Human identification from ECG signals via sparse representation of local segments Wang, Jin, She, Mary, Nahavandi, Saeid and Kouzani, Abbas 2013, Human identification from ECG signals via sparse representation of local segments, IEEE signal processing letters, vol. 20, no. 10, pp. 937-940, doi: 10.1109/LSP.2013.2267593. Attached Files Name Description MIMEType Size Downloads Title Human identification from ECG signals via sparse representation of local segments Author(s) Wang, Jin She, Mary orcid.org/0000-0001-8191-0820 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Kouzani, Abbas orcid.org/0000-0002-6292-1214 Journal name IEEE signal processing letters Volume number 20 Issue number 10 Start page 937 End page 940 Total pages 4 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2013 ISSN 1070-9908 1558-2361 Keyword(s) sparse coding dictionary learning local features Summary This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals. Language eng DOI 10.1109/LSP.2013.2267593 Field of Research 080101 Adaptive Agents and Intelligent Robotics Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30055373 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Institute for Intelligent Systems Research and Innovation (IISRI) School of Engineering 2018 ERA Submission GTP Research Related Links Link Description Connect to published version (restricted access) 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. Versions Version Filter Type Tue, 27 Aug 2013, 12:08:46 EST Thu, 29 Aug 2013, 11:38:08 EST Fri, 13 Sep 2013, 10:38:00 EST Tue, 08 Apr 2014, 13:27:17 EST Wed, 09 Apr 2014, 09:04:59 EST Wed, 09 Apr 2014, 09:05:38 EST Fri, 13 Jun 2014, 18:04:18 EST Wed, 27 Aug 2014, 23:40:35 EST Wed, 18 Nov 2015, 07:33:09 EST Filtered Full Citation counts:   Cited 51 times in TR Web of Science Cited 58 times in Scopus Search Google Scholar Access Statistics: 756 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Tue, 27 Aug 2013, 12:08:46 EST

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