{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T21:35:34Z","timestamp":1772228134385,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.<\/jats:p>","DOI":"10.3390\/computers13120316","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T11:26:08Z","timestamp":1732706768000},"page":"316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2008-464X","authenticated-orcid":false,"given":"Hai-Long","family":"Nguyen","sequence":"first","affiliation":[{"name":"Data and Intelligent Systems Laboratory, Posts and Telecommunications Institute of Technology, Hanoi 10000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2230-7374","authenticated-orcid":false,"given":"Van Su","family":"Pham","sequence":"additional","affiliation":[{"name":"Faculty of Electronics Engineering, Posts and Telecommunications Institute of Technology, Hanoi 10000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0300-7761","authenticated-orcid":false,"given":"Hai-Chau","family":"Le","sequence":"additional","affiliation":[{"name":"Data and Intelligent Systems Laboratory, Posts and Telecommunications Institute of Technology, Hanoi 10000, Vietnam"},{"name":"Department of Data Engineering, Posts and Telecommunications Institute of Technology, Hanoi 10000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, August 15). Cardiovascular Diseases. Available online: https:\/\/www.who.int\/europe\/news-room\/fact-sheets\/item\/cardiovascular-diseases."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1080\/17434440.2022.2115887","article-title":"A review of arrhythmia detection based on electrocardiogram with artificial intelligence","volume":"19","author":"Liu","year":"2022","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.irbm.2019.12.001","article-title":"Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey","volume":"41","author":"Sahoo","year":"2020","journal-title":"Irbm"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Merdjanovska, E., and Rashkovska, A. (2023). A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-38532-9"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Macfarlane, P.W., Van Oosterom, A., Pahlm, O., Kligfield, P., Janse, M., and Camm, J. (2010). Comprehensive Electrocardiology, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-046-3"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Pang, S.P., Xu, F., Ji, P., Zhou, S., and Shu, M. (2022). Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-18664-0"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s43856-024-00464-4","article-title":"Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis","volume":"4","author":"Jin","year":"2024","journal-title":"Commun. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Qananwah, Q., Ababneh, M., and Dagamseh, A. (2024). Cardiac arrhythmias classification using photoplethysmography database. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-53142-9"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3297711","article-title":"Computer-aided arrhythmia diagnosis with bio-signal processing: A survey of trends and techniques","volume":"52","author":"Dinakarrao","year":"2019","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jeong, D.U., and Lim, K.M. (2021). Convolutional neural network for classification of eight types of arrhythmia using 2D time-frequency feature map from standard 12-lead electrocardiogram. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-99975-6"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Andayeshgar, B., Abdali-Mohammadi, F., Sepahvand, M., Almasi, A., and Salari, N. (2024). Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads. BMC Med. Res. Methodol., 24.","DOI":"10.1186\/s12874-024-02223-4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zheng, J., Chu, H., Struppa, D., Zhang, J., Yacoub, M., El-Askary, H., Chang, A., Ehwerhemuepha, L., Abudayyeh, I., and Barrett, A. (2020). Optimal multi-stage arrhythmia classification approach. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-59821-7"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1007\/s13239-023-00687-x","article-title":"Comparison of Machine Learning Algorithms Using Manual\/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old","volume":"14","author":"Hajianfar","year":"2023","journal-title":"Cardiovasc. Eng. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, X., and Ji, Z. (2023). Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram. Sensors, 23.","DOI":"10.3390\/s23094372"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1007\/s40846-018-0389-7","article-title":"Convolutional neural networks for electrocardiogram classification","volume":"38","author":"Bazi","year":"2018","journal-title":"J. Med. Biol. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aziz, S., Ahmed, S., and Alouini, M.S. (2021). ECG-based machine-learning algorithms for heartbeat classification. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-97118-5"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41597-020-0386-x","article-title":"A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients","volume":"7","author":"Zheng","year":"2020","journal-title":"Sci. Data"},{"key":"ref_18","unstructured":"Gautam, K. (2023). Ensemble Methods for Machine Learning, Manning."},{"key":"ref_19","first-page":"102932","article-title":"Applications of Stacking\/Blending ensemble learning approaches for evaluating flash flood susceptibility","volume":"112","author":"Yao","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s42256-020-0217-y","article-title":"Ensemble deep learning in bioinformatics","volume":"2","author":"Cao","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_21","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"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/12\/316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:41:07Z","timestamp":1760114467000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/12\/316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":21,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["computers13120316"],"URL":"https:\/\/doi.org\/10.3390\/computers13120316","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]}}}