{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T01:20:46Z","timestamp":1789348846911,"version":"build-2803163510"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1016\/j.cmpb.2021.106396","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T02:48:13Z","timestamp":1631069293000},"page":"106396","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":34,"special_numbering":"C","title":["Paroxysmal atrial fibrillation prediction based on morphological variant P-wave analysis with wideband ECG and deep learning"],"prefix":"10.1016","volume":"211","author":[{"given":"Heng-An","family":"Tzou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1270-3961","authenticated-orcid":false,"given":"Shien-Fong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng-Sheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.cmpb.2021.106396_bib0001","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1378\/chest.09-1584","article-title":"Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation","volume":"137","author":"Lip","year":"2010","journal-title":"Chest"},{"issue":"8","key":"10.1016\/j.cmpb.2021.106396_bib0002","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1161\/STROKEAHA.117.017293","article-title":"P-wave indices and risk of ischemic stroke: a systematic review and meta-analysis","volume":"48","author":"He","year":"2017","journal-title":"Stroke"},{"issue":"1","key":"10.1016\/j.cmpb.2021.106396_bib0003","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/BF02347697","article-title":"Automatic P-wave analysis of patients prone to atrial fibrillation","volume":"40","author":"Clavier","year":"2002","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.cmpb.2021.106396_bib0004","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.cmpb.2017.10.012","article-title":"Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III","volume":"153","author":"Boon","year":"2018","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.cmpb.2021.106396_bib0005","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cmpb.2018.07.014","article-title":"Prediction of paroxysmal atrial fibrillation: a machine learning based approach using combined feature vector and mixture of expert classification on HRV signal","volume":"165","author":"Ebrahimzadeh","year":"2018","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"1","key":"10.1016\/j.cmpb.2021.106396_bib0006","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"issue":"10201","key":"10.1016\/j.cmpb.2021.106396_bib0007","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction","volume":"394","author":"Attia","year":"2019","journal-title":"Lancet"},{"issue":"8","key":"10.1016\/j.cmpb.2021.106396_bib0008","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCEP.119.007952","article-title":"Artificial intelligence and machine learning in arrhythmias and cardiac electrophysiology","volume":"13","author":"Feeny","year":"2020","journal-title":"Circ. Arrhythm. Electrophysiol."},{"issue":"2","key":"10.1016\/j.cmpb.2021.106396_bib0009","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.cmpb.2015.01.006","article-title":"Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation","volume":"119","author":"Alcaraz","year":"2015","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"1","key":"10.1016\/j.cmpb.2021.106396_bib0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep26799","article-title":"P-wave variability and atrial fibrillation","volume":"6","author":"Censi","year":"2016","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2021.106396_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.12970\/2311-052X.2018.06.01","article-title":"P-wave morphology, amplitude, duration and dispersion in atrial arrhythmias","volume":"6","author":"Centuri\u00c3\u00b3n","year":"2018","journal-title":"J. Cardiol. Ther."},{"issue":"8","key":"10.1016\/j.cmpb.2021.106396_bib0012","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1093\/europace\/euaa118","article-title":"Orthogonal P-wave morphology, conventional P-wave indices, and the risk of atrial fibrillation in the general population using data from the Finnish hospital discharge register","volume":"22","author":"Eranti","year":"2020","journal-title":"Europace"},{"key":"10.1016\/j.cmpb.2021.106396_bib0013","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cmpb.2017.08.016","article-title":"Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation","volume":"151","author":"Filos","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"10.1016\/j.cmpb.2021.106396_bib0014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/0967-3334\/35\/1\/1","article-title":"Morphological variability of the P-wave for premature envision of paroxysmal atrial fibrillation events","volume":"35","author":"Mart\u00ednez","year":"2013","journal-title":"Physiol. Meas."},{"issue":"5","key":"10.1016\/j.cmpb.2021.106396_bib0015","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1038\/s41596-020-0316-6","article-title":"Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG)","volume":"15","author":"Kusayama","year":"2020","journal-title":"Nat. Protoc."},{"issue":"7","key":"10.1016\/j.cmpb.2021.106396_bib0016","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.hrthm.2017.03.030","article-title":"Skin sympathetic nerve activity precedes the onset and termination of paroxysmal atrial tachycardia and fibrillation","volume":"14","author":"Uradu","year":"2017","journal-title":"Heart Rhythm."},{"key":"10.1016\/j.cmpb.2021.106396_bib0017","series-title":"Computers in Cardiology","first-page":"217","article-title":"Screening for paroxysmal atrial fibrillation using atrial premature contractions and spectral measures","author":"Hickey","year":"2002"},{"key":"10.1016\/j.cmpb.2021.106396_bib0018","series-title":"Depth-Wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks","author":"He","year":"2019"},{"key":"10.1016\/j.cmpb.2021.106396_bib0019","series-title":"2017 Computing in Cardiology (CinC)","first-page":"1","article-title":"The physionet QT database: study on the reliability of P-wave manual annotations under noisy recordings","author":"Gonz\u00e1lez","year":"2017"},{"key":"10.1016\/j.cmpb.2021.106396_bib0020","series-title":"Proceedings of the International Conference Multirate Systems & Wavelet Analysis","first-page":"1","article-title":"ECG analysis using the mexican-hat wavelet","author":"Burke","year":"2012"},{"key":"10.1016\/j.cmpb.2021.106396_bib0021","series-title":"Very Deep Convolutional Networks For Large-Scale Image Recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.cmpb.2021.106396_bib0022","article-title":"A survey on explainable artificial intelligence (xai): Toward medical xai","author":"Tjoa","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.cmpb.2021.106396_bib0023","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"issue":"1","key":"10.1016\/j.cmpb.2021.106396_bib0024","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.hrthm.2016.09.019","article-title":"Simultaneous noninvasive recording of skin sympathetic nerve activity and electrocardiogram","volume":"14","author":"Doytchinova","year":"2017","journal-title":"Heart Rhythm."},{"key":"10.1016\/j.cmpb.2021.106396_bib0025","doi-asserted-by":"crossref","unstructured":"D. Calvo, D. Filgueiras-Rama, and J. Jalife, Propagation of sinus waves in the atrial architecture: when laminar electrical fluxes turn turbulent, ed: Am Heart Assoc, 2017.","DOI":"10.1161\/CIRCEP.117.005699"},{"key":"10.1016\/j.cmpb.2021.106396_bib0026","first-page":"401","article-title":"Prediction of paroxysmal atrial fibrillation by footprint analysis","volume":"28","author":"Yang","year":"2001"},{"key":"10.1016\/j.cmpb.2021.106396_bib0027","series-title":"International Workshop on PRedictive Intelligence In MEdicine","first-page":"164","article-title":"Prediction to atrial fibrillation using deep convolutional neural networks","author":"Cho","year":"2018"},{"key":"10.1016\/j.cmpb.2021.106396_bib0028","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2016.07.016","article-title":"Paroxysmal atrial fibrillation prediction method with shorter HRV sequences","volume":"134","author":"Boon","year":"2016","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.cmpb.2021.106396_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.101675","article-title":"A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation","volume":"56","author":"Cao","year":"2020","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260721004703?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260721004703?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:03:41Z","timestamp":1759205021000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260721004703"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":29,"alternative-id":["S0169260721004703"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2021.106396","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Paroxysmal atrial fibrillation prediction based on morphological variant P-wave analysis with wideband ECG and deep learning","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2021.106396","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106396"}}