{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T14:24:43Z","timestamp":1785767083870,"version":"3.56.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Scientific Research Fund of Zhejiang University","award":["XY2023033"],"award-info":[{"award-number":["XY2023033"]}]},{"name":"Talent Program of Zhejiang Province","award":["2021R51004"],"award-info":[{"award-number":["2021R51004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1809204, 61525106"],"award-info":[{"award-number":["U1809204, 61525106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"DOI":"10.1186\/s13634-024-01187-3","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T13:45:49Z","timestamp":1730295949000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["EGCNet: a hierarchical graph convolutional neural network for improved classification of electrocardiograms"],"prefix":"10.1186","volume":"2024","author":[{"given":"Jianhui","family":"Peng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Ran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenjin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"issue":"6","key":"1187_CR1","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1093\/europace\/euq435","volume":"13","author":"FF Syed","year":"2011","unstructured":"F.F. Syed, S.J. Asirvatham, J. Francis, Arrhythmia occurrence with takotsubo cardiomyopathy: a literature review. Europace 13(6), 780\u2013788 (2011)","journal-title":"Europace"},{"key":"1187_CR2","first-page":"638","volume":"119","author":"N Samesima","year":"2022","unstructured":"N. Samesima, E.G. God, J.C.L. Kruse, M.G. Leal, C. Pinho, F.F.A. Franca, J. Pimenta, A.F. Cardoso, A. Paix\u00e3o, A. Fonseca et al., Brazilian society of cardiology guidelines on the analysis and issuance of electrocardiographic reports-2022. Arq. Bras. Cardiol. 119, 638\u2013680 (2022)","journal-title":"Arq. Bras. Cardiol."},{"issue":"11","key":"1187_CR3","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1111\/pace.12446","volume":"37","author":"A Barutcu","year":"2014","unstructured":"A. Barutcu, A. Temiz, A. Bekler, B. Altun, B. Kirilmaz, F.U. Aksu, U. K\u00fc\u00e7\u00fck, E. Gazi, Arrhythmia risk assessment using heart rate variability parameters in patients with frequent ventricular ectopic beats without structural heart disease. Pacing Clin. Electrophysiol. 37(11), 1448\u20131454 (2014)","journal-title":"Pacing Clin. Electrophysiol."},{"issue":"2","key":"1187_CR4","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1109\/TNNLS.2020.2984955","volume":"32","author":"C Yu","year":"2021","unstructured":"C. Yu, Z. Gao, W. Zhang, G. Yang, S. Zhao, H. Zhang, Y. Zhang, S. Li, Multitask learning for estimating multitype cardiac indices in MRI and CT based on adversarial reverse mapping. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 493\u2013506 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.2984955","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"1187_CR5","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1023\/A:1021100828459","volume":"6","author":"A Nogami","year":"2002","unstructured":"A. Nogami, Idiopathic left ventricular tachycardia: assessment and treatment. Card. Electrophysiol. Rev. 6(4), 448\u2013457 (2002)","journal-title":"Card. Electrophysiol. Rev."},{"issue":"3","key":"1187_CR6","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2015","unstructured":"S. Kiranyaz, T. Ince, M. Gabbouj, Real-time patient-specific ecg classification by 1-d convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664\u2013675 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1187_CR7","doi-asserted-by":"crossref","unstructured":"K. Luo, J. Li, Z. Wang, , A. Cuschieri, Patient-specific deep architectural model for ecg classification. J. Healthc. Eng. 2017 (2017)","DOI":"10.1155\/2017\/4108720"},{"key":"1187_CR8","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.bspc.2018.03.003","volume":"43","author":"SK Berkaya","year":"2018","unstructured":"S.K. Berkaya, A.K. Uysal, E.S. Gunal, S. Ergin, S. Gunal, M.B. Gulmezoglu, A survey on ECG analysis. Biomed. Signal Process. Control 43, 216\u2013235 (2018)","journal-title":"Biomed. Signal Process. Control"},{"issue":"11","key":"1187_CR9","doi-asserted-by":"publisher","first-page":"3442","DOI":"10.1016\/j.asoc.2012.07.007","volume":"12","author":"B Do\u011fan","year":"2012","unstructured":"B. Do\u011fan, M. Kor\u00fcrek, A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Appl. Soft Comput. 12(11), 3442\u20133451 (2012)","journal-title":"Appl. Soft Comput."},{"issue":"8","key":"1187_CR10","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/TBME.2011.2113395","volume":"58","author":"T Mar","year":"2011","unstructured":"T. Mar, S. Zaunseder, J.P. Mart\u00ednez, M. Llamedo, R. Poll, Optimization of ECG classification by means of feature selection. IEEE Trans. Biomed. Eng. 58(8), 2168\u20132177 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1187_CR11","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.asoc.2018.04.005","volume":"72","author":"HM Rai","year":"2018","unstructured":"H.M. Rai, K. Chatterjee, A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with multilayered probabilistic neural network classifier. Appl. Soft Comput. 72, 596\u2013608 (2018)","journal-title":"Appl. Soft Comput."},{"key":"1187_CR12","doi-asserted-by":"publisher","unstructured":"L. Fang, J. Xu, H. Hu, Y. Chen, P. Shi, L. Wang, H Liu, Noninvasive imaging of epicardial and endocardial potentials with low rank and sparsity constraints. IEEE Trans Biomed Eng 66(9), 2651-2662 (2019). https:\/\/doi.org\/10.1109\/TBME.2019.2894286","DOI":"10.1109\/TBME.2019.2894286"},{"issue":"9","key":"1187_CR13","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1109\/10.623058","volume":"44","author":"YH Hu","year":"1997","unstructured":"Y.H. Hu, S. Palreddy, W.J. Tompkins, A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 44(9), 891\u2013900 (1997)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"4","key":"1187_CR14","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s13534-017-0043-2","volume":"7","author":"SS Qurraie","year":"2017","unstructured":"S.S. Qurraie, R.G. Afkhami, ECG arrhythmia classification using time frequency distribution techniques. Biomed. Eng. Lett. 7(4), 325\u2013332 (2017)","journal-title":"Biomed. Eng. Lett."},{"issue":"4","key":"1187_CR15","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1109\/TBME.2003.821031","volume":"51","author":"JP Mart\u00ednez","year":"2004","unstructured":"J.P. Mart\u00ednez, R. Almeida, S. Olmos, A.P. Rocha, P. Laguna, A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570\u2013581 (2004)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1187_CR16","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.patrec.2015.11.018","volume":"70","author":"RG Afkhami","year":"2016","unstructured":"R.G. Afkhami, G. Azarnia, M.A. Tinati, Cardiac arrhythmia classification using statistical and mixture modeling features of ecg signals. Pattern Recogn. Lett. 70, 45\u201351 (2016)","journal-title":"Pattern Recogn. Lett."},{"key":"1187_CR17","doi-asserted-by":"crossref","unstructured":"T. Ince, M. Zabihi, S. Kiranyaz, M. Gabbouj, Learned vs hand-designed features for ecg beat classification: A comprehensive study. In: EMBEC & NBC 2017, pp. 551\u2013554. Springer, Berlin (2017)","DOI":"10.1007\/978-981-10-5122-7_138"},{"issue":"9","key":"1187_CR18","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aad9ed","volume":"39","author":"Z Xiong","year":"2018","unstructured":"Z. Xiong, M.P. Nash, E. Cheng, V.V. Fedorov, M.K. Stiles, J. Zhao, ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol. Meas. 39(9), 094006 (2018)","journal-title":"Physiol. Meas."},{"issue":"1","key":"1187_CR19","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","volume":"25","author":"AY Hannun","year":"2019","unstructured":"A.Y. Hannun, P. Rajpurkar, M. Haghpanahi, G.H. Tison, C. Bourn, M.P. Turakhia, A.Y. Ng, Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25(1), 65\u201369 (2019)","journal-title":"Nat. Med."},{"issue":"2","key":"1187_CR20","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/JBHI.2019.2911367","volume":"24","author":"S Saadatnejad","year":"2019","unstructured":"S. Saadatnejad, M. Oveisi, M. Hashemi, LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE J. Biomed. Health Inform. 24(2), 515\u2013523 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1187_CR21","doi-asserted-by":"publisher","first-page":"145395","DOI":"10.1109\/ACCESS.2019.2939947","volume":"7","author":"HM Lynn","year":"2019","unstructured":"H.M. Lynn, S.B. Pan, P. Kim, A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks. IEEE Access 7, 145395\u2013145405 (2019)","journal-title":"IEEE Access"},{"key":"1187_CR22","doi-asserted-by":"crossref","unstructured":"P. Xie, G. Wang, C. Zhang, M. Chen, H. Yang, T. Lv, Z. Sang, P. Zhang, Bidirectional recurrent neural network and convolutional neural network (bircnn) for ecg beat classification. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2018), pp. 2555\u20132558. IEEE","DOI":"10.1109\/EMBC.2018.8512752"},{"key":"1187_CR23","doi-asserted-by":"crossref","unstructured":"Q. Yao, X. Fan, Y. Cai, R. Wang, L. Yin, Y. Li, Time-incremental convolutional neural network for arrhythmia detection in varied-length electrocardiogram. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), (2018), pp. 754\u2013761. IEEE","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00131"},{"issue":"12","key":"1187_CR24","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 6999\u20137019 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1187_CR25","doi-asserted-by":"crossref","unstructured":"D. Li, J. Zhang, Q. Zhang, X. Wei, Classification of ecg signals based on 1d convolution neural network. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), (2017), pp. 1\u20136. IEEE","DOI":"10.1109\/HealthCom.2017.8210784"},{"issue":"7","key":"1187_CR26","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.3390\/s20072136","volume":"20","author":"C-H Hsieh","year":"2020","unstructured":"C.-H. Hsieh, Y.-S. Li, B.-J. Hwang, C.-H. Hsiao, Detection of atrial fibrillation using 1d convolutional neural network. Sensors 20(7), 2136 (2020)","journal-title":"Sensors"},{"issue":"12","key":"1187_CR27","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1109\/TLA.2019.9011541","volume":"17","author":"AAS Le\u00f3n","year":"2019","unstructured":"A.A.S. Le\u00f3n, J.R.N. Alvarez, 1d convolutional neural network for detecting ventricular heartbeats. IEEE Lat. Am. Trans. 17(12), 1970\u20131977 (2019)","journal-title":"IEEE Lat. Am. Trans."},{"key":"1187_CR28","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","volume":"102","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2018","unstructured":"\u00d6. Y\u0131ld\u0131r\u0131m, P. P\u0142awiak, R.-S. Tan, U.R. Acharya, Arrhythmia detection using deep convolutional neural network with long duration ecg signals. Comput. Biol. Med. 102, 411\u2013420 (2018)","journal-title":"Comput. Biol. Med."},{"key":"1187_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, D.J. Inman, 1d convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)","journal-title":"Mech. Syst. Signal Process."},{"issue":"5","key":"1187_CR30","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TBME.2021.3135622","volume":"69","author":"J Malik","year":"2021","unstructured":"J. Malik, O.C. Devecioglu, S. Kiranyaz, T. Ince, M. Gabbouj, Real-time patient-specific ECG classification by 1d self-operational neural networks. IEEE Trans. Biomed. Eng. 69(5), 1788\u20131801 (2021)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1187_CR31","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ymeth.2021.04.021","volume":"202","author":"C-Y Chen","year":"2022","unstructured":"C.-Y. Chen, Y.-T. Lin, S.-J. Lee, W.-C. Tsai, T.-C. Huang, Y.-H. Liu, M.-C. Cheng, C.-Y. Dai, Automated ECG classification based on 1d deep learning network. Methods 202, 127\u2013135 (2022)","journal-title":"Methods"},{"key":"1187_CR32","doi-asserted-by":"crossref","unstructured":"J. Ferretti, V. Randazzo, G. Cirrincione, E. Pasero, 1-d convolutional neural network for ecg arrhythmia classification. In Progresses in Artificial Intelligence and Neural Systems, (Springer, Berlin, 2021), pp. 269\u2013279.","DOI":"10.1007\/978-981-15-5093-5_25"},{"key":"1187_CR33","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1016\/j.procs.2018.05.045","volume":"132","author":"S Singh","year":"2018","unstructured":"S. Singh, S.K. Pandey, U. Pawar, R.R. Janghel, Classification of ECG arrhythmia using recurrent neural networks. Proc. Comput. Sci. 132, 1290\u20131297 (2018)","journal-title":"Proc. Comput. Sci."},{"key":"1187_CR34","doi-asserted-by":"publisher","first-page":"125380","DOI":"10.1109\/ACCESS.2020.3006707","volume":"8","author":"X Xu","year":"2020","unstructured":"X. Xu, S. Jeong, J. Li, Interpretation of electrocardiogram (ECG) rhythm by combined CNN and BILSTM. IEEE Access 8, 125380\u2013125388 (2020)","journal-title":"IEEE Access"},{"key":"1187_CR35","doi-asserted-by":"crossref","unstructured":"B. Dhananjay, N.P. Venkatesh, A. Bhardwaj, J. Sivaraman, Design and development of LSTM-RNN model for the prediction of rr intervals in ECG signals. In Proceedings of the International e-Conference on Intelligent Systems and Signal Processing, (Springer, 2022), pp. 133\u2013141","DOI":"10.1007\/978-981-16-2123-9_10"},{"issue":"4","key":"1187_CR36","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1109\/TIM.2019.2910342","volume":"69","author":"B Hou","year":"2019","unstructured":"B. Hou, J. Yang, P. Wang, R. Yan, LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Trans. Instrum. Meas. 69(4), 1232\u20131240 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1187_CR37","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1109\/LSP.2020.3036314","volume":"27","author":"E Prabhakararao","year":"2020","unstructured":"E. Prabhakararao, S. Dandapat, Attentive RNN-based network to fuse 12-lead ECG and clinical features for improved myocardial infarction diagnosis. IEEE Signal Process. Lett. 27, 2029\u20132033 (2020)","journal-title":"IEEE Signal Process. Lett."},{"issue":"1","key":"1187_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-84374-8","volume":"11","author":"K Weimann","year":"2021","unstructured":"K. Weimann, T.O. Conrad, Transfer learning for ECG classification. Sci. Rep. 11(1), 1\u201312 (2021)","journal-title":"Sci. Rep."},{"key":"1187_CR39","doi-asserted-by":"crossref","unstructured":"S. Kiranyaz, T. Ince, R. Hamila, M. Gabbouj, Convolutional neural networks for patient-specific ECG classification. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2015), pp. 2608\u20132611. IEEE","DOI":"10.1109\/EMBC.2015.7318926"},{"key":"1187_CR40","doi-asserted-by":"publisher","first-page":"35592","DOI":"10.1109\/ACCESS.2020.2974712","volume":"8","author":"AM Shaker","year":"2020","unstructured":"A.M. Shaker, M. Tantawi, H.A. Shedeed, M.F. Tolba, Generalization of convolutional neural networks for ECG classification using generative adversarial networks. IEEE Access 8, 35592\u201335605 (2020)","journal-title":"IEEE Access"},{"issue":"5","key":"1187_CR41","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/JBHI.2019.2942938","volume":"24","author":"J Niu","year":"2019","unstructured":"J. Niu, Y. Tang, Z. Sun, W. Zhang, Inter-patient ECG classification with symbolic representations and multi-perspective convolutional neural networks. IEEE J. Biomed. Health Inform. 24(5), 1321\u20131332 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"1187_CR42","doi-asserted-by":"publisher","first-page":"18738","DOI":"10.1038\/s41598-021-97118-5","volume":"11","author":"S Aziz","year":"2021","unstructured":"S. Aziz, S. Ahmed, M.-S. Alouini, ECG-based machine-learning algorithms for heartbeat classification. Sci. Rep. 11(1), 18738 (2021)","journal-title":"Sci. Rep."},{"key":"1187_CR43","doi-asserted-by":"crossref","unstructured":"S. Jang, S.-E. Moon, J.-S. Lee, Eeg-based video identification using graph signal modeling and graph convolutional neural network. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2018), pp. 3066\u20133070. IEEE","DOI":"10.1109\/ICASSP.2018.8462207"},{"key":"1187_CR44","doi-asserted-by":"crossref","unstructured":"H.J. Nussbaumer, The fast fourier transform. In Fast Fourier Transform and Convolution Algorithms, (Springer, Berlin, 1981), pp. 80\u2013111","DOI":"10.1007\/978-3-662-00551-4_4"},{"key":"1187_CR45","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1187_CR46","unstructured":"T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks. (2016) arXiv preprint arXiv:1609.02907"},{"issue":"2","key":"1187_CR47","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"D.K. Hammond, P. Vandergheynst, R. Gribonval, Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129\u2013150 (2011)","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"1187_CR48","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"S. Kullback, R.A. Leibler, On information and sufficiency. Ann. Math. Stat. 22, 79\u201386 (1951)","journal-title":"Ann. Math. Stat."},{"key":"1187_CR49","unstructured":"M.I. Belghazi, A. Baratin, S. Rajeswar, S. Ozair, Y. Bengio, A. Courville, R.D Hjelm, Mine: Mutual information neural estimation (2018)"},{"issue":"12","key":"1187_CR50","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/abc960","volume":"41","author":"EAP Alday","year":"2020","unstructured":"E.A.P. Alday, A. Gu, A.J. Shah, C. Robichaux, A.-K.I. Wong, C. Liu, F. Liu, A.B. Rad, A. Elola, S. Seyedi et al., Classification of 12-lead ECGS: the physionet\/computing in cardiology challenge 2020. Physiol. Meas. 41(12), 124003 (2020)","journal-title":"Physiol. Meas."},{"key":"1187_CR51","unstructured":"D.P. Kingma, J. Ba, Adam: A method for stochastic optimization. (2014) arXiv preprint arXiv:1412.6980"},{"issue":"1","key":"1187_CR52","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","volume":"17","author":"A Tharwat","year":"2021","unstructured":"A. Tharwat, Classification assessment methods. Appl. Comput. Inform. 17(1), 168\u2013192 (2021)","journal-title":"Appl. Comput. Inform."},{"key":"1187_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhao, W., Liu, S.: Se-ecgnet: A multi-scale deep residual network with squeeze-and-excitation module for ecg signal classification. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), (2020), pp. 2685\u20132691. IEEE","DOI":"10.1109\/BIBM49941.2020.9313548"},{"key":"1187_CR54","doi-asserted-by":"crossref","unstructured":"D. Borra, A. Andal\u00f2, S. Severi, C. Corsi, On the application of convolutional neural networks for 12-lead ECG multi-label classification using datasets from multiple centers. In 2020 Computing in Cardiology, (2020), pp. 1\u20134. IEEE","DOI":"10.22489\/CinC.2020.349"},{"key":"1187_CR55","doi-asserted-by":"crossref","unstructured":"Z. Jiang, T.P. Almeida, F.S. Schlindwein, G.A. Ng, H. Zhou, X. Li, Diagnostic of multiple cardiac disorders from 12-lead ecgs using graph convolutional network based multi-label classification. In 2020 Computing in Cardiology, (2020), pp. 1\u20134. IEEE","DOI":"10.22489\/CinC.2020.135"},{"key":"1187_CR56","doi-asserted-by":"crossref","unstructured":"H. Wang, W. Zhao, Z. Li, D. Jia, C. Yan, J. Hu, J. Fang, M. Yang, A weighted graph attention network based method for multi-label classification of electrocardiogram abnormalities. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2020), pp. 418\u2013421. IEEE","DOI":"10.1109\/EMBC44109.2020.9175981"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-024-01187-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-024-01187-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-024-01187-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T13:46:30Z","timestamp":1730295990000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-024-01187-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1187"],"URL":"https:\/\/doi.org\/10.1186\/s13634-024-01187-3","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"93"}}