{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:54:54Z","timestamp":1767138894330,"version":"build-2238731810"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811659393","type":"print"},{"value":"9789811659409","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-981-16-5940-9_37","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T19:04:08Z","timestamp":1631214248000},"page":"481-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ECG-Based Arrhythmia Detection Using Attention-Based Convolutional Neural Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4429-6133","authenticated-orcid":false,"given":"Renxing","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2137-8785","authenticated-orcid":false,"given":"Runnan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"37_CR1","volume-title":"Global Atlas on Cardiovascular Disease Prevention and Control","author":"S Mendis","year":"2011","unstructured":"Mendis, S., Puska, P., Norrving, B., Organization, W.H., et al.: Global Atlas on Cardiovascular Disease Prevention and Control. World Health Organization, Geneva (2011)"},{"issue":"6","key":"37_CR2","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.jelectrocard.2007.06.023","volume":"40","author":"R Mehra","year":"2007","unstructured":"Mehra, R.: Global public health problem of sudden cardiac death. J. Electrocardiol. 40(6), 118\u2013122 (2007)","journal-title":"J. Electrocardiol."},{"issue":"11","key":"37_CR3","doi-asserted-by":"publisher","first-page":"3058","DOI":"10.1109\/78.726818","volume":"46","author":"T Stamkopoulos","year":"1998","unstructured":"Stamkopoulos, T., Diamantaras, K., Maglaveras, N., Strintzis, M.: ECG analysis using nonlinear PCA neural networks for is chemia detection. IEEE Trans. Signal Process. 46(11), 3058\u20133067 (1998)","journal-title":"IEEE Trans. Signal Process."},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Leijdekkers, P., Gay, V.: A self-test to detect a heart attack using a mobile phone and wearable sensors. In: 2008 21st IEEE International Symposium on Computer-Based Medical Systems, pp. IEEE (2008)","DOI":"10.1109\/CBMS.2008.59"},{"issue":"3","key":"37_CR5","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1161\/01.STR.10.3.253","volume":"10","author":"DS Goldstein","year":"1979","unstructured":"Goldstein, D.S.: The electrocardiogram in stroke: relationship to pathophysiological type and comparison with prior tracings. Stroke 10(3), 253\u2013259 (1979)","journal-title":"Stroke"},{"key":"37_CR6","doi-asserted-by":"publisher","first-page":"102262","DOI":"10.1016\/j.bspc.2020.102262","volume":"64","author":"BM Mathunjwa","year":"2021","unstructured":"Mathunjwa, B.M., Lin, Y.-T., Lin, C.-H., Abbod, M.F., Shieh, J.-S.: ECG arrhythmia classification by using a recurrence plot and convolutional neural network. Biomed. Signal Process. Control 64, 102262 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"37_CR7","doi-asserted-by":"publisher","first-page":"101856","DOI":"10.1016\/j.artmed.2020.101856","volume":"106","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Liu, A., Gao, M., Chen, X., Zhang, X., Chen, X.: ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif. Intell. Med. 106, 101856 (2020)","journal-title":"Artif. Intell. Med."},{"key":"37_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","volume":"405","author":"U Rajendra Acharya","year":"2017","unstructured":"Rajendra Acharya, U., Hamido Fujita, O., Lih, S., Hagiwara, Y., Tan, J.H., Adam, M.: Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 405, 81\u201390 (2017)","journal-title":"Inf. Sci."},{"key":"37_CR9","doi-asserted-by":"publisher","first-page":"102324","DOI":"10.1016\/j.bspc.2020.102324","volume":"64","author":"ST Sanamdikar","year":"2021","unstructured":"Sanamdikar, S.T., Hamde, S.T., Asutkar, V.G.: Classification and analysis of cardiac arrhythmia based on incremental support vector regression on IOT platform. Biomed. Signal Process. Control 64, 102324 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"37_CR10","doi-asserted-by":"publisher","first-page":"114809","DOI":"10.1016\/j.eswa.2021.114809","volume":"174","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., Zhai, X., Tin, C.: Fully automatic electrocardiogram classification system based on generative adversarial network with auxiliary classifier. Expert Syst. Appl. 174, 114809 (2021)","journal-title":"Expert Syst. Appl."},{"key":"37_CR11","doi-asserted-by":"publisher","first-page":"105479","DOI":"10.1016\/j.cmpb.2020.105479","volume":"193","author":"A Chen","year":"2020","unstructured":"Chen, A., et al.: Multi-information fusion neural networks for arrhythmia automatic detection. Comput. Methods programs Biomed. 193, 105479 (2020)","journal-title":"Comput. Methods programs Biomed."},{"key":"37_CR12","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.future.2020.10.024","volume":"116","author":"X Xie","year":"2021","unstructured":"Xie, X., et al.: A multi-stage denoising framework for ambulatory ECG signal based on domain knowledge and motion artifact detection. Futur. Gener. Comput. Syst. 116, 103\u2013116 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"37_CR13","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.inffus.2019.06.024","volume":"53","author":"Q Yao","year":"2020","unstructured":"Yao, Q., Wang, R., Fan, X., Liu, J., Li, Y.: Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Inf. Fusion 53, 174\u2013182 (2020)","journal-title":"Inf. Fusion"},{"key":"37_CR14","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","volume":"100","author":"U Rajendra Acharya","year":"2018","unstructured":"Rajendra Acharya, U., Shu Lih, O., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270\u2013278 (2018)","journal-title":"Comput. Biol. Med."},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Soman, S., Rai, A., Srivastava, P.K.: Deep learning for health informatics: recent trends and future directions. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1665\u20131670 (2017)","DOI":"10.1109\/ICACCI.2017.8126082"},{"key":"37_CR16","series-title":"Studies in Big Data","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33966-1","volume-title":"Deep Learning Techniques for Biomedical and Health Informatics","year":"2020","unstructured":"Dash, S., Acharya, B.R., Mittal, M., Abraham, A., Kelemen, A. (eds.): Deep Learning Techniques for Biomedical and Health Informatics. SBD, vol. 68. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-33966-1"},{"key":"37_CR17","unstructured":"Kwak, G.H.-J., Hui, P.: DeepHealth: deep learning for health informatics reviews, challenges, and opportunities on medical imaging, electronic health records, genomics, sensing, and online communication health. arXiv preprint arXiv (2019)"},{"key":"37_CR18","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-33966-1_6","volume-title":"Deep Learning Techniques for Biomedical and Health Informatics","author":"J Saha","year":"2020","unstructured":"Saha, J., Chowdhury, C., Biswas, S.: Review of machine learning and deep learning based recommender systems for health informatics. In: Dash, S., Acharya, B.R., Mittal, M., Abraham, A., Kelemen, A. (eds.) Deep Learning Techniques for Biomedical and Health Informatics. SBD, vol. 68, pp. 101\u2013126. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-33966-1_6"},{"issue":"11","key":"37_CR19","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1001\/jama.2018.11103","volume":"320","author":"C David Naylor","year":"2018","unstructured":"David Naylor, C.: On the prospects for a (deep) learning health care system. JAMA 320(11), 1099 (2018)","journal-title":"JAMA"},{"issue":"13","key":"37_CR20","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","volume":"319","author":"AL Beam","year":"2018","unstructured":"Beam, A.L., Kohane, I.S.: Big data and machine learning in health care. JAMA 319(13), 1317\u20131318 (2018)","journal-title":"JAMA"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Ardabili, S., Varkonyi-Koczy, A.R.: List of deep learning models. In: International Conference on Global Research and Education, pp. 202\u2013214 (2019)","DOI":"10.1007\/978-3-030-36841-8_20"},{"issue":"5","key":"37_CR22","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","volume":"22","author":"B Shickel","year":"2018","unstructured":"Shickel, B., Tighe, P.J., Bihorac, A., Rashidi, P.: Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J. Biomed. Health Inform. 22(5), 1589\u20131604 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Navamani, T.M.: Efficient deep learning approaches for health informatics. In: Deep Learning and Parallel Computing Environment for Bioengineering Systems, pp. 123\u2013137 (2019)","DOI":"10.1016\/B978-0-12-816718-2.00014-2"},{"issue":"11","key":"37_CR24","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1001\/jama.2018.11100","volume":"320","author":"G Hinton","year":"2018","unstructured":"Hinton, G.: Deep learning\u2014a technology with the potential to transform health care. JAMA 320(11), 1101\u20131102 (2018)","journal-title":"JAMA"},{"key":"37_CR25","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.patrec.2020.09.033","volume":"140","author":"RJ Martis","year":"2020","unstructured":"Martis, R.J., Lin, H., Javadi, B., Fernandes, S.L., Yasmin, M.: Editorial of the special issue DLHI: deep learning in medical imaging and health informatics. Pattern Recogn. Lett. 140, 116\u2013118 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"37_CR26","series-title":"Translational Systems Sciences","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-981-15-3781-3_1","volume-title":"Health Informatics","author":"H Matsushita","year":"2021","unstructured":"Matsushita, H.: Innovation in health informatics. In: Matsushita, H. (ed.) Health Informatics. TSS, vol. 24, pp. 1\u201323. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-3781-3_1"},{"key":"37_CR27","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1136\/heart.87.3.220","volume":"87","author":"M Malik","year":"2002","unstructured":"Malik, M., F\u00e4rbom, P., Batchvarov, V., et al.: Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval. Heart 87, 220\u2013228 (2002)","journal-title":"Heart"},{"issue":"12","key":"37_CR28","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1016\/j.hrthm.2014.08.026","volume":"11","author":"H Bogossian","year":"2014","unstructured":"Bogossian, H., Frommeyer, G., Ninios, I., et al.: New formula for evaluation of the QT interval in patients with left bundle branch block. Heart Rhythm 11(12), 2273\u20132277 (2014)","journal-title":"Heart Rhythm"},{"issue":"8","key":"37_CR29","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1016\/j.amjcard.2003.12.055","volume":"93","author":"PM Rautaharju","year":"2004","unstructured":"Rautaharju, P.M., Zhang, Z.M., Prineas, R., Heiss, G.: Assessment of prolonged QT and JT intervals in ventricular conduction defects. Am. J. Cardiol. 93(8), 1017\u20131021 (2004)","journal-title":"Am. J. Cardiol."},{"issue":"10","key":"37_CR30","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1016\/j.hrthm.2016.06.030","volume":"13","author":"R Sriwattanakomen","year":"2016","unstructured":"Sriwattanakomen, R., Mukamal, K.J., Shvilkin, A.: A novel algorithm to predict the QT interval during intrinsic atrioventricular conduction from an electrocardiogram obtained during ventricular pacing. Heart Rhythm 13(10), 2076\u20132082 (2016)","journal-title":"Heart Rhythm"},{"issue":"3","key":"37_CR31","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45\u201350 (2001)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"issue":"2","key":"37_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.bspc.2012.08.004","volume":"8","author":"RJ Martis","year":"2013","unstructured":"Martis, R.J., Rajendra Acharya, U., Mandana, K.M., Ray, A.K., Chakraborty, C.: Cardiac decision making using higher order spectra. Biomed. Signal Process. Control 8(2), 193\u2013203 (2013)","journal-title":"Biomed. Signal Process. Control"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, J., Zhang, Q., Wei, X.: Classification of ECG signals based on 1D convolution neural network.\u00a0In: 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services, Healthcom, pp. 1\u201316. IEEE (2017)","DOI":"10.1109\/HealthCom.2017.8210784"},{"key":"37_CR34","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.compbiomed.2018.06.002","volume":"102","author":"O Shu Lih","year":"2018","unstructured":"Shu Lih, O., Ng, E.Y.K., Tan, R.S., Rajendra Acharya, U.: Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput. Biol. Med. 102, 278\u2013287 (2018)","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"37_CR35","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.3390\/s19112558","volume":"19","author":"Y Ji","year":"2019","unstructured":"Ji, Y., Zhang, S., Xiao, W.: Electrocardiogram classification based on faster regions with convolutional neural network. Sensors 19(11), 2558 (2019)","journal-title":"Sensors"},{"key":"37_CR36","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.compbiomed.2018.12.012","volume":"105","author":"O Shu Lih","year":"2019","unstructured":"Shu Lih, O., Ng, E.Y.K., Tan, R.S., Rajendra Acharya, U.: Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types. Comput. Biol. Med. 105, 92\u2013101 (2019). https:\/\/doi.org\/10.1016\/j.compbiomed.2018.12.012","journal-title":"Comput. Biol. Med."}],"updated-by":[{"DOI":"10.1007\/978-981-16-5940-9_41","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000}}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-5940-9_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:10:16Z","timestamp":1710241816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-5940-9_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811659393","9789811659409"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-5940-9_37","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"10 September 2021","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"In the originally published version, in chapter \u201cECG-Based Arrhythmia Detection Using Attention-Based Convolutional Neural Network\u201d the author referred to a wrong table \u2013 Table 5. The citation at the bottom of page 498 was changed from Table 5 to Table 3.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.icpcsee.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"256","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":"81","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":"0","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":"32% - 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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}