{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:48:38Z","timestamp":1791607718067,"version":"4.3.4"},"reference-count":36,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T00:00:00Z","timestamp":1693699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea government (MSIT)","award":["RS-2022-00155966"],"award-info":[{"award-number":["RS-2022-00155966"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true\u2013normal sinus rhythm (T-NSR), atrial fibrillation\u2013normal sinus rhythm (AF-NSR), and clinically important arrhythmia\u2013normal sinus rhythm (CIA-NSR). The T-NSR group comprised patients with at least three normal rhythms in a year and no atrial fibrillation or arrhythmias history. Clinically important arrhythmia included atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, ventricular premature contraction, ventricular tachycardia, right and left bundle branch block, and atrioventricular block over the second degree. The AF-NSR group included normal sinus rhythm paired with atrial fibrillation or atrial flutter within 14 days, and the CIA-NSR group comprised normal sinus rhythm paired with CIA occurring within 14 days. Three deep learning models, ResNet-18, LSTM, and Transformer-based models, were utilized to distinguish T-NSR from AF-NSR and T-NSR from CIA-NSR. The experiments demonstrated the potential of using discrete heartbeats in predicting future arrhythmia and atrial fibrillation incidences extracted from 12-lead electrocardiogram (ECG) signals alone, without any additional patient information. The analysis reveals that these discrete heartbeats contain subtle patterns that deep learning models can identify. Focusing on discrete heartbeats may lead to more timely and accurate diagnoses of these conditions, improving patient outcomes and enabling automated diagnosis using ECG signals as a biomarker.<\/jats:p>","DOI":"10.3390\/diagnostics13172849","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T02:26:12Z","timestamp":1693794372000},"page":"2849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods"],"prefix":"10.3390","volume":"13","author":[{"given":"Yehyun","family":"Kim","sequence":"first","affiliation":[{"name":"Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8860-3276","authenticated-orcid":false,"given":"Myeonggyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeung","family":"Yoon","sequence":"additional","affiliation":[{"name":"Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4185-9342","authenticated-orcid":false,"given":"Yeji","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7435-7884","authenticated-orcid":false,"given":"Hyunseok","family":"Min","sequence":"additional","affiliation":[{"name":"Tomocube Inc., Daejeon 34141, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyungjoo","family":"Cho","sequence":"additional","affiliation":[{"name":"Tomocube Inc., Daejeon 34141, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junbeom","family":"Park","sequence":"additional","affiliation":[{"name":"Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea"},{"name":"Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taeyoung","family":"Shin","sequence":"additional","affiliation":[{"name":"Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea"},{"name":"Department of Urology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1161\/01.CIR.89.1.224","article-title":"Asymptomatic Arrhythmias in Patients with Symptomatic Paroxysmal Atrial Fibrillation and Paroxysmal Supraventricular Tachycardia","volume":"89","author":"Page","year":"1994","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1001\/jamacardio.2017.3180","article-title":"Incidence of Previously Undiagnosed Atrial Fibrillation Using Insertable Cardiac Monitors in a High-Risk Population","volume":"2","author":"Reiffel","year":"2017","journal-title":"JAMA Cardiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1056\/NEJMoa1105575","article-title":"Subclinical Atrial Fibrillation and the Risk of Stroke","volume":"366","author":"Healey","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1016\/j.jacc.2015.08.038","article-title":"Arrhythmia-Induced Cardiomyopathies: Mechanisms, Recognition, and Management","volume":"66","author":"Gopinathannair","year":"2015","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1161\/01.STR.16.2.182","article-title":"Non-Rheumatic Atrial Fibrillation as a Risk Factor for Stroke","volume":"16","author":"Britton","year":"1985","journal-title":"Stroke"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1212\/WNL.28.10.973","article-title":"Epidemiologic Assessment of Chronic Atrial Fibrillation and Risk of Stroke: The Fiamingham Study","volume":"28","author":"Wolf","year":"1978","journal-title":"Neurology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/S0002-9343(02)01236-6","article-title":"A Population-Based Study of the Long-Term Risks Associated with Atrial Fibrillation: 20-Year Follow-up of the Renfrew\/Paisley Study","volume":"113","author":"Stewart","year":"2002","journal-title":"Am. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1161\/CIRCULATIONAHA.113.005119","article-title":"Worldwide Epidemiology of Atrial Fibrillation: A Global Burden of Disease 2010 Study","volume":"129","author":"Chugh","year":"2014","journal-title":"Circulation"},{"key":"ref_9","first-page":"e38","article-title":"Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association","volume":"133","author":"Mozaffarian","year":"2016","journal-title":"Circulation"},{"key":"ref_10","unstructured":"(2019). Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J. Am. Coll. Cardiol., 74, 2365\u20132375."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1001\/jama.2018.8102","article-title":"Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation","volume":"320","author":"Steinhubl","year":"2018","journal-title":"JAMA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1161\/CIRCULATIONAHA.120.047829","article-title":"Deep Neural Networks Can Predict New-Onset Atrial Fibrillation from the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation\u2013Related Stroke","volume":"143","author":"Raghunath","year":"2021","journal-title":"Circulation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12818","DOI":"10.1038\/s41598-021-92172-5","article-title":"A New Deep Learning Algorithm of 12-Lead Electrocardiogram for Identifying Atrial Fibrillation during Sinus Rhythm","volume":"11","author":"Baek","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/S0140-6736(19)31719-2","article-title":"AI Can Now Identify Atrial Fibrillation through Sinus Rhythm","volume":"394","author":"Hendriks","year":"2019","journal-title":"Lancet"},{"key":"ref_15","first-page":"1813","article-title":"Electrocardiographic Biomarkers to Predict Atrial Fibrillation in Sinus Rhythm Electrocardiograms","volume":"107","author":"Cecconi","year":"2021","journal-title":"Heart (Br. Card. Soc.)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e009355","DOI":"10.1161\/CIRCEP.120.009355","article-title":"Artificial Intelligence\u2013Electrocardiography to Predict Incident Atrial Fibrillation","volume":"13","author":"Christopoulos","year":"2020","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compbiomed.2018.12.012","article-title":"Automated Beat-Wise Arrhythmia Diagnosis Using Modified U-Net on Extended Electrocardiographic Recordings with Heterogeneous Arrhythmia Types","volume":"105","author":"Oh","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_18","first-page":"422","article-title":"Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks","volume":"45","author":"Degirmenci","year":"2021","journal-title":"IRBM"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/S0002-8703(42)90821-4","article-title":"The AVl, AVr, and AVf Leads","volume":"24","author":"Goldberger","year":"1942","journal-title":"Am. Heart J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6649970","DOI":"10.1155\/2021\/6649970","article-title":"ECG Heartbeat Classification Based on an Improved ResNet-18 Model","volume":"2021","author":"Jing","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"012031","DOI":"10.1088\/1742-6596\/2547\/1\/012031","article-title":"ECG Heartbeat Classification Using Deep Transfer Learning with Convolutional Neural Network and STFT Technique","volume":"2547","author":"Cao","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3233\/THC-212847","article-title":"A Deep Learning Algorithm Based on 1D CNN-LSTM for Automatic Sleep Staging","volume":"30","author":"Zhao","year":"2021","journal-title":"Technol. Health Care"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"261","DOI":"10.5573\/IEIESPC.2020.9.4.261","article-title":"Detection of Arrhythmia Using 1D Convolution Neural Network with LSTM Model","volume":"9","author":"Han","year":"2020","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Che, C., Zhang, P., Zhu, M., Qu, Y., and Jin, B. (2021). Constrained Transformer Network for ECG Signal Processing and Arrhythmia Classification. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01546-2"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"196","DOI":"10.26599\/BDMA.2020.9020004","article-title":"Gradient Amplification: An Efficient Way to Train Deep Neural Networks","volume":"3","author":"Basodi","year":"2020","journal-title":"Big Data Min. Anal."},{"key":"ref_27","unstructured":"Salehinejad, H., Sankar, S., Barfett, J., Colak, E., and Valaee, S. (2018). Recent Advances in Recurrent Neural Networks. arXiv."},{"key":"ref_28","unstructured":"Xu, Y., Zhang, Q., Zhang, J., and Tao, D. (2023, August 29). ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias. Semantic Scholar. Available online: https:\/\/www.semanticscholar.org\/paper\/ViTAE%3A-Vision-Transformer-Advanced-by-Exploring-Xu-Zhang\/576c462dbc1f3d732b919ef1daac37a817123e52."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/978-3-662-44851-9_15","article-title":"Optimal Thresholding of Classifiers to Maximize F1 Measure","volume":"8725","author":"Lipton","year":"2014","journal-title":"Mach. Learn. Knowl. Discov. Databases"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2019.06.025","article-title":"Comparing Ensemble Strategies for Deep Learning: An Application to Facial Expression Recognition","volume":"136","author":"Renda","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"233372141985968","DOI":"10.1177\/2333721419859687","article-title":"Prevalence and Incidence of Atrial Fibrillation and Other Arrhythmias in the General Older Population: Findings from the Swedish National Study on Aging and Care","volume":"5","author":"Lindberg","year":"2019","journal-title":"Gerontol. Geriatr. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"754909","DOI":"10.3389\/fcvm.2022.754909","article-title":"Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders","volume":"9","author":"Chang","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hirota, N., Suzuki, S., Arita, T., Yagi, N., Otsuka, T., and Yamashita, T. (2021). Prediction of Biological Age and All-Cause Mortality by 12-Lead Electrocardiogram in Patients without Structural Heart Disease. BMC Geriatr., 21.","DOI":"10.1186\/s12877-021-02391-8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e007284","DOI":"10.1161\/CIRCEP.119.007284","article-title":"Age and Sex Estimation Using Artificial Intelligence from Standard 12-Lead ECGs","volume":"12","author":"Attia","year":"2019","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e001277","DOI":"10.1161\/JAHA.114.001277","article-title":"Prolonged PR Interval Predicts Clinical Recurrence of Atrial Fibrillation after Catheter Ablation","volume":"3","author":"Park","year":"2014","journal-title":"J. Am. Heart Assoc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.hrthm.2017.09.009","article-title":"Utility and Limitations of Long-Term Monitoring of Atrial Fibrillation Using an Implantable Loop Recorder","volume":"15","author":"Lee","year":"2018","journal-title":"Heart Rhythm"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/17\/2849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:45:40Z","timestamp":1760129140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/17\/2849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,3]]},"references-count":36,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["diagnostics13172849"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13172849","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,3]]}}}