{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:41:32Z","timestamp":1769834492676,"version":"3.49.0"},"reference-count":15,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,23]]},"DOI":"10.23919\/eusipco54536.2021.9616079","type":"proceedings-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T21:55:53Z","timestamp":1639000553000},"page":"1145-1149","source":"Crossref","is-referenced-by-count":8,"title":["Synthesis of Realistic ECG Waveforms Using a Composite Generative Adversarial Network for Classification of Atrial Fibrillation"],"prefix":"10.23919","author":[{"given":"Rohan","family":"Banerjee","sequence":"first","affiliation":[]},{"given":"Avik","family":"Ghose","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"1","article-title":"Af classification from a short single lead ecg recording: The physionet computing in cardiology challenge 2017","volume":"44","author":"clifford","year":"2017","journal-title":"Proceedings Computers in Cardiology"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/abc960"},{"key":"ref12","first-page":"1","article-title":"Identifying normal, af and other abnormal ecg rhythms using a cascaded binary classifier","volume":"44","author":"datta","year":"2017","journal-title":"Computing"},{"key":"ref13","first-page":"1","article-title":"Convolutional recurrent neural networks for electrocardiogram classification","volume":"44","author":"zihlmann","year":"2017","journal-title":"Computing"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref15","first-page":"1322","article-title":"Adasyn: Adaptive synthetic sampling approach for imbalanced learning","author":"he","year":"2008","journal-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-42516-z"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-55448-5"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1093\/eurheartj\/ehq278","article-title":"Guidelines for the management of atrial fibrillation: the task force for the management of atrial fibrillation of the european society of cardiology (esc)","volume":"31","author":"members","year":"2010","journal-title":"European Heart Journal"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053800"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref7","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2008.4749156"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2003.808805"},{"key":"ref9","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"ar Xiv preprint"}],"event":{"name":"2021 29th European Signal Processing Conference (EUSIPCO)","location":"Dublin, Ireland","start":{"date-parts":[[2021,8,23]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["2021 29th European Signal Processing Conference (EUSIPCO)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9615915\/9615917\/09616079.pdf?arnumber=9616079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T06:44:24Z","timestamp":1644907464000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9616079\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":15,"URL":"https:\/\/doi.org\/10.23919\/eusipco54536.2021.9616079","relation":{},"subject":[],"published":{"date-parts":[[2021,8,23]]}}}