{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:55:14Z","timestamp":1774385714382,"version":"3.50.1"},"reference-count":39,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2016,12]]},"abstract":"<jats:p> In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian\u2013Bernoulli and Bernoulli\u2013Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence. A suitable feature representation from the raw ECG data can therefore be extracted in an unsupervised way. In order to enhance the performance of DBN, a fine-tuning process is carried out, which uses backpropagation by adding a softmax regression layer on the top of the resulting hidden representation layer to perform multiclass classification. The method is then validated by experiments on the well-known MIT-BIH arrhythmia database. Considering the real clinical application, the inter-patient heartbeat dataset is divided into two sets and grouped into four classes (N, S, V, F) following the recommendations of AAMI. The experiment results show our approach achieves better performance with less feature learning time than traditional hand-designed methods on the classification of ECG arrhythmias. <\/jats:p>","DOI":"10.1142\/s1469026816500218","type":"journal-article","created":{"date-parts":[[2016,11,28]],"date-time":"2016-11-28T02:02:06Z","timestamp":1480298526000},"page":"1650021","source":"Crossref","is-referenced-by-count":37,"title":["A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks"],"prefix":"10.1142","volume":"15","author":[{"given":"Zhiyong","family":"Wu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 23 Hongkong West Road, Qingdao, Shandong 266073, China"},{"name":"School of Computer Science and Technology, Shandong University of Technology, No. 266 New Village West Road, Zibo, Shandong 255000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangqian","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 23 Hongkong West Road, Qingdao, Shandong 266073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 23 Hongkong West Road, Qingdao, Shandong 266073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2016,12,27]]},"reference":[{"key":"S1469026816500218BIB001","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-8159.1997.tb06114.x"},{"key":"S1469026816500218BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2004.827359"},{"key":"S1469026816500218BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.880879"},{"key":"S1469026816500218BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2013.01.005"},{"key":"S1469026816500218BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2008.2003323"},{"key":"S1469026816500218BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2006.08.020"},{"key":"S1469026816500218BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.04.072"},{"key":"S1469026816500218BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.jart.2015.06.008"},{"key":"S1469026816500218BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.05.006"},{"key":"S1469026816500218BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.016"},{"key":"S1469026816500218BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2006.11.009"},{"key":"S1469026816500218BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.01.093"},{"key":"S1469026816500218BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.06.046"},{"key":"S1469026816500218BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2006.05.014"},{"key":"S1469026816500218BIB016","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2015.10.008"},{"key":"S1469026816500218BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.12.063"},{"key":"S1469026816500218BIB018","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2008.01.014"},{"key":"S1469026816500218BIB019","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.07.010"},{"key":"S1469026816500218BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"S1469026816500218BIB021","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"S1469026816500218BIB022","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.01.008"},{"key":"S1469026816500218BIB023","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"S1469026816500218BIB024","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.277"},{"key":"S1469026816500218BIB025","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2353635"},{"key":"S1469026816500218BIB028","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/107046"},{"key":"S1469026816500218BIB029","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.01.082"},{"key":"S1469026816500218BIB030","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"S1469026816500218BIB031","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.08.104"},{"key":"S1469026816500218BIB032","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2468589"},{"key":"S1469026816500218BIB033","first-page":"505","volume":"3","author":"Wang D.","year":"2012","journal-title":"Int. J. Inf. Educ. Technol."},{"key":"S1469026816500218BIB034","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"S1469026816500218BIB036","volume-title":"Boltzmann Machines: Constraint Satisfaction Networks that Learn","author":"Hinton G. E.","year":"1984"},{"key":"S1469026816500218BIB037","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog0901_7"},{"key":"S1469026816500218BIB038","first-page":"194","volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations","volume":"1","author":"Smolensky P.","year":"1986"},{"key":"S1469026816500218BIB042","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_32"},{"key":"S1469026816500218BIB043","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"S1469026816500218BIB047","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.883802"},{"key":"S1469026816500218BIB048","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2009.2013934"},{"key":"S1469026816500218BIB049","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.900239"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026816500218","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T20:31:31Z","timestamp":1565123491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026816500218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12]]},"references-count":39,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2016,12,27]]},"published-print":{"date-parts":[[2016,12]]}},"alternative-id":["10.1142\/S1469026816500218"],"URL":"https:\/\/doi.org\/10.1142\/s1469026816500218","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12]]}}}