{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:14:39Z","timestamp":1753874079613,"version":"3.41.2"},"reference-count":39,"publisher":"AIP Publishing","issue":"12","content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:p>We present an integrated approach to analyze the multi-lead electrocardiogram (ECG) data using the framework of multiplex recurrence networks (MRNs). We explore how their intralayer and interlayer topological features can capture the subtle variations in the recurrence patterns of the underlying spatio-temporal dynamics of the cardiac system. We find that MRNs from ECG data of healthy cases are significantly more coherent with high mutual information and less divergence between respective degree distributions. In cases of diseases, significant differences in specific measures of similarity between layers are seen. The coherence is affected most in the cases of diseases associated with localized abnormality such as bundle branch block. We note that it is important to do a comprehensive analysis using all the measures to arrive at disease-specific patterns. Our approach is very general and as such can be applied in any other domain where multivariate or multi-channel data are available from highly complex systems.<\/jats:p>","DOI":"10.1063\/5.0026954","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T12:32:22Z","timestamp":1606825942000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":5,"title":["Multiplex recurrence networks from multi-lead ECG data"],"prefix":"10.1063","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0418-376X","authenticated-orcid":false,"given":"Sneha","family":"Kachhara","sequence":"first","affiliation":[{"name":"Indian Institute of Science Education and Research (IISER) Tirupati , Tirupati 517507, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7303-9692","authenticated-orcid":false,"given":"G.","family":"Ambika","sequence":"additional","affiliation":[{"name":"Indian Institute of Science Education and Research (IISER) Tirupati , Tirupati 517507, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"317","published-online":{"date-parts":[[2020,12,1]]},"reference":[{"key":"2023063022253272900_c1","doi-asserted-by":"publisher","first-page":"028501","DOI":"10.1063\/1.3156832","article-title":"Introduction to controversial topics in nonlinear science: Is the normal heart rate chaotic?","volume":"19","year":"2009","journal-title":"Chaos"},{"key":"2023063022253272900_c2","doi-asserted-by":"publisher","first-page":"093701","DOI":"10.1063\/1.5003940","article-title":"Introduction to focus issue: Complex cardiac dynamics","volume":"27","year":"2017","journal-title":"Chaos"},{"volume-title":"Advances in Cardiac Signal Processing","year":"2007","key":"2023063022253272900_c3"},{"volume-title":"12-Lead ECG: The Art of Interpretation","year":"2013","key":"2023063022253272900_c4"},{"key":"2023063022253272900_c5","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: A review","volume":"44","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"2023063022253272900_c6","doi-asserted-by":"publisher","first-page":"15127","DOI":"10.1038\/s41598-017-15498-z","article-title":"Detecting abnormality in heart dynamics from multifractal analysis of ECG signals","volume":"7","year":"2017","journal-title":"Sci. Rep."},{"key":"2023063022253272900_c7","doi-asserted-by":"publisher","first-page":"033105","DOI":"10.1063\/1.4941374","article-title":"Complexity in congestive heart failure: A time-frequency approach","volume":"26","year":"2016","journal-title":"Chaos"},{"key":"2023063022253272900_c8","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.pbiomolbio.2010.11.001","article-title":"Chaos in the genesis and maintenance of cardiac arrhythmias","volume":"105","year":"2011","journal-title":"Prog. Biophys. Mol. Biol."},{"key":"2023063022253272900_c9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.physrep.2014.05.002","article-title":"Nonlinear and stochastic dynamics in the heart","volume":"543","year":"2014","journal-title":"Phys. Rep."},{"key":"2023063022253272900_c10","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1093\/europace\/eul031","article-title":"Decrease in the heart rate complexity prior to the onset of atrial fibrillation","volume":"8","year":"2006","journal-title":"Europace"},{"key":"2023063022253272900_c11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2018.10.005","article-title":"Complex network approaches to nonlinear time series analysis","volume":"787","year":"2019","journal-title":"Phys. Rep."},{"key":"2023063022253272900_c12","doi-asserted-by":"publisher","first-page":"50001","DOI":"10.1209\/0295-5075\/116\/50001","article-title":"Complex network analysis of time series","volume":"116","year":"2017","journal-title":"Europhys. Lett."},{"key":"2023063022253272900_c13","doi-asserted-by":"publisher","first-page":"033025","DOI":"10.1088\/1367-2630\/12\/3\/033025","article-title":"Recurrence networks\u2014A novel paradigm for nonlinear time series analysis","volume":"12","year":"2010","journal-title":"New J. Phys."},{"key":"2023063022253272900_c14","doi-asserted-by":"publisher","first-page":"60004","DOI":"10.1209\/0295-5075\/127\/60004","article-title":"Bimodality and scaling in recurrence networks from ECG data","volume":"127","year":"2019","journal-title":"Europhys. Lett."},{"key":"2023063022253272900_c15","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.jelectrocard.2015.05.002","article-title":"Vectorcardiographic diagnostic & prognostic information derived from the 12-lead electrocardiogram: Historical review and clinical perspective","volume":"48","year":"2015","journal-title":"J. Electrocardiol."},{"key":"2023063022253272900_c16","doi-asserted-by":"publisher","first-page":"012312","DOI":"10.1103\/PhysRevE.97.012312","article-title":"Multiplex recurrence networks","volume":"97","year":"2018","journal-title":"Phys. Rev. E"},{"key":"2023063022253272900_c17","doi-asserted-by":"publisher","DOI":"10.1002\/esp.5004","article-title":"Holocene climate forcings and lacustrine regime shifts in the indian summer monsoon realm","year":"2020","journal-title":"Earth Surf. Processes Landforms"},{"key":"2023063022253272900_c18","doi-asserted-by":"publisher","first-page":"035809","DOI":"10.1063\/1.4977950","article-title":"Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior","volume":"27","year":"2017","journal-title":"Chaos"},{"key":"2023063022253272900_c19","doi-asserted-by":"publisher","first-page":"3982","DOI":"10.1109\/TII.2017.2785384","article-title":"A novel multiplex network-based sensor information fusion model and its application to industrial multiphase flow system","volume":"14","year":"2017","journal-title":"IEEE Trans. Ind. Inf."},{"key":"2023063022253272900_c20","doi-asserted-by":"publisher","first-page":"113126","DOI":"10.1063\/1.5120538","article-title":"A recurrence network-based convolutional neural network for fatigue driving detection from EEG","volume":"29","year":"2019","journal-title":"Chaos"},{"key":"2023063022253272900_c21","doi-asserted-by":"publisher","first-page":"5841609","DOI":"10.1155\/2020\/5841609","article-title":"Information linkage between carbon and energy markets: Multiplex recurrence network approach","volume":"2020","year":"2020","journal-title":"Complexity"},{"key":"2023063022253272900_c22","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3389\/fams.2020.00009","article-title":"Studying complex adaptive systems with internal states: A recurrence network approach to the analysis of multivariate time-series data representing self-reports of human experience","volume":"6","year":"2020","journal-title":"Front. Appl. Math. Stat."},{"key":"2023063022253272900_c23","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1515\/bmte.1995.40.s1.317","article-title":"Nutzung der ekg-signaldatenbank cardiodat der ptb \u00fcber das internet","volume":"40","year":"1995","journal-title":"Biomed. Tech.\/Biomed. Eng."},{"key":"2023063022253272900_c24","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1515\/bmte.1995.40.s1.319","article-title":"Automatisierte ekg-auswertung mit hilfe der ekg-signaldatenbank cardiodat der ptb","volume":"40","year":"1995","journal-title":"Biomed. Tech.\/Biomed. Eng."},{"key":"2023063022253272900_c25","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals","volume":"101","year":"2000","journal-title":"Circulation"},{"key":"2023063022253272900_c26","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.bspc.2018.03.003","article-title":"A survey on ECG analysis","volume":"43","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"volume-title":"Nonlinear Time Series Analysis","year":"2004","key":"2023063022253272900_c27"},{"key":"2023063022253272900_c28"},{"key":"2023063022253272900_c29","doi-asserted-by":"publisher","first-page":"085720","DOI":"10.1063\/1.5024914","article-title":"Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions","volume":"28","year":"2018","journal-title":"Chaos"},{"key":"2023063022253272900_c30","doi-asserted-by":"publisher","first-page":"015101","DOI":"10.1103\/PhysRevE.81.015101","article-title":"Ambiguities in recurrence-based complex network representations of time series","volume":"81","year":"2010","journal-title":"Phys. Rev. E"},{"key":"2023063022253272900_c31","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1063\/1.166424","article-title":"Practical implementation of nonlinear time series methods: The tisean package","volume":"9","year":"1999","journal-title":"Chaos"},{"key":"2023063022253272900_c32","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1140\/epjst\/e2008-00833-5","article-title":"Selection of recurrence threshold for signal detection","volume":"164","year":"2008","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"2023063022253272900_c33","doi-asserted-by":"publisher","first-page":"012202","DOI":"10.1103\/PhysRevE.93.012202","article-title":"Uniform framework for the recurrence-network analysis of chaotic time series","volume":"93","year":"2016","journal-title":"Phys. Rev. E"},{"key":"2023063022253272900_c34","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.physrep.2006.11.001","article-title":"Recurrence plots for the analysis of complex systems","volume":"438","year":"2007","journal-title":"Phys. Rep."},{"key":"2023063022253272900_c35","doi-asserted-by":"publisher","first-page":"171747","DOI":"10.1098\/rsos.171747","article-title":"Quantifying layer similarity in multiplex networks: A systematic study","volume":"5","year":"2018","journal-title":"R. Soc. Open Sci."},{"key":"2023063022253272900_c36","doi-asserted-by":"publisher","first-page":"15508","DOI":"10.1038\/srep15508","article-title":"Network structure of multivariate time series","volume":"5","year":"2015","journal-title":"Sci. Rep."},{"volume-title":"Networks: An Introduction","year":"2010","key":"2023063022253272900_c37"},{"volume-title":"Statistical Inference Based on Divergence Measures","year":"2018","key":"2023063022253272900_c38"},{"volume-title":"Research Design and Statistical Analysis","year":"2010","key":"2023063022253272900_c39"}],"container-title":["Chaos: An Interdisciplinary Journal of Nonlinear Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.aip.org\/aip\/cha\/article-pdf\/doi\/10.1063\/5.0026954\/14107550\/123106_1_online.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.aip.org\/aip\/cha\/article-pdf\/doi\/10.1063\/5.0026954\/14107550\/123106_1_online.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T22:25:44Z","timestamp":1688163944000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/cha\/article\/30\/12\/123106\/282729\/Multiplex-recurrence-networks-from-multi-lead-ECG"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":39,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12,1]]}},"URL":"https:\/\/doi.org\/10.1063\/5.0026954","relation":{},"ISSN":["1054-1500","1089-7682"],"issn-type":[{"type":"print","value":"1054-1500"},{"type":"electronic","value":"1089-7682"}],"subject":[],"published-other":{"date-parts":[[2020,12]]},"published":{"date-parts":[[2020,12,1]]},"article-number":"123106"}}