{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:58:43Z","timestamp":1777431523147,"version":"3.51.4"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002642","name":"Korea University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002642","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n              <jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990\u20132010, respectively. In this context, this study uses convolutional neural networks for classifying (diagnosing) AF, employing electrocardiogram data in a general hospital.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Data came from Anam Hospital in Seoul, Korea, with 20,000 unique patients (10,000 normal sinus rhythm and 10,000 AF). 30 convolutional neural networks were applied and compared for the diagnosis of the normal sinus rhythm vs. AF condition: 6 Alex networks with 5 convolutional layers, 3 fully connected layers and the number of kernels changing from 3 to 256; and 24 residual networks with the number of residuals blocks (or kernels) varying from 8 to 2 (or 64 to 2).<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In terms of the accuracy, the best Alex network was one with 24 initial kernels (i.e., kernels in the first layer), 5,268,818 parameters and the training time of 89\u2009s (0.997), while the best residual network was one with 6 residual blocks, 32 initial kernels, 248,418 parameters and the training time of 253\u2009s (0.999). In general, the performance of the residual network improved as the number of its residual blocks (its depth) increased.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>For AF diagnosis, the residual network might be a good model with higher accuracy and fewer parameters than its Alex-network counterparts.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-019-0946-1","type":"journal-article","created":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T20:44:43Z","timestamp":1572468283000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Atrial fibrillation classification based on convolutional neural networks"],"prefix":"10.1186","volume":"19","author":[{"given":"Kwang-Sig","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunghoon","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeongjoon","family":"Gil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8535-0020","authenticated-orcid":false,"given":"Ho Sung","family":"Son","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"issue":"17","key":"946_CR1","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1161\/CIRCULATIONAHA.114.008720","volume":"132","author":"GA Roth","year":"2015","unstructured":"Roth GA, Huffman MD, Moran AE, Feigin V, Mensah GA, Naghavi M, Murray CJ. Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation. 2015;132(17):1667\u201378.","journal-title":"Circulation"},{"issue":"8","key":"946_CR2","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1161\/CIRCULATIONAHA.113.005119","volume":"129","author":"SS Chugh","year":"2014","unstructured":"Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al. Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation. 2014;129(8):837\u201347.","journal-title":"Circulation"},{"key":"946_CR3","volume-title":"Year 2016 statistics on causes of death in Korea","author":"S Korea","year":"2017","unstructured":"Korea S. Year 2016 statistics on causes of death in Korea. Sejong: Statistics Korea; 2017."},{"issue":"2","key":"946_CR4","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1093\/pubmed\/fdt056","volume":"36","author":"KS Lee","year":"2014","unstructured":"Lee KS, Park JH. 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Informed consent was waived by the IRB given that data were de-identified.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"206"}}