A global training model for beat classification using basic electrocardiogram morphological features Sumesh, Shubha, Yearwood, John Leighton, Huda, MD Shamsul and Ahmad, S 2022, A global training model for beat classification using basic electrocardiogram morphological features, Computers, Materials and Continua, vol. 70, no. 3, pp. 4503-4521, doi: 10.32604/cmc.2022.015474. Attached Files Name Description MIMEType Size Downloads Title A global training model for beat classification using basic electrocardiogram morphological features Author(s) Sumesh, Shubha orcid.org/0000-0002-7562-6767 Yearwood, John Leighton orcid.org/0000-0001-7848-0508 Huda, MD Shamsul Ahmad, S Journal name Computers, Materials and Continua Volume number 70 Issue number 3 Start page 4503 End page 4521 Total pages 19 Publisher Tech Science Press Place of publication Encino, Calif. Publication date 2022 ISSN 1546-2218 1546-2226 Keyword(s) adaptive training Computer Science Computer Science, Information Systems ECG global HRV Materials Science Materials Science, Multidisciplinary morphological feature multilayer perceptron (MLP) NEURAL-NETWORK random forest (RF) Science & Technology support vector machine (SVM) Technology Language eng DOI 10.32604/cmc.2022.015474 Field of Research 0103 Numerical and Computational Mathematics 0912 Materials Engineering 0915 Interdisciplinary Engineering HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30158023 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 29 Oct 2021, 06:45:39 EST Fri, 29 Oct 2021, 15:36:16 EST Mon, 01 Nov 2021, 12:49:35 EST Mon, 01 Nov 2021, 15:57:57 EST Tue, 02 Nov 2021, 14:52:14 EST Tue, 02 Nov 2021, 15:09:19 EST Mon, 08 Nov 2021, 07:33:45 EST Mon, 08 Nov 2021, 07:35:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 73 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 01 Nov 2021, 12:49:35 EST