{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:20:30Z","timestamp":1779207630683,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah Bint Abdulrahman University Researchers","award":["PNURSP2022R196"],"award-info":[{"award-number":["PNURSP2022R196"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system\u2019s effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.<\/jats:p>","DOI":"10.3390\/s22239347","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T03:03:41Z","timestamp":1669863821000},"page":"9347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-3083","authenticated-orcid":false,"given":"Mohamed","family":"Hammad","sequence":"first","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9072-5878","authenticated-orcid":false,"given":"Souham","family":"Meshoul","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7667-0874","authenticated-orcid":false,"given":"Piotr","family":"Dziwi\u0144ski","sequence":"additional","affiliation":[{"name":"Department of Intelligent Computer Systems, Czestochowa University of Technology, Armii Krajowej 36, 42-218 Czestochowa, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-2801","authenticated-orcid":false,"given":"Pawe\u0142","family":"P\u0142awiak","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland"},{"name":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7154-2307","authenticated-orcid":false,"given":"Ibrahim A.","family":"Elgendy","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","unstructured":"Rolls, H.K., Stevenson, W.G., Strichartz, G.R., and Lilly, L.S. 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