unstructured":"Holmqvist F, Stridh M et al (2006) Atrial fibrillation signal organization predicts sinus rhythm maintenance in patients undergoing cardioversion of atrial fibrillation. Europace 8(8):559\u2013565. https:\/\/doi.org\/10.1093\/europace\/eul072","journal-title":"Europace"},{"key":"3449_CR4","doi-asserted-by":"publisher","unstructured":"Lankveld T, Zeemering S, et al (2016) Atrial fibrillation complexity parameters derived from surface ECGs predict procedural outcome and long-term follow-up of stepwise catheter ablation for atrial fibrillation. Circ Arrhythm Electrophys 9(2). https:\/\/doi.org\/10.1161\/CIRCEP.115.003354","DOI":"10.1161\/CIRCEP.115.003354"},{"issue":"7","key":"3449_CR5","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1093\/europace\/eux234","volume":"20","author":"S Zeemering","year":"2018","unstructured":"Zeemering S, Lankveld T et al (2018) The electrocardiogram as a predictor of successful pharmacological cardioversion and progression of atrial fibrillation. Europace 20(7):96\u2013104. https:\/\/doi.org\/10.1093\/europace\/eux234","journal-title":"Europace"},{"issue":"6","key":"3449_CR6","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1016\/j.bspc.2013.02.005","volume":"8","author":"A Buttu","year":"2013","unstructured":"Buttu A, Pruvot E et al (2013) Adaptive frequency tracking of the baseline ECG identifies the site of atrial fibrillation termination by catheter ablation. Biomed Signal Process Control 8(6):969\u2013980. https:\/\/doi.org\/10.1016\/j.bspc.2013.02.005","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"3449_CR7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/S0008-6363(97)00289-7","volume":"38","author":"M Holm","year":"1998","unstructured":"Holm M, Pehrson S et al (1998) Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method. Cardiovasc Res 38(1):69\u201381. https:\/\/doi.org\/10.1016\/S0008-6363(97)00289-7","journal-title":"Cardiovasc Res"},{"key":"3449_CR8","doi-asserted-by":"publisher","unstructured":"Dharmaprani D, Dykes L, et al (2018) Information theory and atrial fibrillation (AF): a review. Front Physiol 9:957. https:\/\/doi.org\/10.3389\/fphys.2018.00957","DOI":"10.3389\/fphys.2018.00957"},{"issue":"6","key":"3449_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/EMB-M.2006.250505","volume":"25","author":"S Petrutiu","year":"2006","unstructured":"Petrutiu S, Ng J et al (2006) Atrial fibrillation and waveform characterization. IEEE Eng Med Biol Mag 25(6):24\u201330. https:\/\/doi.org\/10.1109\/EMB-M.2006.250505","journal-title":"IEEE Eng Med Biol Mag"},{"key":"3449_CR10","doi-asserted-by":"publisher","unstructured":"Juli\u00e1n M, Alcaraz R, Rieta J (2014) Comparative assessment of nonlinear metrics to quantify organization-related events in surface electrocardiograms of atrial fibrillation. Comp Biol Med 48:66\u201376. https:\/\/doi.org\/10.1016\/j.compbiomed.2014.02.010","DOI":"10.1016\/j.compbiomed.2014.02.010"},{"key":"3449_CR11","doi-asserted-by":"publisher","unstructured":"Safarbali B, Golpayegani S (2019) Nonlinear dynamic approaches to identify atrial fibrillation progression based on topological methods. Biomed Signal Process Control 53:101563. https:\/\/doi.org\/10.1016\/j.bspc.2019.101563","DOI":"10.1016\/j.bspc.2019.101563"},{"issue":"4","key":"3449_CR12","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.bspc.2006.12.003","volume":"1","author":"V Corino","year":"2006","unstructured":"Corino V, Sassi R et al (2006) Signal processing methods for information enhancement in atrial fibrillation: spectral analysis and non-linear parameters. Biomed Signal Process Control 1(4):271\u2013281. https:\/\/doi.org\/10.1016\/j.bspc.2006.12.003","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"3449_CR13","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1111\/j.1540-8159.2011.03125.x","volume":"34","author":"R Alcaraz","year":"2011","unstructured":"Alcaraz R, Hornero F, Rieta J (2011) Noninvasive time and frequency predictor of long-standing atrial fibrillation early recurrence after electrical cardioversion: predictor of cardioversion outcome. Pacing Clin Electrophys 34(10):1241\u20131250. https:\/\/doi.org\/10.1111\/j.1540-8159.2011.03125.x","journal-title":"Pacing Clin Electrophys"},{"issue":"11","key":"3449_CR14","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1093\/europace\/eul113","volume":"8","author":"A Bollmann","year":"2006","unstructured":"Bollmann A, Husser D et al (2006) Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications. Europace 8(11):911\u2013926. https:\/\/doi.org\/10.1093\/europace\/eul113","journal-title":"Europace"},{"issue":"8","key":"3449_CR15","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1093\/europace\/eur436","volume":"14","author":"L Uldry","year":"2012","unstructured":"Uldry L, Zaen J et al (2012) Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation. Europace 14(8):1125\u20131131. https:\/\/doi.org\/10.1093\/europace\/eur436","journal-title":"Europace"},{"issue":"6","key":"3449_CR16","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1161\/CIRCULATIONAHA.104.517011","volume":"112","author":"P Sanders","year":"2005","unstructured":"Sanders P, Berenfeld O et al (2005) Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans. Circ 112(6):789\u2013797. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.104.517011","journal-title":"Circ"},{"issue":"1","key":"3449_CR17","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1063\/1.166092","volume":"5","author":"S Pincus","year":"1995","unstructured":"Pincus S (1995) Approximate entropy (ApEn) as a complexity measure. Chaos 5(1):110\u2013117. https:\/\/doi.org\/10.1063\/1.166092","journal-title":"Chaos"},{"issue":"6","key":"3449_CR18","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"J Richman","year":"2000","unstructured":"Richman J, Moorman J (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):2039\u20132049. https:\/\/doi.org\/10.1152\/ajpheart.2000.278.6.H2039","journal-title":"Am J Physiol Heart Circ Physiol"},{"issue":"1","key":"3449_CR19","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.cmpb.2010.02.009","volume":"99","author":"R Alcaraz","year":"2010","unstructured":"Alcaraz R, Ab\u00e1solo D et al (2010) Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Comp Meth Prog Biomed 99(1):124\u2013132. https:\/\/doi.org\/10.1016\/j.cmpb.2010.02.009","journal-title":"Comp Meth Prog Biomed"},{"issue":"7","key":"3449_CR20","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s11517-008-0348-5","volume":"46","author":"R Alcaraz","year":"2008","unstructured":"Alcaraz R, Rieta J (2008) A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation. Med Biol Eng Comp 46(7):625\u2013635. https:\/\/doi.org\/10.1007\/s11517-008-0348-5","journal-title":"Med Biol Eng Comp"},{"key":"3449_CR21","doi-asserted-by":"publisher","unstructured":"Costa M, Peng C-K, et al (2003) Multiscale entropy analysis of human gait dynamics. Phys A Stat Mech Appl 330:53\u201360. https:\/\/doi.org\/10.1016\/j.physa.2003.08.022","DOI":"10.1016\/j.physa.2003.08.022"},{"key":"3449_CR22","doi-asserted-by":"publisher","unstructured":"Costa M, Goldberger A, Peng C-K (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 89:068102. https:\/\/doi.org\/10.1103\/PhysRevLett.89.068102","DOI":"10.1103\/PhysRevLett.89.068102"},{"issue":"3","key":"3449_CR23","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.3390\/e15031069","volume":"15","author":"S Wu","year":"2013","unstructured":"Wu S, Wu C et al (2013) Time series analysis using composite multiscale entropy. Entropy 15(3):1069\u20131084. https:\/\/doi.org\/10.3390\/e15031069","journal-title":"Entropy"},{"issue":"9","key":"3449_CR24","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/TBME.2009.2021986","volume":"56","author":"J Valencia","year":"2009","unstructured":"Valencia J, Porta A et al (2009) Refined multiscale entropy: application to 24-h Holter recordings of heart period variability in healthy and aortic stenosis subjects. IEEE Trans Biomed Eng 56(9):2202\u20132213. https:\/\/doi.org\/10.1109\/TBME.2009.2021986","journal-title":"IEEE Trans Biomed Eng"},{"issue":"12","key":"3449_CR25","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1016\/S0002-9149(98)00210-0","volume":"81","author":"A Bollmann","year":"1998","unstructured":"Bollmann A, Kanuru N et al (1998) Frequency analysis of human atrial fibrillation using the surface electrocardiogram and its response to ibutilide. Am J Cardiol 81(12):1439\u20131445. https:\/\/doi.org\/10.1016\/S0002-9149(98)00210-0","journal-title":"Am J Cardiol"},{"issue":"2","key":"3449_CR26","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.jelectrocard.2011.09.001","volume":"45","author":"W Rademacher","year":"2012","unstructured":"Rademacher W, Seeck A et al (2012) Multidimensional ECG-based analysis of cardiac autonomic regulation predicts early AF recurrence after electrical cardioversion. J Electrocardiol 45(2):116\u2013122. https:\/\/doi.org\/10.1016\/j.jelectrocard.2011.09.001","journal-title":"J Electrocardiol"},{"key":"3449_CR27","doi-asserted-by":"publisher","unstructured":"Meo M, Zarzoso Vao (2013) Catheter ablation outcome prediction in persistent atrial fibrillation using weighted principal component analysis. Biomed Signal Process Control 8(6):958\u2013968. https:\/\/doi.org\/10.1016\/j.bspc.2013.02.002","DOI":"10.1016\/j.bspc.2013.02.002"},{"issue":"1","key":"3449_CR28","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TBME.2012.2220639","volume":"60","author":"M Meo","year":"2013","unstructured":"Meo M, Zarzoso V et al (2013) Spatial variability of the 12-lead surface ECG as a tool for noninvasive prediction of catheter ablation outcome in persistent atrial fibrillation. IEEE Trans Biomed Eng 60(1):20\u201327. https:\/\/doi.org\/10.1109\/TBME.2012.2220639","journal-title":"IEEE Trans Biomed Eng"},{"issue":"5","key":"3449_CR29","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.jjcc.2015.06.006","volume":"66","author":"N Sasaki","year":"2015","unstructured":"Sasaki N, Okumura Y et al (2015) Frequency analysis of atrial fibrillation from the specific ECG leads V7\u2013V9: a lower DF in lead V9 is a marker of potential atrial remodeling. J Cardiol 66(5):388\u2013394. https:\/\/doi.org\/10.1016\/j.jjcc.2015.06.006","journal-title":"J Cardiol"},{"key":"3449_CR30","doi-asserted-by":"crossref","unstructured":"S\u00f6rnmo L, Laguna P (2005) ECG signal processing in bioelectrical signal processing in cardiac and neurological applications. Elsevier Academic Press, ???","DOI":"10.1016\/B978-012437552-9\/50007-6"},{"key":"3449_CR31","doi-asserted-by":"publisher","unstructured":"Vest A, Poian G et al (2018) An open source benchmarked toolbox for cardiovascular waveform and interval analysis. Physiol Meas 39(10):105004. https:\/\/doi.org\/10.1088\/1361-6579\/aae021","DOI":"10.1088\/1361-6579\/aae021"},{"issue":"1","key":"3449_CR32","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/10.900266","volume":"48","author":"M Stridh","year":"2001","unstructured":"Stridh M, S\u00f6rnmo L (2001) Spatiotemporal qrst cancellation techniques for analysis of atrial fibrillation. IEEE Trans Biomed Eng 48(1):105\u2013111. https:\/\/doi.org\/10.1109\/10.900266","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"3449_CR33","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1088\/0967-3334\/31\/1\/008","volume":"31","author":"R Alcaraz","year":"2010","unstructured":"Alcaraz R, Rieta J (2010) The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation. Physio Meas 31(1):115\u2013130. https:\/\/doi.org\/10.1088\/0967-3334\/31\/1\/008","journal-title":"Physio Meas"},{"issue":"2","key":"3449_CR34","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.nonrwa.2009.01.047","volume":"11","author":"R Alcaraz","year":"2010","unstructured":"Alcaraz R, Rieta J (2010) A novel application of sample entropy to the electrocardiogram of atrial fibrillation. Nonlinear Anal Real World Appl 11(2):1026\u20131035. https:\/\/doi.org\/10.1016\/j.nonrwa.2009.01.047","journal-title":"Nonlinear Anal Real World Appl"},{"key":"3449_CR35","doi-asserted-by":"publisher","unstructured":"Nardelli M, Scilingo E, Valenza G (2019) Multichannel Complexity Index (MCI) for a multi-organ physiological complexity assessment. Phys A Statisl Mech Appl 530:121543. https:\/\/doi.org\/10.1016\/j.physa.2019.121543","DOI":"10.1016\/j.physa.2019.121543"},{"key":"3449_CR36","doi-asserted-by":"publisher","unstructured":"Escudero J, Ab\u00e1solo D, et al. Analysis of electroencephalograms in alzheimer\u2019s disease patients with multiscale entropy. Physiol Meas 27(11):1091 https:\/\/doi.org\/10.1088\/0967-3334\/27\/11\/004","DOI":"10.1088\/0967-3334\/27\/11\/004"},{"key":"3449_CR37","doi-asserted-by":"publisher","unstructured":"Frank J (1951) The kolmogorov-smirnov test for goodness of fit. J Am Stat Assoc 46(253):68\u201378. https:\/\/doi.org\/10.1080\/01621459.1951.10500769","DOI":"10.1080\/01621459.1951.10500769"},{"key":"3449_CR38","doi-asserted-by":"publisher","unstructured":"Williams L, Quave K (2019) Chapter 7 - Comparing Two Groups: t-Tests, pp 89\u2013104. Academic Press, ???. https:\/\/doi.org\/10.1016\/C2016-0-02554-8","DOI":"10.1016\/C2016-0-02554-8"},{"key":"3449_CR39","unstructured":"Gibbons J, Chakraborti S (2011) Chapter 6 - The General Two Sample Problem, 5. ed edn. Statistics: textbooks and monographs, vol 198. Chapman & Hall\/CRC Press, ???"},{"issue":"9","key":"3449_CR40","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","volume":"5","author":"JN Mandrekar","year":"2010","unstructured":"Mandrekar JN (2010) Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 5(9):1315\u20131316. https:\/\/doi.org\/10.1097\/JTO.0b013e3181ec173d","journal-title":"J Thorac Oncol"},{"issue":"1","key":"3449_CR41","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1214\/ss\/1177013815","volume":"1","author":"B Efron","year":"1986","unstructured":"Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1(1):54\u201375. https:\/\/doi.org\/10.1214\/ss\/1177013815","journal-title":"Stat Sci"},{"key":"3449_CR42","unstructured":"Harrel F (2015) Chapter 5 - Describing, Resampling, Validating, and Simpligying the Model, pp 106\u2013123. Springer, ???"},{"issue":"1","key":"3449_CR43","first-page":"23","volume":"16","author":"Y Hwang","year":"2018","unstructured":"Hwang Y, Hung Y et al (2018) Finding the optimal threshold of a parametric roc curve under a continuous diagnostic measurement. Stat J 16(1):23\u201343","journal-title":"Stat J"},{"key":"3449_CR44","doi-asserted-by":"publisher","unstructured":"Alcaraz R, Rieta J (2009) Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion. Physiol meas 30:479\u2013489. https:\/\/doi.org\/10.1088\/0967-3334\/30\/5\/005","DOI":"10.1088\/0967-3334\/30\/5\/005"},{"key":"3449_CR45","doi-asserted-by":"publisher","unstructured":"Sterling M, Huang D, Ghoraani B (2015) Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation. Comp Math Meth Med 2015:1\u201310. https:\/\/doi.org\/10.1155\/2015\/527815","DOI":"10.1155\/2015\/527815"},{"issue":"5","key":"3449_CR46","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1016\/j.eupc.2005.04.007","volume":"7","author":"P \u017dohar","year":"2005","unstructured":"\u017dohar P, Kova\u010di\u010d M et al (2005) Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling. Europace 7(5):500\u2013507. https:\/\/doi.org\/10.1016\/j.eupc.2005.04.007","journal-title":"Europace"},{"key":"3449_CR47","doi-asserted-by":"publisher","unstructured":"McLachlan G (1992) Discriminant Analysis and Statistical Pattern Recognition. John Wiley and Sons, Inc., ???. https:\/\/doi.org\/10.1002\/0471725293","DOI":"10.1002\/0471725293"},{"key":"3449_CR48","unstructured":"Rahimi A, Recht B (2008) Random features for large-scale kernel machines. Adv Neural Inf Process Syst 20(null), 1177\u20131184"},{"key":"3449_CR49","doi-asserted-by":"publisher","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(null), 1157\u20131182. https:\/\/doi.org\/10.5555\/944919.944968","DOI":"10.5555\/944919.944968"},{"key":"3449_CR50","doi-asserted-by":"publisher","unstructured":"Amalnerkar E, Lee T, Lim W (2020) Reliability analysis using bootstrap information criterion for small sample size response functions. Struct Multidiscip Optim 62. https:\/\/doi.org\/10.1007\/s00158-020-02724-y","DOI":"10.1007\/s00158-020-02724-y"},{"key":"3449_CR51","doi-asserted-by":"publisher","unstructured":"Liu Q, Chen Y, et al (2015) EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks. Comput Math Methods Med 2015:232381. https:\/\/doi.org\/10.1155\/2015\/232381","DOI":"10.1155\/2015\/232381"},{"issue":"4","key":"3449_CR52","doi-asserted-by":"publisher","first-page":"93808","DOI":"10.1371\/journal.pone.0093808","volume":"9","author":"V Bari","year":"2014","unstructured":"Bari V, Valencia J et al (2014) Multiscale complexity analysis of the cardiac control identifies asymptomatic and symptomatic patients in long qt syndrome type 1. PLoS ONE 9(4):93808. https:\/\/doi.org\/10.1371\/journal.pone.0093808","journal-title":"PLoS ONE"},{"key":"3449_CR53","doi-asserted-by":"publisher","unstructured":"Chenxi L, Chen Y, et al (2016) Complexity analysis of brain activity in attention-deficit\/hyperactivity disorder: A multiscale entropy analysis. Brain Res Bull 124:12\u201320. https:\/\/doi.org\/10.1016\/j.brainresbull.2016.03.007","DOI":"10.1016\/j.brainresbull.2016.03.007"},{"key":"3449_CR54","doi-asserted-by":"publisher","unstructured":"Lankveld T, Vos C et al (2016) Systematic analysis of ecg predictors of sinus rhythm maintenance after electrical cardioversion for persistent atrial fibrillation. Heart Rhythm 13(5):1020\u20131027. https:\/\/doi.org\/10.1016\/j.hrthm.2016.01.004, Accessed 2020-03-31","DOI":"10.1016\/j.hrthm.2016.01.004"},{"key":"3449_CR55","doi-asserted-by":"publisher","unstructured":"Buttu A, Vesin J, et al (2016) A High Baseline Electrographic Organization Level Is Predictive of Successful Termination of Persistent Atrial Fibrillation by Catheter Ablation. JACC: Clin Electrophysiol 2(6):746\u2013755. https:\/\/doi.org\/10.1016\/j.jacep.2016.05.017","DOI":"10.1016\/j.jacep.2016.05.017"},{"key":"3449_CR56","doi-asserted-by":"publisher","unstructured":"Nault I, Lellouche N et al (2009) Clinical value of fibrillatory wave amplitude on surface ecg in patients with persistent atrial fibrillation. J Interv Card Electrophysiol 26(1):11\u201319. https:\/\/doi.org\/10.1007\/s10840-009-9398-3, Accessed 2020-02-10","DOI":"10.1007\/s10840-009-9398-3"},{"issue":"5","key":"3449_CR57","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/e22050531","volume":"22","author":"J Lee","year":"2020","unstructured":"Lee J, Guo Y et al (2020) Towards the development of nonlinear approaches to discriminate af from nsr using a single-lead ecg. Entropy 22(5):531. https:\/\/doi.org\/10.3390\/e22050531","journal-title":"Entropy"},{"key":"3449_CR58","doi-asserted-by":"publisher","unstructured":"Ho Y, Lin C et al (2011) The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure\u2014a pilot study of multiscale entropy. PLoS ONE 6(4):18699. https:\/\/doi.org\/10.1371\/journal.pone.0018699","DOI":"10.1371\/journal.pone.0018699"},{"key":"3449_CR59","doi-asserted-by":"publisher","unstructured":"Cerutti S, Mainardi L, et al (1997) Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes. In: Comp Cardiol 1997, pp 77\u201380. IEEE, ???. https:\/\/doi.org\/10.1109\/CIC.1997.647834","DOI":"10.1109\/CIC.1997.647834"},{"issue":"2","key":"3449_CR60","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/BF02344809","volume":"39","author":"L Mainardi","year":"2001","unstructured":"Mainardi L, Porta A et al (2001) Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes. Med Biol Eng Comput 39(2):249\u2013254. https:\/\/doi.org\/10.1007\/BF02344809","journal-title":"Med Biol Eng Comput"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03449-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03449-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03449-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T05:57:00Z","timestamp":1770098220000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03449-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["3449"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03449-0","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,10]]},"assertion":[{"value":"7 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2025","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 article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclaimer"}}]}}