{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T22:40:38Z","timestamp":1788302438350,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771100"],"award-info":[{"award-number":["61771100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset. This integration enables the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, and specifically its effectiveness in making a new prediction with each new heartbeat. Catering to the potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of the automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments were collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with 100 R-R intervals was achieved, with a sensitivity of 97.12%, a specificity of 97.77%, and an accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding window step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 min in advance. The present results suggest that the sliding window technique on raw R-R interval sequences, along with deep learning-based algorithms, may offer the possibility of providing an accurate, real-time, end-to-end clinical tool for mass monitoring of PAF.<\/jats:p>","DOI":"10.3390\/bioengineering11090903","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:53:03Z","timestamp":1725947583000},"page":"903","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Achieving Real-Time Prediction of Paroxysmal Atrial Fibrillation Onset by Convolutional Neural Network and Sliding Window on R-R Interval Sequences"],"prefix":"10.3390","volume":"11","author":[{"given":"Wenjing","family":"Chen","sequence":"first","affiliation":[{"name":"West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2694-9451","authenticated-orcid":false,"given":"Peirong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Bu","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanning","family":"Xu","sequence":"additional","affiliation":[{"name":"West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dakun","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e275","DOI":"10.1016\/j.hrthm.2017.05.012","article-title":"2017 HRS\/EHRA\/ECAS\/APHRS\/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation","volume":"14","author":"Calkins","year":"2017","journal-title":"Heart Rhythm"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1016\/j.amjcard.2013.05.063","article-title":"Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the US Adult Population","volume":"112","author":"Colilla","year":"2013","journal-title":"Am. J. Cardiol."},{"key":"ref_3","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 Programs Biomed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.joa.2017.05.004","article-title":"2017 consensus of the Asia Pacific Heart Rhythm Society on stroke prevention in atrial fibrillation","volume":"33","author":"Chiang","year":"2017","journal-title":"J. Arrhythmia"},{"key":"ref_5","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":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.physa.2018.06.022","article-title":"Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability","volume":"509","author":"Narin","year":"2018","journal-title":"Phys. A"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"E107","DOI":"10.1001\/jamainternmed.2013.11912","article-title":"Atrial Fibrillation and the Risk of Myocardial Infarction","volume":"174","author":"Soliman","year":"2014","journal-title":"JAMA Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, L.H., Yan, Z.H., Yang, Y.T., Chen, J.Y., Yang, T., Kuo, I.C., Abu, P.A.R., Huang, P.C., Chen, C.A., and Chen, S.L. (2021). A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia. Sensors, 21.","DOI":"10.3390\/s21155222"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/eurheartj\/ehaa612","article-title":"2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)","volume":"42","author":"Hindricks","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_10","first-page":"1067","article-title":"Comparison of pharmacological and electrical cardioversion in permanent atrial fibrillation after prosthetic cardiac valve replacement: A prospective randomized trial","volume":"41","author":"Chen","year":"2013","journal-title":"J. Med. Internet Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s00392-013-0586-x","article-title":"Types and outcomes of cardioversion in patients admitted to hospital for atrial fibrillation: Results of the German RHYTHM-AF Study","volume":"102","author":"Gitt","year":"2013","journal-title":"Clin. Res. Cardiol."},{"key":"ref_12","first-page":"113","article-title":"Predicting the onset of paroxysmal atrial fibrillation","volume":"28","author":"Moody","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_13","first-page":"117","article-title":"Automated assessment of atrial fibrillation","volume":"28","author":"Heneghan","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_14","first-page":"405","article-title":"A two-stage solution algorithm for paroxysmal atrial fibrillation prediction","volume":"28","author":"Lynn","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_15","first-page":"409","article-title":"Some important R-R interval based paroxysmal atrial fibrillation predictors","volume":"28","author":"Krstacic","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_16","first-page":"129","article-title":"Screening and prediction of paroxysmal atrial fibrillation by analysis of heart rate variability parameters","volume":"28","author":"Maier","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_17","first-page":"401","article-title":"Prediction of paroxysmal atrial fibrillation by footprint analysis","volume":"28","author":"Yang","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_18","first-page":"121","article-title":"Can paroxysmal atrial fibrillation be predicted?","volume":"28","author":"Langley","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_19","first-page":"125","article-title":"A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis","volume":"28","author":"Zong","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.compbiomed.2016.03.015","article-title":"Rhythm-based heartbeat duration normalization for atrial fibrillation detection","volume":"72","author":"Islam","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_21","first-page":"133","article-title":"An automatic ECG processing algorithm to identify patients prone to paroxysmal atrial fibrillation","volume":"28","author":"Schreier","year":"2001","journal-title":"Comput. Cardiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/JBHI.2018.2832610","article-title":"PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Stream Physiological Waveform","volume":"23","author":"Sutton","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/JBHI.2018.2858789","article-title":"Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings","volume":"22","author":"Fan","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_25","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."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"035006","DOI":"10.1088\/1361-6579\/aaaa9d","article-title":"A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length","volume":"39","author":"Kamaleswaran","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"S18","DOI":"10.1016\/j.jelectrocard.2018.08.008","article-title":"Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings","volume":"51","author":"Rubin","year":"2018","journal-title":"J. Electrocardiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1016\/j.amjcard.2011.01.028","article-title":"A Simple Method to Detect Atrial Fibrillation Using RR Intervals","volume":"107","author":"Lian","year":"2011","journal-title":"Am. J. Cardiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TBME.2003.820331","article-title":"Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis","volume":"51","author":"Stridh","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/JBHI.2016.2625338","article-title":"An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation","volume":"21","author":"Roonizi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1111\/j.1468-0394.2008.00486.x","article-title":"Detection and delineation of P and T waves in 12-lead electrocardiograms","volume":"26","author":"Mehta","year":"2009","journal-title":"Expert Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ladavich, S., and Ghoraani, B. (2014, January 26\u201330). Developing An Atrial Activity-Based Algorithm For Detection Of Atrial Fibrillation. Proceedings of the IEEE 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6943527"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.jelectrocard.2016.07.033","article-title":"Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification","volume":"49","author":"Kennedy","year":"2016","journal-title":"J. Electrocardiol."},{"key":"ref_34","doi-asserted-by":"crossref","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","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_36","first-page":"354","article-title":"Heart rate variability-Standards of measurement, physiological interpretation, and clinical use","volume":"93","author":"Camm","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, J., Chen, H., Zhang, Q., Yang, R., Peng, S., Wu, Z., Liu, N., Tang, L., Liu, Z., and Zhou, S. (2023). Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study. Diagnostics, 13.","DOI":"10.3390\/diagnostics13061207"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lee, J.-D., Kuo, Y.-W., Lee, C.-P., Huang, Y.-C., Lee, M., and Lee, T.-H. (2022). Development and Validation of a Novel Score for Predicting Paroxysmal Atrial Fibrillation in Acute Ischemic Stroke. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19127277"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/9\/903\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:52:48Z","timestamp":1760111568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/9\/903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["bioengineering11090903"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11090903","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,10]]}}}