{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T22:26:01Z","timestamp":1782253561398,"version":"3.54.5"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61303108"],"award-info":[{"award-number":["61303108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["17KJA520004"],"award-info":[{"award-number":["17KJA520004"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Suzhou Key Industries Technological Innovation-Prospective Applied Research Project","award":["SYG201804"],"award-info":[{"award-number":["SYG201804"]}]},{"DOI":"10.13039\/501100007824","name":"Soochow University","doi-asserted-by":"publisher","award":["KJS1524"],"award-info":[{"award-number":["KJS1524"]}],"id":[{"id":"10.13039\/501100007824","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2950383","type":"journal-article","created":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T20:05:03Z","timestamp":1572465903000},"page":"159369-159378","source":"Crossref","is-referenced-by-count":30,"title":["ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient"],"prefix":"10.1109","volume":"7","author":[{"given":"Fei","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2226-2859","authenticated-orcid":false,"given":"Fei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bairong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"894","article-title":"Soft-DTW: A differentiable loss function for time-series","author":"cuturi","year":"2017","journal-title":"Proc ICML"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1978.1163055"},{"key":"ref33","article-title":"Polyphonic music generation with sequence generative adversarial networks","author":"lee","year":"2017","journal-title":"arXiv 1710 11418"},{"key":"ref32","article-title":"How (not) to train your generative model: Scheduled sampling, likelihood, adversary","author":"husz\u00e1r","year":"2015","journal-title":"arXiv 1511 05101"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-018-9377-x"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.110"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.76"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2008.4595271"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/31\/10\/002"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2221924.2221942"},{"key":"ref12","article-title":"Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network","volume":"9","author":"zhu","year":"2019","journal-title":"Sci Rep"},{"key":"ref13","first-page":"2852","article-title":"SeqGAN: Sequence generative adversarial nets with policy gradient","author":"yu","year":"2017","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref14","first-page":"1057","article-title":"Policy gradient methods for reinforcement learning with function approximation","author":"sutton","year":"2000","journal-title":"Proc NIPS"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2012.2186810"},{"key":"ref16","first-page":"5705","article-title":"Attention-based relation extraction with bidirectional gated recurrent unit and highway network in the analysis of geological data","volume":"27","author":"luo","year":"2017","journal-title":"IEEE Access"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"ref18","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc ICML"},{"key":"ref19","first-page":"5767","article-title":"Improved training of Wasserstein GANS","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/205749"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2015.10.008"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2003.808805"},{"key":"ref29","first-page":"417","article-title":"Medical image synthesis with context&#x2014;Aware generative adversarial networks","author":"nie","year":"2017","journal-title":"Proc MICCAI"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2582340"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/31\/5\/001"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2004.1443037"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2468589"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2007.4745562"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.01.082"},{"key":"ref20","first-page":"406","article-title":"PEGASUS: A policy search method for large MDPs and POMDPs","author":"ng","year":"2000","journal-title":"Proc UAI"},{"key":"ref22","first-page":"1486","article-title":"Deep generative image models using a Laplacian pyramid of adversarial networks","author":"denton","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref21","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref24","first-page":"1","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2016","journal-title":"Proc ICLR"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref26","first-page":"146","article-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"schlegl","year":"2017","journal-title":"Proc IPMI"},{"key":"ref25","first-page":"5040","article-title":"Disentangling factors of variation in deep representation using adversarial training","author":"mathieu","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08887504.pdf?arnumber=8887504","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:42:57Z","timestamp":1643244177000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8887504\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2950383","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}