{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:37:29Z","timestamp":1763141849718,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031067938"},{"type":"electronic","value":"9783031067945"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06794-5_33","type":"book-chapter","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T23:03:27Z","timestamp":1656889407000},"page":"405-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on ECG Signal Classification Based on Data Enhancement of Generative Adversarial Network"],"prefix":"10.1007","author":[{"given":"Jian","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Hui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"issue":"10","key":"33_CR1","doi-asserted-by":"publisher","first-page":"7178","DOI":"10.1109\/JIOT.2021.3108792","volume":"9","author":"L Sun","year":"2022","unstructured":"Sun, L., Wang, Y., Qu, Z., Xiong, N.N.: BeatClass: a sustainable ecg classification system in iot-based ehealth. IEEE Internet Things J. 9(10), 7178\u20137195 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3108792","journal-title":"IEEE Internet Things J."},{"key":"33_CR2","first-page":"1","volume":"2021","author":"AM Alqudah","year":"2021","unstructured":"Alqudah, A.M., Qazan, S., Al-Ebbini, L., Alquran, H., Qasmieh, I.A.: ECG heartbeat arrhythmias classification: a comparison study between different types of spectrum representation and convolutional neural networks architectures. J. Ambient. Intell. Humaniz. Comput. 2021, 1\u201331 (2021)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Ge, Z., Zhu, Z., Feng, P., Zhang, S., Wang, J., Zhou, B.: ECG-signal classification using svm with multi-feature. In: 2019 8th International Symposium on Next Generation Electronics (ISNE), pp. 1\u20133 (2019)","DOI":"10.1109\/ISNE.2019.8896430"},{"issue":"1","key":"33_CR4","doi-asserted-by":"publisher","first-page":"17","DOI":"10.32604\/cmc.2022.018613","volume":"71","author":"KM Aamir","year":"2022","unstructured":"Aamir, K.M., Ramzan, M., Skinadar, S., Khan, H.U., Tariq, U.: Automatic heart disease detection by classification of ventricular arrhythmias on ecg using machine learning. Comput. Mater. Continua 71(1), 17\u201333 (2022)","journal-title":"Comput. Mater. Continua"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Subashini, A., Sairamesh, L., Raghuraman, G.: Identification and classification of heart beat by analyzing ecg signal using naive bayes. In: 2019 Third International Conference on Inventive Systems and Control (ICISC), pp. 691\u2013694 (2019)","DOI":"10.1109\/ICISC44355.2019.9036455"},{"issue":"2","key":"33_CR6","doi-asserted-by":"publisher","first-page":"493","DOI":"10.32604\/iasc.2021.015129","volume":"28","author":"J Kh-Madhloom","year":"2021","unstructured":"Kh-Madhloom, J., Khanapi, M., Baharon, M.R.: Ecg encryption enhancement technique with multiple layers of aes and DNA computing. Intell. Autom. Soft Comput. 28(2), 493\u2013512 (2021)","journal-title":"Intell. Autom. Soft Comput."},{"key":"33_CR7","doi-asserted-by":"publisher","first-page":"105948","DOI":"10.1016\/j.cmpb.2021.105948","volume":"202","author":"FM Dias","year":"2021","unstructured":"Dias, F.M., Monteiro, H.L., Cabral, T.W., Naji, R., Kuehni, M., Luz, E.J.D.S.: Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm. Comput. Methods Prog. Biomed. 202, 105948 (2021)","journal-title":"Comput. Methods Prog. Biomed."},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Thilagavathy, R., Srivatsan, R., Sreekarun, S., Sudeshna, D., Priya, P.L., Venkataramani, B.: Real-time ecg signal feature extraction and classification using support vector machine. In: 2020 International Conference on Contemporary Computing and Applications (IC3A), pp. 44\u201348 (2020)","DOI":"10.1109\/IC3A48958.2020.233266"},{"issue":"3","key":"33_CR9","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/TAI.2021.3083689","volume":"2","author":"S Bhattacharyya","year":"2021","unstructured":"Bhattacharyya, S., Majumder, S., Debnath, P., Chanda, M.: Arrhythmic heartbeat classification using ensemble of random forest and support vector machine algorithm. IEEE Trans. Artif. Intell. 2(3), 260\u2013268 (2021). https:\/\/doi.org\/10.1109\/TAI.2021.3083689","journal-title":"IEEE Trans. Artif. Intell."},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Bouaziz, F., Boutana, D., Oulhadj, H.: Diagnostic of ecg arrhythmia using wavelet analysis and k-nearest neighbor algorithm. In: 2018 International Conference on Applied Smart Systems (ICASS), pp. 1\u20136 (2018)","DOI":"10.1109\/ICASS.2018.8652020"},{"issue":"3","key":"33_CR11","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1109\/TLA.2016.7459585","volume":"14","author":"LSCD Oliveira","year":"2016","unstructured":"Oliveira, L.S.C.D., Andreao, R.V., Filho, M.S.: Bayesian network with decision threshold for heart beat classification. IEEE Lat. Am. Trans. 14(3), 1103\u20131108 (2016)","journal-title":"IEEE Lat. Am. Trans."},{"issue":"3","key":"33_CR12","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.bbe.2019.06.001","volume":"39","author":"L Guo","year":"2019","unstructured":"Guo, L., Sim, G., Matuszewski, B.: Inter-patient ECG classification with convolutional and recurrent neural networks. Biocybern. Biomed. Eng. 39(3), 868\u2013879 (2019)","journal-title":"Biocybern. Biomed. Eng."},{"issue":"5","key":"33_CR13","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/JBHI.2019.2942938","volume":"24","author":"J Niu","year":"2020","unstructured":"Niu, J., Tang, Y., Sun, Z., Zhang, W.: Inter-patient ecg classification with symbolic representations and multi-perspective convolutional neural networks. IEEE J. Biomed. Health Inform. 24(5), 1321\u20131332 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"33_CR14","doi-asserted-by":"publisher","first-page":"183","DOI":"10.32604\/csse.2022.021698","volume":"42","author":"S Karthik","year":"2022","unstructured":"Karthik, S., Santhosh, M., Kavitha, M.S., Paul, A.C.: Automated deep learning based cardiovascular disease diagnosis using ecg signals. Comput. Syst. Sci. Eng. 42(1), 183\u2013199 (2022)","journal-title":"Comput. Syst. Sci. Eng."},{"key":"33_CR15","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"33_CR16","doi-asserted-by":"publisher","first-page":"144292","DOI":"10.1109\/ACCESS.2019.2934928","volume":"7","author":"S Harada","year":"2019","unstructured":"Harada, S., Hayashi, H., Uchida, S.: Biosignal generation and latent variable analysis with recurrent generative adversarial networks. IEEE Access 7, 144292\u2013144302 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"33_CR17","doi-asserted-by":"publisher","first-page":"121","DOI":"10.3390\/electronics9010121","volume":"9","author":"Z Zheng","year":"2020","unstructured":"Zheng, Z., Chen, Z., Hu, F.: An automatic diagnosis of arrhythmias using a combination of cnn and LSTM technology. Electronics 9(1), 121 (2020)","journal-title":"Electronics"},{"issue":"1","key":"33_CR18","first-page":"78","volume":"9","author":"E Bridgman","year":"1991","unstructured":"Bridgman, E.: Aami: Association for the Advancement of Medical Instrumentation completes recommended practice on decontamination. J. Healthc. Mater. Manage. 9(1), 78 (1991)","journal-title":"J. Healthc. Mater. Manage."},{"issue":"31","key":"33_CR19","doi-asserted-by":"publisher","first-page":"22325","DOI":"10.1007\/s11042-020-09035-w","volume":"79","author":"X Song","year":"2020","unstructured":"Song, X., Yang, G., Wang, K., Huang, Y., Yuan, F., Yin, Y.: Short term ECG classification with residual-concatenate network and metric learning. Multim. Tools Appl. 79(31), 22325\u201322336 (2020)","journal-title":"Multim. Tools Appl."},{"issue":"2","key":"33_CR20","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.32604\/cmc.2021.016534","volume":"69","author":"N Mangathayaru","year":"2021","unstructured":"Mangathayaru, N., Rani, P., Janaki, V., Srinivas, K., Bai, B.M.: An attention based neural architecture for arrhythmia detection and classification from ecg signals. Comput. Mater. Continua 69(2), 2425\u20132443 (2021)","journal-title":"Comput. Mater. Continua"},{"key":"33_CR21","unstructured":"Vensko, G., Lieu, K.B., Meloche, S.A., Potter, J.C.: ITT Corp, dynamic time warping (DTW) apparatus for use in speech recognition systems. U.S. Patent 5,073,939 (1991)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Ranjeet, K.: Retained signal energy based optimal wavelet selection for denoising of ecg signal using modifide thresholding. In: 2011 International Conference on Multimedia, Signal Processing and Communication Technologies, pp. 196\u2013199. IEEE (2011)","DOI":"10.1109\/MSPCT.2011.6150473"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Sharma, L.N., Dandapat, S.: Compressed sensing for multi-lead electrocardiogram signals. In: 2012 World Congress on Information and Communication Technologies, pp. 812\u2013816. IEEE (2012)","DOI":"10.1109\/WICT.2012.6409186"},{"key":"33_CR24","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.bspc.2018.08.007","volume":"47","author":"V Mond\u00e9jar-Guerra","year":"2019","unstructured":"Mond\u00e9jar-Guerra, V., Novo, J., Rouco, J., Penedo, M.G., Ortega, M.: Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed. Signal Process. Control 47, 41\u201348 (2019)","journal-title":"Biomed. Signal Process. Control"},{"key":"33_CR25","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.eswa.2018.12.037","volume":"122","author":"A Sellami","year":"2019","unstructured":"Sellami, A., Hwang, H.: A robust deep convolutional neural network with batch-weighted loss for heartbeat classification. Exp. Syst. Appl. 122, 75\u201384 (2019)","journal-title":"Exp. Syst. Appl."},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Chen, M., Wang, G., Ding, Z., Li, J., Yang, H.: Unsupervised domain adaptation for ecg arrhythmia classification. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 304\u2013307 (2020)","DOI":"10.1109\/EMBC44109.2020.9175928"},{"issue":"4","key":"33_CR27","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3390\/healthcare8040437","volume":"8","author":"L Niu","year":"2020","unstructured":"Niu, L., Chen, C., Liu, H.: A deep-learning approach to ecg classification based on adversarial domain adaptation. Healthcare 8(4), 437 (2020)","journal-title":"Healthcare"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06794-5_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T12:13:27Z","timestamp":1657714407000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06794-5_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031067938","9783031067945"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06794-5_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Adaptive and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qinghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icais2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icaisconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1124","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"116","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}