{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:31:57Z","timestamp":1726032717401},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030228842"},{"type":"electronic","value":"9783030228859"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-22885-9_11","type":"book-chapter","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T17:12:18Z","timestamp":1561050738000},"page":"110-123","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ECG Beat Classification Based on Stationary Wavelet Transform"],"prefix":"10.1007","author":[{"given":"Lahcen","family":"El Bouny","sequence":"first","affiliation":[]},{"given":"Mohammed","family":"Khalil","sequence":"additional","affiliation":[]},{"given":"Abdellah","family":"Adib","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"issue":"2","key":"11_CR1","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/10.740882","volume":"46","author":"V Afonso","year":"1999","unstructured":"Afonso, V., Tompkins, W., Nquyen, T., Luo, S.: ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46(2), 192\u2013201 (1999)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/S0010-4825(01)00009-9","volume":"31","author":"S Benitez","year":"2001","unstructured":"Benitez, S., Gaydecki, P., Zaidi, A., Fitzpatrick, A.: The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31, 399\u2013406 (2001)","journal-title":"Comput. Biol. Med."},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 144\u2013152. ACM, New York (1992). \n                    https:\/\/doi.org\/10.1145\/130385.130401","DOI":"10.1145\/130385.130401"},{"issue":"7","key":"11_CR4","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1049\/iet-spr.2013.0391","volume":"8","author":"F Bouaziz","year":"2014","unstructured":"Bouaziz, F., Boutana, D., Benidir, M.: Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET. Signal Process. 8(7), 774\u2013782 (2014). \n                    https:\/\/doi.org\/10.1049\/iet-spr.2013.0391","journal-title":"IET. Signal Process."},{"issue":"3","key":"11_CR5","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.cmpb.2005.11.012","volume":"82","author":"S Chen","year":"2006","unstructured":"Chen, S., Chen, H., Chan, H.: A real-time QRS method based on moving-averaging incorporating with wavelet denoising. Comput. Methods Prog. Biomed. 82(3), 187\u2013195 (2006)","journal-title":"Comput. Methods Prog. Biomed."},{"key":"11_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An Introduction to Support Vector Machines and Other Kernel based Learning Methods","author":"N Christianini","year":"2000","unstructured":"Christianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel based Learning Methods. Cambridge University Press, Cambridge (2000)"},{"key":"11_CR7","unstructured":"Clifford, G.D., Azuaje, F., McSharry, P.E.: Advanced methods and tools for ECG data analysis. Engineering in Medicine and Biology Series, Artech House, Inc. (2006). ISBN 1580539661"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","volume":"161","author":"O Faust","year":"2018","unstructured":"Faust, O., Hagiwara, Y., Jen Hong, T., Shu Lih, O., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: a review. Comput. Methods Prog. Biomed. 161, 1\u201313 (2018). \n                    https:\/\/doi.org\/10.1016\/j.cmpb.2018.04.005","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"4","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1016\/j.dsp.2009.10.017","volume":"20","author":"ZE Hadj Slimane","year":"2010","unstructured":"Hadj Slimane, Z.E., Nait-Ali, A.: QRS complex detection using Empirical Mode Decomposition. Digit. Signal Process. 20(4), 1221\u20131228 (2010)","journal-title":"Digit. Signal Process."},{"key":"11_CR10","first-page":"66","volume":"26","author":"YH Hu","year":"1993","unstructured":"Hu, Y.H., Tompkins, W., Urrusti, J., Afonso, V.: Applications of artificial neural networks for ECG signal detection and classification. J. Electrocardiol. 26, 66\u201373 (1993)","journal-title":"J. Electrocardiol."},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.cmpb.2011.10.002","volume":"105","author":"Y Kutlu","year":"2012","unstructured":"Kutlu, Y., Kuntalp, D.: Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput. Methods Prog. Biomed. 105, 257\u2013267 (2012). \n                    https:\/\/doi.org\/10.1016\/j.cmpb.2011.10.002","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"1","key":"11_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/10.362922","volume":"42","author":"C Li","year":"1995","unstructured":"Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points by wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21\u201328 (1995)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","volume":"127","author":"Eduardo Jos\u00e9 da S. Luz","year":"2016","unstructured":"Luz, E.J.d.S., Schwartz, W.R., Camara-Chavez, G., Menotti, D.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Prog. Biomed. 127, 144\u2013164 (2016). \n                    https:\/\/doi.org\/10.1016\/j.cmpb.2015.12.008","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"11_CR14","unstructured":"Mark, R., Moody, G.: MIT-BIH-Arrhythmia Database. \n                    http:\/\/www.physionet.org\/physiobank\/database\/mitdb"},{"issue":"4","key":"11_CR15","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1109\/TBME.2003.821031","volume":"51","author":"J Martinez","year":"2004","unstructured":"Martinez, J., Almeida, R., Olmos, S., Rocha, A., Laguna, P.: A wavelet based ECG delineator: evaluation on standard database. IEEE Trans. Biomed. Eng. 51(4), 570\u2013581 (2004)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"11_CR16","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","volume":"8","author":"R Martis","year":"2013","unstructured":"Martis, R., Acharya, U., Lim, C.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437\u2013448 (2013)","journal-title":"Biomed. Signal Process. Control"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"11792","DOI":"10.1016\/j.eswa.2012.04.072","volume":"39","author":"R Martis","year":"2012","unstructured":"Martis, R., Acharya, U., Mandana, K., Ray, A., Chakraborty, C.: Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst. Appl. 39, 11792\u201311800 (2012). \n                    https:\/\/doi.org\/10.1016\/j.eswa.2012.04.072","journal-title":"Expert Syst. Appl."},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1016\/j.eswa.2009.09.021","volume":"37","author":"M Moavenian","year":"2010","unstructured":"Moavenian, M., Khorrami, H.: A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Syst. Appl. 37, 3088\u20133093 (2010). \n                    https:\/\/doi.org\/10.1016\/j.eswa.2009.09.021","journal-title":"Expert Syst. Appl."},{"key":"11_CR19","series-title":"Lecture Notes in Statistics","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-1-4612-2544-7_17","volume-title":"Wavelets and Statistics","author":"G Nason","year":"1995","unstructured":"Nason, G., Silverman, B.: The stationary wavelet transform and some statistical applications. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics. LNS, vol. 103, pp. 281\u2013299. Springer, New York (1995). \n                    https:\/\/doi.org\/10.1007\/978-1-4612-2544-7_17"},{"issue":"32","key":"11_CR20","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TBME.1985.325532","volume":"3","author":"J Pan","year":"1985","unstructured":"Pan, J., Tompkins, W.: A real time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3(32), 230\u2013236 (1985)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/10.469381","volume":"42","author":"R Poli","year":"1995","unstructured":"Poli, R., Cagnoni, S., Valli, G.: Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans. Biomed. Eng. 42, 1137\u20131141 (1995)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.measurement.2017.05.022","volume":"24","author":"S Sahoo","year":"2017","unstructured":"Sahoo, S., Kanungo, B., Behera, S., Sabut, S.: Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 24, 63\u201371 (2017). \n                    https:\/\/doi.org\/10.1016\/j.measurement.2017.05.022","journal-title":"Measurement"},{"issue":"11","key":"11_CR23","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TBME.1984.325393","volume":"31","author":"N Thakor","year":"1984","unstructured":"Thakor, N., Webstor, J., Thompkins, W.: Estimation of the QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. 31(11), 702\u2013706 (1984)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR24","volume-title":"Statistical Learning Theory","author":"V Vapnik","year":"1998","unstructured":"Vapnik, V.: Statistical Learning Theory. Willey, New York (1998)"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.cmpb.2013.05.011","volume":"111","author":"Z Zidelmala","year":"2013","unstructured":"Zidelmala, Z., Amirou, A., Ould Abdeslam, D., Merckle, J.: ECG beat classifcation using a cost sensitive classifier. Comput. Methods Prog. Biomed. 111, 570\u2013577 (2013). \n                    https:\/\/doi.org\/10.1016\/j.cmpb.2013.05.011","journal-title":"Comput. Methods Prog. Biomed."}],"container-title":["Lecture Notes in Computer Science","Mobile, Secure, and Programmable Networking"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-22885-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T17:15:44Z","timestamp":1561050944000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-22885-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030228842","9783030228859"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-22885-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"21 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MSPN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile, Secure, and Programmable Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mohammedia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mspn2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.adda-association.org\/mspn-2019\/Home.html","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"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"48","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"48% - 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"}},{"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"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}