{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:29:37Z","timestamp":1777267777872,"version":"3.51.4"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2020,12,1]]},"DOI":"10.1587\/transcom.2020sep0002","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T22:16:13Z","timestamp":1593555373000},"page":"1403-1410","source":"Crossref","is-referenced-by-count":5,"title":["ECG Classification with Multi-Scale Deep Features Based on Adaptive Beat-Segmentation"],"prefix":"10.23919","volume":"E103.B","author":[{"given":"Huan","family":"SUN","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchun","family":"GUO","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yishuai","family":"CHEN","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"CHEN","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] F. Wu, T. Wu, and M.R. Yuce, \u201cDesign and implementation of a wearable sensor network system for iot-connected safety and health applications,\u201d 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp.87-90, 2019. 10.1109\/wf-iot.2019.8767280","DOI":"10.1109\/WF-IoT.2019.8767280"},{"key":"2","unstructured":"[2] T.J. Jun, H.M. Nguyen, D. Kang, D. Kim, D. Kim, and Y.H. Kim, \u201cECG arrhythmia classification using a 2-D convolutional neural network,\u201d arXiv preprint arXiv:1804.06812, 2018."},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] S. Kaplan Berkaya, A.K. Uysal, E. Sora Gunal, S. Ergin, S. Gunal, and M.B. Gulmezoglu, \u201cA survey on ECG analysis,\u201d Biomedical Signal Processing and Control, no.43, pp.216-235, 2018. 10.1016\/j.bspc.2018.03.003","DOI":"10.1016\/j.bspc.2018.03.003"},{"key":"4","unstructured":"[4] C. Ye, B.V. Kumar, and M.T. Coimbra, \u201cCombining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification,\u201d Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp.2428-2431, IEEE, 2012."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. Zubair, J. Kim, and C. Yoon, \u201cAn automated ECG beat classification system using convolutional neural networks,\u201d 2016 6th international conference on IT convergence and security (ICITCS), pp.1-5, IEEE, 2016. 10.1109\/icitcs.2016.7740310","DOI":"10.1109\/ICITCS.2016.7740310"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] S. Kiranyaz, T. Ince, R. Hamila, and M. Gabbouj, \u201cConvolutional neural networks for patient-specific ECG classification,\u201d Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp.2608-2611, IEEE, 2015. 10.1109\/embc.2015.7318926","DOI":"10.1109\/EMBC.2015.7318926"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] Y. Xiang, J. Luo, T. Zhu, S. Wang, X. Xiang, and J. Meng, \u201cECG-based heartbeat classification using two-level convolutional neural network and RR interval difference,\u201d IEICE Trans. Inf. &amp; Syst., vol.E101-D, no.4, pp.1189-1198, April 2018. 10.1587\/transinf.2017edp7285","DOI":"10.1587\/transinf.2017EDP7285"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] E. Cimen and G. Ozturk, \u201cArrhythmia classification via k-means based polyhedral conic functions algorithm,\u201d Computational Science and Computational Intelligence (CSCI), 2016 International Conference on, pp.798-802, IEEE, 2016. 10.1109\/csci.2016.0155","DOI":"10.1109\/CSCI.2016.0155"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] S. Kiranyaz, T. Ince, and M. Gabbouj, \u201cReal-time patient-specific ecg classification by 1-D convolutional neural networks,\u201d IEEE Trans. Biomed. Eng., vol.63, no.3, pp.664-675, 2016. 10.1109\/tbme.2015.2468589","DOI":"10.1109\/TBME.2015.2468589"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Kropf, D. Hayn, and G. Schreier, \u201cECG classification based on time and frequency domain features using random forests,\u201d Computing, vol.44, p.1, 2017. 10.22489\/cinc.2017.168-168","DOI":"10.22489\/CinC.2017.168-168"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] C.I.M. Cruz, J.P. Marasigan, A.P.G. Perez, J.E.V. Pillejera, N.P. Veron, and A.R. dela Cruz, \u201cA comparative study between DWT-ANFIS and DWT-SVM in ECG classification,\u201d Region 10 Conference (TENCON), 2016 IEEE, pp.2052-2055, IEEE, 2016. 10.1109\/tencon.2016.7848386","DOI":"10.1109\/TENCON.2016.7848386"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] A. Ebrahimzadeh, M. Ahmadi, and M. Safarnejad, \u201cClassification of ECG signals using hermite functions and MLP neural networks,\u201d J. AI and Data Mining, vol.4, no.1, pp.55-65, 2016. 10.5829\/idosi.jaidm.2016.04.01.07","DOI":"10.5829\/idosi.JAIDM.2016.04.01.07"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] C. Ye, B.V. Kumar, and M.T. Coimbra, \u201cHeartbeat classification using morphological and dynamic features of ECG signals,\u201d IEEE Trans. Biomed. Eng., vol.59, no.10, pp.2930-2941, 2012. 10.1109\/tbme.2012.2213253","DOI":"10.1109\/TBME.2012.2213253"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] A. Vishwa, M. Lal, S. Dixit, and P. Vardwaj, \u201cClasification of arrhythmic ECG data using machine learning techniques,\u201d International J. Interactive Multimedia and Artificial Intelligence, vol.1, no.4, pp.67-70, 2011. 10.9781\/ijimai.2011.1411","DOI":"10.9781\/ijimai.2011.1411"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] M. Limam and F. Precioso, \u201cAtrial fibrillation detection and ECG classification based on convolutional recurrent neural network,\u201d Computing, vol.44, p.1, 2017.","DOI":"10.22489\/CinC.2017.171-325"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] W. Jiang and S.G. Kong, \u201cBlock-based neural networks for personalized ecg signal classification,\u201d IEEE Trans. Neural Netw., vol.18, no.6, pp.1750-1761, 2007. 10.1109\/tnn.2007.900239","DOI":"10.1109\/TNN.2007.900239"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] T. Li and M. Zhou, \u201cECG classification using wavelet packet entropy and random forests,\u201d Entropy, vol.18, no.8, p.285, 2016. 10.3390\/e18080285","DOI":"10.3390\/e18080285"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] R. Sm\u00ed\u0161ek, J. Hej\u010d, M. Ronzhina, A. N\u0115mcov\u00e1, L. Mar\u0161\u00e1nov\u00e1, J. Chmel\u00edk, J. Kol\u00e1\u0159ov\u00e1, I. Provazn\u00edk, L. Smital, and M. V\u00edtek, \u201cSVM based ECG classification using rhythm and morphology features, cluster analysis and multilevel noise estimation,\u201d Computing, vol.44, p.1, 2017. 10.22489\/cinc.2017.172-200","DOI":"10.22489\/CinC.2017.172-200"},{"key":"19","unstructured":"[19] K. Kadir, A. Safie, M.I. Yusof, H. Nasir, and L. Petropoulakis, \u201cMultiple pulse k-nearest neighbors authentication for malay ECG based class attendance system,\u201d International Conference on Engineering Technology &amp; Technopreneuship, 2014. 10.1109\/ice2t.2014.7006238"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] J. Hu, L. Shen, and G. Sun, \u201cSqueeze-and-excitation networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.7132-7141, 2018. 10.1109\/cvpr.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] S. Woo, J. Park, J.Y. Lee, and I. So Kweon, \u201cCBAM: Convolutional block attention module,\u201d Proc. European Conference on Computer Vision (ECCV), pp.3-19, 2018. 10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] J. Pan and W.J. Tompkins, \u201cA real-time QRS detection algorithm,\u201d IEEE Trans. Biomed. Eng, vol.32, no.3, pp.230-236, 1985. 10.1109\/tbme.1985.325532","DOI":"10.1109\/TBME.1985.325532"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] U.R. Acharya, K.P. Joseph, N. Kannathal, L.C. Min, and J.S. Suri, \u201cHeart rate variability,\u201d Advances in Cardiac Signal Processing, pp.121-165, 2018. 10.1007\/978-3-540-36675-1_5","DOI":"10.1007\/978-3-540-36675-1_5"},{"key":"24","unstructured":"[24] A. ECAR, \u201cRecommended practice for testing and reporting performance results of ventricular arrhythmia detection algorithms,\u201d Association for the Advancement of Medical Instrumentation, p.69, 1987."},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] X. Tang and L. Shu, \u201cClassification of electrocardiogram signals with RS and quantum neural networks,\u201d International J. Multimedia and Ubiquitous Engineering, vol.9, no.2, pp.363-372, 2014. 10.14257\/ijmue.2014.9.2.37","DOI":"10.14257\/ijmue.2014.9.2.37"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, M. Adam, A. Gertych, and R. San Tan, \u201cA deep convolutional neural network model to classify heartbeats,\u201d Comput. Biol. Med., vol.89, pp.389-396, 2017. 10.1016\/j.compbiomed.2017.08.022","DOI":"10.1016\/j.compbiomed.2017.08.022"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] X. Zhai and C. Tin, \u201cAutomated ECG classification using dual heartbeat coupling based on convolutional neural network,\u201d IEEE Access, vol.6, pp.27465-27472, 2018. 10.1109\/access.2018.2833841","DOI":"10.1109\/ACCESS.2018.2833841"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E103.B\/12\/E103.B_2020SEP0002\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T14:59:42Z","timestamp":1704898782000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E103.B\/12\/E103.B_2020SEP0002\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":27,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2020sep0002","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,1]]}}}