{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T08:00:16Z","timestamp":1784448016162,"version":"3.55.0"},"reference-count":65,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Med. Eng. Phys."],"published-print":{"date-parts":[[2022,12,1]]},"DOI":"10.1016\/j.medengphy.2022.103811","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T21:47:34Z","timestamp":1651096054000},"page":"103811","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals"],"prefix":"10.1088","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6942-3464","authenticated-orcid":true,"given":"Naimahmed","family":"Nesaragi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashish","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0306-5224","authenticated-orcid":true,"given":"Shivnarayan","family":"Patidar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"bib0001","first-page":"12","type":"other","article-title":"European society of cardiology: cardiovascular disease statistics 2019","volume":"41","author":"Timmis","year":"2020"},{"key":"bib0002","first-page":"S1","type":"other","article-title":"The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues: a report from the american college of cardiology","volume":"60","author":"Laslett","year":"2012"},{"key":"bib0003","first-page":"2043","type":"other","article-title":"Preventing coronary artery disease by lowering cholesterol levels: fifty years from bench to bedside","volume":"282","author":"Steinberg","year":"1999"},{"key":"bib0004","first-page":"1648","type":"other","article-title":"Protecting athletes from sudden cardiac death","volume":"296","author":"Thompson","year":"2006"},{"key":"bib0005","author":"Bonow","year":"2011","type":"other"},{"key":"bib0006","author":"Kern","year":"2015","type":"other"},{"key":"bib0007","first-page":"103346","type":"other","article-title":"Machine learning-based coronary artery disease diagnosis: a comprehensive review","volume":"111","author":"Alizadehsani","year":"2019"},{"key":"bib0008","first-page":"9","author":"Alizadehsani","year":"2012","type":"other"},{"key":"bib0009","first-page":"104095","type":"other","article-title":"Coronary artery disease detection using artificial intelligence techniques: a survey of trends, geographical differences and diagnostic features 1991\u20132020","volume":"128","author":"Alizadehsani","year":"2020"},{"key":"bib0010","first-page":"373","type":"other","article-title":"Entropies for automated detection of coronary artery disease using ECG signals: a review","volume":"38","author":"Acharya","year":"2018"},{"key":"bib0011","first-page":"117","type":"other","article-title":"Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM","volume":"138","author":"Dolatabadi","year":"2017"},{"key":"bib0012","first-page":"301","type":"other","article-title":"Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals","volume":"31","author":"Kumar","year":"2017"},{"key":"bib0013","first-page":"31","type":"other","article-title":"Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals","volume":"31","author":"Acharya","year":"2017"},{"key":"bib0014","first-page":"62","type":"other","article-title":"Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network","volume":"132","author":"Acharya","year":"2017"},{"key":"bib0015","first-page":"1","type":"other","article-title":"Automated diagnosis of coronary artery disease using tunable-q wavelet transform applied on heart rate signals","volume":"82","author":"Patidar","year":"2015"},{"key":"bib0016","first-page":"19","type":"other","article-title":"Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals","volume":"94","author":"Tan","year":"2018"},{"key":"bib0017","first-page":"3912","type":"other","article-title":"Joint time-frequency domain-based CAD disease sensing system using ECG signals","volume":"19","author":"Sharma","year":"2019"},{"key":"bib0018","first-page":"58","type":"other","article-title":"An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank","volume":"98","author":"Sharma","year":"2018"},{"key":"bib0019","first-page":"2016282","type":"other","article-title":"Automated diagnosis of coronary artery disease: a review and workflow","volume":"2018","author":"Wah","year":"2018"},{"key":"bib0020","first-page":"1","type":"other","article-title":"A database for using machine learning and data mining techniques for coronary artery disease diagnosis","volume":"6","author":"Alizadehsani","year":"2019"},{"key":"bib0021","first-page":"2773","type":"other","article-title":"Tensor decompositions for learning latent variable models","volume":"15","author":"Anandkumar","year":"2014"},{"key":"bib0022","first-page":"104430","type":"other","article-title":"Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis","volume":"134","author":"Nesaragi","year":"2021"},{"key":"bib0023","first-page":"1013","type":"other","article-title":"A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data","volume":"41","author":"Nesaragi","year":"2021"},{"key":"bib0024","first-page":"119","type":"other","article-title":"Automatic ECG classification using continuous wavelet transform and convolutional neural network","volume":"23","author":"Wang","year":"2021"},{"key":"bib0025","first-page":"230","type":"other","article-title":"A real-time QRS detection algorithm","author":"Pan","year":"1985"},{"key":"bib0026","first-page":"333","type":"other","article-title":"Integer coefficient bandpass filter for the simultaneous removal of baseline wander, 50 and 100\u00a0Hz interference from the ECG","volume":"29","author":"Warlar","year":"1991"},{"key":"bib0027","first-page":"1","type":"other","article-title":"Analysis of cardiac signals using spatial filling index and time-frequency domain","volume":"3","author":"Faust","year":"2004"},{"key":"bib0028","first-page":"510","type":"other","article-title":"Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index","volume":"43","author":"Fujita","year":"2016"},{"key":"bib0029","first-page":"23","type":"other","article-title":"An algorithm based on the continuous wavelet transform with splines for the automatic measurement of QT dispersion: validation and application in chronic kidney disease","author":"de Lourdes Corzo-Cuesta","year":"2018"},{"key":"bib0030","first-page":"81","type":"other","article-title":"A new mathematical based QRS detector using continuous wavelet transform","volume":"34","author":"Ghaffari","year":"2008"},{"key":"bib0031","first-page":"439","author":"Senhadji","year":"2017","type":"other"},{"key":"bib0032","first-page":"1","type":"other","article-title":"The continuous wavelet transform, an analysis tool for NMR spectroscopy","volume":"128","author":"Barache","year":"1997"},{"key":"bib0033","first-page":"409","author":"Meintjes","year":"2018","type":"other"},{"key":"bib0034","first-page":"1291","type":"other","article-title":"Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems","volume":"18","author":"Guo","year":"2017"},{"key":"bib0035","first-page":"P19","type":"other","article-title":"Spectral decomposition of seismic data with continuous-wavelet transform","volume":"70","author":"Sinha","year":"2005"},{"key":"bib0036","first-page":"170820","type":"other","article-title":"A novel method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks","volume":"7","author":"Wu","year":"2019"},{"key":"bib0037","author":"Vetterli","year":"1995","type":"other"},{"key":"bib0038","first-page":"2","author":"Grossmann","year":"1990","type":"other"},{"key":"bib0039","first-page":"149","type":"other","article-title":"Parafac. Tutorial and applications","volume":"38","author":"Bro","year":"1997"},{"key":"bib0040","first-page":"1","type":"other","article-title":"Tensors for data mining and data fusion: models, applications, and scalable algorithms","volume":"8","author":"Papalexakis","year":"2016"},{"key":"bib0041","first-page":"117","type":"other","article-title":"A practical introduction to tensor networks: matrix product states and projected entangled pair states","volume":"349","author":"Or\u00fas","year":"2014"},{"key":"bib0042","type":"other"},{"key":"bib0043","first-page":"635","type":"other","article-title":"Algorithm 862: MATLAB tensor classes for fast algorithm prototyping","volume":"32","author":"Bader","year":"2006"},{"key":"bib0044","first-page":"21","type":"other","article-title":"Ensemble based systems in decision making","volume":"6","author":"Polikar","year":"2006"},{"key":"bib0045","first-page":"1","type":"other","article-title":"Most tensor problems are np-hard","volume":"60","author":"Hillar","year":"2013"},{"key":"bib0046","first-page":"71","type":"other","article-title":"Third-order tensor based analysis of multilead ECGfor classification of myocardial infarction","volume":"31","author":"Padhy","year":"2017"},{"key":"bib0047","first-page":"1","author":"Hoang","year":"2019","type":"other"},{"key":"bib0048","first-page":"112","type":"other","article-title":"Exploiting multi-lead electrocardiogram correlations using robust third-order tensor decomposition","volume":"2","author":"Padhy","year":"2015"},{"key":"bib0049","first-page":"102032","type":"other","article-title":"Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care","volume":"113","author":"Hernandez","year":"2021"},{"key":"bib0050","first-page":"105120","type":"other","article-title":"Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition","volume":"184","author":"Liu","year":"2020"},{"key":"bib0051","first-page":"4","author":"Arafat","year":"2005","type":"other"},{"key":"bib0052","first-page":"198","type":"other","article-title":"Nonlinear dynamics analysis of electrocardiograms for detection of coronary artery disease","volume":"92","author":"Antanavi\u010dius","year":"2008"},{"key":"bib0053","first-page":"132","type":"other","article-title":"Detection of coronary artery disease from the normal resting ECG using nonlinear mathematical transformation","volume":"17","author":"Schreck","year":"1988"},{"key":"bib0054","first-page":"1502","type":"other","article-title":"Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure","volume":"37","author":"\u0130\u015fler","year":"2007"},{"key":"bib0055","first-page":"2225","type":"other","article-title":"Potential of feature selection methods in heart rate variability analysis for the classification of different cardiovascular diseases","volume":"21","author":"Schumann","year":"2002"},{"key":"bib0056","first-page":"347","type":"other","article-title":"Heart rate variability and outcome in acute severe stroke","volume":"1","author":"Gujjar","year":"2004"},{"key":"bib0057","type":"other"},{"key":"bib0058","first-page":"e215","type":"other","article-title":"Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000"},{"key":"bib0059","first-page":"52","type":"other","article-title":"A data mining approach for diagnosis of coronary artery disease","volume":"111","author":"Alizadehsani","year":"2013"},{"key":"bib0060","first-page":"1240017","type":"other","article-title":"Novel classification of coronary artery disease using heart rate variability analysis","volume":"12","author":"Dua","year":"2012"},{"key":"bib0061","first-page":"274","type":"other","article-title":"Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform","volume":"37","author":"Giri","year":"2013"},{"key":"bib0062","first-page":"55","type":"other","article-title":"Linear and nonlinear analysis of normal and CAD-affected heart rate signals","volume":"113","author":"Acharya","year":"2014"},{"key":"bib0063","first-page":"165","type":"other","article-title":"An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals","volume":"63","author":"Kumar","year":"2016"},{"key":"bib0064","first-page":"1640002","type":"other","article-title":"Application of empirical mode decomposition\u2013based features for analysis of normal and CAD heart rate signals","volume":"16","author":"Sood","year":"2016"},{"key":"bib0065","first-page":"13","type":"other","article-title":"QRS detection and heart rate variability analysis: asurvey","volume":"2","author":"Oweis","year":"2014"}],"container-title":["Medical Engineering &amp; Physics"],"original-title":[],"link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1350453322000601?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1350453322000601?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1016\/j.medengphy.2022.103811","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1016\/j.medengphy.2022.103811\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1016\/j.medengphy.2022.103811\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1016\/j.medengphy.2022.103811\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:58:15Z","timestamp":1769093895000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1016\/j.medengphy.2022.103811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,4,27]]},"published-print":{"date-parts":[[2022,12,1]]}},"URL":"https:\/\/doi.org\/10.1016\/j.medengphy.2022.103811","relation":{},"ISSN":["1350-4533","1873-4030"],"issn-type":[{"value":"1350-4533","type":"print"},{"value":"1873-4030","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,27]]},"assertion":[{"value":"Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals","name":"article_title","label":"Article Title"},{"value":"Medical Engineering & Physics","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2022 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2021-09-09","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2022-04-25","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2022-04-27","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}