{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:30:16Z","timestamp":1780500616908,"version":"3.54.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["CURR MED SCI"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11596-025-00134-z","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T08:37:23Z","timestamp":1762331843000},"page":"1460-1470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unveiling Hearts: Deep Learning-Based Electrocardiogram Classification for Congenital Heart Disease Detection"],"prefix":"10.1007","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6630-7820","authenticated-orcid":false,"given":"Rishika","family":"Anand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0863-369X","authenticated-orcid":false,"given":"S. R. N.","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2081-0935","authenticated-orcid":false,"given":"Dinesh Kumar","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"134_CR1","volume-title":"LeoSchamroth: An Introduction to Electro Cardiography","author":"C Narasimhan","year":"2023","unstructured":"Narasimhan C, Francis J. LeoSchamroth: An Introduction to Electro Cardiography. 8th ed. New Delhi: Wiley; 2023.","edition":"8"},{"issue":"5","key":"134_CR2","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1080\/03091900512331332587","volume":"29","author":"HL Chan","year":"2005","unstructured":"Chan HL, Chou WS, Chen SW, et al. Continuous and online analysis of heart rate variability. J Med Eng Technol. 2005;29(5):227\u2013234.","journal-title":"J Med Eng Technol."},{"issue":"1","key":"134_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1006\/cbmr.1994.1006","volume":"27","author":"P Laguna","year":"1994","unstructured":"Laguna P, Jan\u00e9 R, Caminal P. Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput Biomed Res. 1994;27(1):45\u201360.","journal-title":"Comput Biomed Res."},{"issue":"7348","key":"134_CR4","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1136\/bmj.324.7348.1264","volume":"324","author":"J Edhouse","year":"2002","unstructured":"Edhouse J. ABC of clinical electrocardiography: Conditions affecting the left side of the heart. Bmj. 2002;324(7348):1264\u20131267.","journal-title":"Bmj."},{"issue":"1","key":"134_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.medengphy.2008.03.004","volume":"31","author":"Y Wu","year":"2009","unstructured":"Wu Y, Rangayyan RM, Zhou Y, et al. Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med Eng Phys. 2009;31(1):17\u201326.","journal-title":"Med Eng Phys."},{"issue":"3","key":"134_CR6","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 2001;20(3):45-50.","journal-title":"IEEE Eng Med Biol Mag."},{"issue":"1","key":"134_CR7","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1038\/s41467-024-44930-y","volume":"15","author":"J Chen","year":"2024","unstructured":"Chen J, Huang S, Zhang Y, et al. Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts. Nat Commun. 2024;15(1):976.","journal-title":"Nat Commun."},{"issue":"2","key":"134_CR8","doi-asserted-by":"publisher","first-page":"234","DOI":"10.5005\/jp-journals-10071-23731","volume":"25","author":"A Sarma","year":"2021","unstructured":"Sarma A. Crochetage sign: an invaluable independent ECG sign in detecting ASD. Indian J Crit Care Med. 2021;25(2):234\u2013235.","journal-title":"Indian J Crit Care Med."},{"issue":"3","key":"134_CR9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1111\/j.1542-474X.2006.00105.x","volume":"11","author":"RN MacAlpin","year":"2006","unstructured":"MacAlpin RN. Significance of abnormal Q waves in the electrocardiograms of adults less than 40 years old. Ann Noninvasive Electrocardiol. 2006;11(3):203\u2013210.","journal-title":"Ann Noninvasive Electrocardiol."},{"issue":"4","key":"134_CR10","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1161\/01.CIR.12.4.604","volume":"12","author":"AJ Brink","year":"1955","unstructured":"Brink AJ, Neill CA. The electrocardiogram in congenital heart disease; with special reference to left axis deviation. Circulation. 1955;12(4):604\u2013611.","journal-title":"Circulation."},{"key":"134_CR11","doi-asserted-by":"crossref","unstructured":"Bronzetti G. Congenital heart disease. Atlas of Pediatric and Youth ECG. Cham: Springer International Publishing, 2017:317\u2013358.","DOI":"10.1007\/978-3-319-57102-7_18"},{"key":"134_CR12","doi-asserted-by":"crossref","unstructured":"Charlap S, Shani J, Schulhoff N, et al. R- and S-wave amplitude changes with acute anterior transmural myocardial ischemia. Correlations with left ventricular filling pressures. Chest. 1990;97(3):566\u2013571.","DOI":"10.1378\/chest.97.3.566"},{"issue":"1","key":"134_CR13","doi-asserted-by":"publisher","DOI":"10.1111\/anec.12799","volume":"26","author":"T Istolahti","year":"2021","unstructured":"Istolahti T, Lyytik\u00e4inen LP, Huhtala H, et al. The prognostic significance of T-wave inversion according to ECG lead group during long-term follow-up in the general population. Ann Noninvasive Electrocardiol. 2021;26(1):e12799.","journal-title":"Ann Noninvasive Electrocardiol."},{"key":"134_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.105959","volume":"202","author":"S Mousavi","year":"2021","unstructured":"Mousavi S, Afghah F, Khadem F, et al. ECG Language processing (ELP): a new technique to analyze ECG signals. Comput Methods Programs Biomed. 2021;202:105959.","journal-title":"Comput Methods Programs Biomed."},{"issue":"1","key":"134_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/970\/1\/012012","volume":"970","author":"A Turnip","year":"2018","unstructured":"Turnip A, Ilham Rizqywan M, Kusumandari DE, et al. Classification of ECG signal with support vector machine method for arrhythmia detection. J Phys Conf Ser. 2018;970(1):012012.","journal-title":"J Phys Conf Ser."},{"key":"134_CR16","unstructured":"Jun TJ, Nguyen HM, Kang D, et al. ECG arrhythmia classification using a 2-D convolutional neural network [preprint]. 2018: arXiv:1804.06812. Available from: https:\/\/arxiv.org\/abs\/1804.06812"},{"key":"134_CR17","unstructured":"Wu YN, Yang F, Liu Y, et al. A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification [preprint]. 2018: arXiv:1810.07088. Available from: https:\/\/arxiv.org\/abs\/1810.07088"},{"issue":"1","key":"134_CR18","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1038\/s41467-020-15432-4","volume":"11","author":"AH Ribeiro","year":"2020","unstructured":"Ribeiro AH, Ribeiro MH, Paix\u00e3o GMM, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11(1):1760.","journal-title":"Nat Commun."},{"key":"134_CR19","doi-asserted-by":"publisher","first-page":"2694768","DOI":"10.1155\/2018\/2694768","volume":"2018","author":"MM Casas","year":"2018","unstructured":"Casas MM, Avitia RL, Gonzalez-Navarro FF, et al. Bayesian classification models for premature ventricular ontraction detection on ECG traces. J Healthc Eng. 2018;2018:2694768.","journal-title":"J Healthc Eng."},{"key":"134_CR20","doi-asserted-by":"crossref","unstructured":"Shoughi A, Dowlatshahi MB. A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset. In: Proceedings of the 2021 26th International Computer Conference, Computer Society of Iran (CSICC); 03\u201304 March 2021; Tehran, Iran. Piscataway (NJ): IEEE; 2021. pp. 1\u20136. Available from: https:\/\/ieeexplore.ieee.org\/document\/9420620.","DOI":"10.1109\/CSICC52343.2021.9420620"},{"issue":"2","key":"134_CR21","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/bioengineering5020035","volume":"5","author":"S Savalia","year":"2018","unstructured":"Savalia S, Emamian V. Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks. Bioengineering (Basel). 2018;5(2):35.","journal-title":"Bioengineering (Basel)."},{"key":"134_CR22","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.procs.2015.12.045","volume":"73","author":"B Mohamed","year":"2015","unstructured":"Mohamed B, Issam A, Mohamed A, et al. ECG image classification in real time based on the haar-like features and artificial neural networks. Procedia Comput Sci. 2015;73:32\u201339.","journal-title":"Procedia Comput Sci."},{"key":"134_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2020.569050","volume":"11","author":"Y Liang","year":"2020","unstructured":"Liang Y, Yin S, Tang Q, et al. Deep learning algorithm classifies heartbeat events based on electrocardiogram signals. Front Physiol. 2020;11:569050.","journal-title":"Front Physiol."},{"key":"134_CR24","doi-asserted-by":"crossref","unstructured":"Oliveira AT, Nobrega EGO. A novel arrhythmia classification method based on convolutional neural networks interpretation of electrocardiogram images. In: Proceedings of\u00a02019 IEEE International Conference on Industrial Technology (ICIT); 13\u201315 February 2019; Melbourne, VIC, Austrilia. Piscataway (NJ): IEEE; 2019. pp. 841\u2013846.","DOI":"10.1109\/ICIT.2019.8755177"},{"key":"134_CR25","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.artmed.2017.06.004","volume":"79","author":"FY Zhou","year":"2017","unstructured":"Zhou FY, Jin LP, Dong J. Premature ventricular contraction detection combining deep neural networks and rules inference. Artif Intell Med. 2017;79:42\u201351.","journal-title":"Artif Intell Med."},{"key":"134_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101597","volume":"55","author":"M Porumb","year":"2020","unstructured":"Porumb M, Iadanza E, Massaro S, et al. A convolutional neural network approach to detect congestive heart failure. Biomed Signal Process Control. 2020;55:101597.","journal-title":"Biomed Signal Process Control."},{"key":"134_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2019.109515","volume":"136","author":"A Diker","year":"2020","unstructured":"Diker A, Avci E, Tanyildizi E, et al. A novel ECG signal classification method using DEA-ELM. Med Hypotheses. 2020;136:109515.","journal-title":"Med Hypotheses."},{"issue":"7","key":"134_CR28","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.119.014717","volume":"9","author":"JM Kwon","year":"2020","unstructured":"Kwon JM, Lee SY, Jeon KH, et al. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc. 2020;9(7):e014717.","journal-title":"J Am Heart Assoc."},{"issue":"1","key":"134_CR29","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s13634-017-0519-3","volume":"2017","author":"R He","year":"2017","unstructured":"He R, Wang K, Li Q, et al. A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization. EURASIP J Adv Signal Process. 2017;2017(1):82.","journal-title":"EURASIP J Adv Signal Process."},{"issue":"1","key":"134_CR30","doi-asserted-by":"publisher","first-page":"8445","DOI":"10.1038\/s41598-020-65105-x","volume":"10","author":"H Makimoto","year":"2020","unstructured":"Makimoto H, H\u00f6ckmann M, Lin T, et al. Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction. Sci Rep. 2020;10(1):8445.","journal-title":"Sci Rep."},{"issue":"10","key":"134_CR31","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.3390\/s16101744","volume":"16","author":"H Li","year":"2016","unstructured":"Li H, Yuan D, Wang Y, et al. Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system. Sensors (Basel). 2016;16(10):1744.","journal-title":"Sensors (Basel)."},{"key":"134_CR32","doi-asserted-by":"crossref","unstructured":"Khan MU, Aziz S, Javeria MC, et al. ECG Signal Analysis for Classification of Congenital Heart Defects. In: Proceedings of 2020 International Conference on Computing and Information Technology (ICCIT-1441); 09\u201310 September 2020; Tabuk, Saudi Arabia. Piscataway (NJ): IEEE; 2020. pp. 1\u20135. Available from: https:\/\/ieeexplore.ieee.org\/document\/9213825","DOI":"10.1109\/ICCIT-144147971.2020.9213825"},{"key":"134_CR33","doi-asserted-by":"crossref","unstructured":"Muthukumar B, Kartheesan L, Pasupathy V, et al. Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection Model from ECG Signals. In: Proceedings of the 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA); 11\u201313 December 2024; Theni, India. Piscataway (NJ): IEEE; 2024. pp. 217\u2013223. Available from: https:\/\/ieeexplore.ieee.org\/document\/10863976.","DOI":"10.1109\/ICSCNA63714.2024.10863976"}],"container-title":["Current Medical Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11596-025-00134-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11596-025-00134-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11596-025-00134-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T09:15:51Z","timestamp":1766999751000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11596-025-00134-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["134"],"URL":"https:\/\/doi.org\/10.1007\/s11596-025-00134-z","relation":{},"ISSN":["2096-5230","2523-899X"],"issn-type":[{"value":"2096-5230","type":"print"},{"value":"2523-899X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]},"assertion":[{"value":"31 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"This study utilizes the publicly available MIT-BIH Arrhythmia Database, which is de-identified and was collected with appropriate ethical approvals and participant consent in its original study. Therefore, no additional ethical approval or consent was required for this secondary analysis.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"Not applicable. This study did not involve individual person\u2019s data.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}