A Q-transform-based deep learning model for the classification of atrial fibrillation types
Author:
Funder
SERB
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13246-024-01391-3.pdf
Reference51 articles.
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2. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE (2001) Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and risk factors in Atrial Fibrillation (ATRIA) Study. JAMA 285(18):2370–2375. https://doi.org/10.1001/jama.285.18.2370
3. Morillo CA, Banerjee A, Perel P, Wood D, Jouven X (2017) Atrial fibrillation: the current epidemic. J Geriatr Cardiol 14(3):195–203. https://doi.org/10.11909/j.issn.1671-5411.2017.03.011
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5. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, Gillum RF, Kim YH, McAnulty JH Jr, Zheng ZJ, Forouzanfar MH, Naghavi M, Mensah GA, Ezzati M, Murray CJL (2014) Worldwide epidemiology of atrial fibrillation: a global burden of Disease 2010 study. Circulation 129(8):837–847. https://doi.org/10.1161/CIRCULATIONAHA.113.005119
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