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Coumel, and J.F. Leclercq, \u201cAmbulatory sudden cardiac death: Mechanisms of production of fatal arrhythmia on the basis of data from 157 cases,\u201d American Heart Journal, vol.117, no.1, pp.151-159, 1989. 10.1016\/0002-8703(89)90670-4","DOI":"10.1016\/0002-8703(89)90670-4"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] R. Hoekema, G.J.H. Uijen, and A. van Oosterom, \u201cGeometrical aspects of the interindividual variability of multilead ECG recordings,\u201d IEEE Trans. Biomed. Eng., vol.48, no.5, pp.551-559, 2001. 10.1109\/10.918594","DOI":"10.1109\/10.918594"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] V. de Pinto, \u201cFilters for the reduction of baseline wander and muscle artifact in the ECG,\u201d Journal of Electrocardiology, vol.25, pp.40-48, 1992. 10.1016\/0022-0736(92)90060-d","DOI":"10.1016\/0022-0736(92)90060-D"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S. Osowski, L.T. Hoai, and T. Markiewicz, \u201cSupport vector machine-based expert system for reliable heartbeat recognition,\u201d IEEE Trans. Biomed. Eng., vol.51, no.4, pp.582-589, 2004. 10.1109\/tbme.2004.824138","DOI":"10.1109\/TBME.2004.824138"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] J. Kim, H. Shin, Y. Lee, and M. Lee, \u201cAlgorithm for classifying arrhythmia using extreme learning machine and principal component analysis,\u201d 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3257-3260, 2007. 10.1109\/iembs.2007.4353024","DOI":"10.1109\/IEMBS.2007.4353024"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] C. Ye, B.V.K.V. Kumar, and M.T. Coimbra, \u201cHeartbeat classification using morphological and dynamic features of ECG signals,\u201d IEEE Transa. Biomed. Eng., vol.59, no.10, pp.2930-2941, 2012. 10.1109\/tbme.2012.2213253","DOI":"10.1109\/TBME.2012.2213253"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] T.J. Jun, H.J. Park, N.H. Minh, D. Kim, and Y.-H. Kim, \u201cPremature ventricular contraction beat detection with deep neural networks,\u201d 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.859-864, 2016. 10.1109\/icmla.2016.0154","DOI":"10.1109\/ICMLA.2016.0154"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] V.H.C. de Albuquerque, T.M. Nunes, D.R. Pereira, E.J.d.S. Luz, D. Menotti, J.P. Papa, and J.M.R.S. Tavares, \u201cRobust automated cardiac arrhythmia detection in ECG beat signals,\u201d Neural Computing and Applications, vol.29, no.3, pp.679-693, 2018. 10.1007\/s00521-016-2472-8","DOI":"10.1007\/s00521-016-2472-8"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] H.F. Huang, G.S. Hu, and L. Zhu, \u201cSparse representation-based heartbeat classification using independent component analysis,\u201d Journal of Medical Systems, vol.36, no.3, pp.1235-1247, 2012. 10.1007\/s10916-010-9585-x","DOI":"10.1007\/s10916-010-9585-x"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] P. de Chazal, M. O&apos;Dwyer, and R.B. Reilly, \u201cAutomatic classification of heartbeats using ECG morphology and heartbeat interval features,\u201d IEEE Trans. Biomed. Eng., vol.51, no.7, pp.1196-1206, 2004. 10.1109\/tbme.2004.827359","DOI":"10.1109\/TBME.2004.827359"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] P. Li, Y. Wang, J. He, L. Wang, Y. Tian, T.-S. Zhou, T. Li, and J.-S. Li, \u201cHigh-performance personalized heartbeat classification model for long-term ECG signal,\u201d IEEE Trans. Biomed. Eng., vol.64, no.1, pp.78-86, 2016. 10.1109\/tbme.2016.2539421","DOI":"10.1109\/TBME.2016.2539421"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] T. Ince, S. Kiranyaz, and M. Gabbouj, \u201cA generic and robust system for automated patient-specific classification of ECG signals,\u201d IEEE Trans. Biomed. Eng., vol.56, no.5, pp.1415-1426, 2009. 10.1109\/tbme.2009.2013934","DOI":"10.1109\/TBME.2009.2013934"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] P. P\u0142awiak, \u201cNovel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system,\u201d Expert Systems with Applications, vol.92, pp.334-349, 2018. 10.1016\/j.eswa.2017.09.022","DOI":"10.1016\/j.eswa.2017.09.022"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] T. Li and M. Zhou, \u201cECG classification using wavelet packet entropy and random forests,\u201d Entropy, vol.18, no.8, 285, 2016. 10.3390\/e18080285","DOI":"10.3390\/e18080285"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] Y. \u00d6zbay, R. Ceylan, and B. Karlik, \u201cIntegration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier,\u201d Expert Systems with Applications, vol.38, no.1, pp.1004-1010, 2011. 10.1016\/j.eswa.2010.07.118","DOI":"10.1016\/j.eswa.2010.07.118"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] S. Raj and K.C. Ray, \u201cSparse representation of ECG signals for automated recognition of cardiac arrhythmias,\u201d Expert Systems with Applications, vol.105, pp.49-64, 2018. 10.1016\/j.eswa.2018.03.038","DOI":"10.1016\/j.eswa.2018.03.038"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] P. Xie, G. Wang, C. Zhang, M. Chen, H. Yang, T. Lv, Z. Sang, and P. Zhang, \u201cBidirectional recurrent neural network and convolutional neural network (BiRCNN) for ECG beat classification,\u201d 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.2555-2558, 2018. 10.1109\/embc.2018.8512752","DOI":"10.1109\/EMBC.2018.8512752"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] 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, 2015. 10.1109\/tbme.2015.2468589","DOI":"10.1109\/TBME.2015.2468589"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] 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"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] S. Saadatnejad, M. Oveisi, and M. Hashemi, \u201cLSTM-based ECG classification for continuous monitoring on personal wearable devices,\u201d IEEE J. Biomed. Health Inform., vol.24, no.2, pp.515-523, 2020. 10.1109\/jbhi.2019.2911367","DOI":"10.1109\/JBHI.2019.2911367"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] M. Schuster and K.K. Paliwal, \u201cBidirectional recurrent neural networks,\u201d IEEE Trans. Signal Process., vol.45, no.11, pp.2673-2681, 1997. 10.1109\/78.650093","DOI":"10.1109\/78.650093"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] S.S. Xu, M.-W. Mak, and C.-C. Cheung, \u201cTowards end-to-end ECG classification with raw signal extraction and deep neural networks,\u201d IEEE J. Biomed. Health Inform., vol.23, no.4, pp.1574-1584, 2019. 10.1109\/jbhi.2018.2871510","DOI":"10.1109\/JBHI.2018.2871510"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] \u00d6. Yildirim, \u201cA novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification,\u201d Computers in Biology and Medicine, vol.96, pp.189-202, 2018. 10.1016\/j.compbiomed.2018.03.016","DOI":"10.1016\/j.compbiomed.2018.03.016"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] Z. Wu, T. Lan, C. Yang, and Z. Nie, \u201cA novel method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks,\u201d IEEE Access, vol.7, pp.170820-170830, 2019. 10.1109\/access.2019.2956050","DOI":"10.1109\/ACCESS.2019.2956050"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] S. Nurmaini, R.U. Partan, W. Caesarendra, T. Dewi, M.N. Rahmatullah, A. Darmawahyuni, V. Bhayyu, and F. Firdaus, \u201cAn automated ECG beat classification system using deep neural networks with an unsupervised feature extraction technique,\u201d Applied Sciences, vol.9, no.14, 2921, 2019. 10.3390\/app9142921","DOI":"10.3390\/app9142921"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] E.K. Wang, X. Zhang, and L. Pan, \u201cAutomatic classification of CAD ECG signals with SDAE and bidirectional long short-term network,\u201d IEEE Access, vol.7, pp.182873-182880, 2019. 10.1109\/access.2019.2936525","DOI":"10.1109\/ACCESS.2019.2936525"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] F. Li, Y. Xu, Z. Chen, and Z. Liu, \u201cAutomated heartbeat classification using 3-D inputs based on convolutional neural network with multi-fields of view,\u201d IEEE Access, vol.7, pp.76295-76304, 2019. 10.1109\/access.2019.2921991","DOI":"10.1109\/ACCESS.2019.2921991"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] A.Y. Hannun, P. Rajpurkar, M. Haghpanahi, G.H. Tison, C. Bourn, M.P. Turakhia, and A.Y. Ng, \u201cCardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network,\u201d Nature Medicine, vol.25, no.1, pp.65-69, 2019. 10.1038\/s41591-018-0268-3","DOI":"10.1038\/s41591-018-0268-3"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, M. Adam, A. Gertych, and R.S. Tan, \u201cA deep convolutional neural network model to classify heartbeats,\u201d Computers in Biology and Medicine, vol.89, pp.389-396, 2017. 10.1016\/j.compbiomed.2017.08.022","DOI":"10.1016\/j.compbiomed.2017.08.022"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] A. Amirshahi and M. Hashemi, \u201cECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices,\u201d IEEE Trans. Biomed. Circuits Syst., vol.13, no.6, pp.1483-1493, 2019. 10.1109\/tbcas.2019.2948920","DOI":"10.1109\/TBCAS.2019.2948920"},{"key":"31","unstructured":"[31] R. Mark and G. Moody, \u201cMIT-BIH arrhythmia database directory,\u201d Massachusetts Institute of Technology, Cambridge, 1988."},{"key":"32","unstructured":"[32] Association for the Advancement of Medical Instrumentation, \u201cTesting and reporting performance results of cardiac rhythm and ST segment measurement algorithms,\u201d ANSI\/AAMI EC38, vol.1998, 1998."},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] 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":"34","doi-asserted-by":"crossref","unstructured":"[34] A.A.R. Bsoul, S.-Y. Ji, K. Ward, and K. Najarian, \u201cDetection of P, QRS, and T components of ECG using wavelet transformation,\u201d 2009 ICME International Conference on Complex Medical Engineering, pp.1-6, 2009. 10.1109\/iccme.2009.4906677","DOI":"10.1109\/ICCME.2009.4906677"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] P. Warrick and M.N. Homsi, \u201cCardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks,\u201d 2017 Computing in Cardiology (CinC), vol.44, pp.1-4, 2017. 10.22489\/cinc.2017.161-460","DOI":"10.22489\/CinC.2017.161-460"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] M. Blanco-Velasco, B. Weng, and K.E. Barner, \u201cECG signal denoising and baseline wander correction based on the empirical mode decomposition,\u201d Computers in Biology and Medicine, vol.38, no.1, pp.1-13, 2008. 10.1016\/j.compbiomed.2007.06.003","DOI":"10.1016\/j.compbiomed.2007.06.003"},{"key":"37","unstructured":"[37] M. Jaderberg, K. Simonyan, A. Zisserman, et al., \u201cSpatial transformer networks,\u201d Advances in Neural Information Processing Systems, pp.2017-2025, 2015."},{"key":"38","doi-asserted-by":"publisher","unstructured":"[38] J. Pan and W.J. Tompkins, \u201cA real-time QRS detection algorithm,\u201d IEEE Trans. Biomed. Eng., vol.BME-32, no.3, pp.230-236, 1985. 10.1109\/tbme.1985.325532","DOI":"10.1109\/TBME.1985.325532"},{"key":"39","unstructured":"[39] D. Arthur and S. Vassilvitskii, \u201ck-means++: The advantages of careful seeding,\u201d Proc. Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp.1027-1035, 2007."},{"key":"40","unstructured":"[40] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, \u201cDropout: A simple way to prevent neural networks from overfitting,\u201d The Journal of Machine Learning Research, vol.15, no.1, pp.1929-1958, 2014."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/5\/E103.D_2019EDP7282\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T06:39:12Z","timestamp":1588833552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/5\/E103.D_2019EDP7282\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,1]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2019edp7282","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,1]]}}}