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Sparse Matrix for ECG Identification with Two-Lead Features

Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping...

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Detalles Bibliográficos
Autores principales: Tseng, Kuo-Kun, Luo, Jiao, Hegarty, Robert, Wang, Wenmin, Haiting, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415669/
https://www.ncbi.nlm.nih.gov/pubmed/25961074
http://dx.doi.org/10.1155/2015/656807
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author Tseng, Kuo-Kun
Luo, Jiao
Hegarty, Robert
Wang, Wenmin
Haiting, Dong
author_facet Tseng, Kuo-Kun
Luo, Jiao
Hegarty, Robert
Wang, Wenmin
Haiting, Dong
author_sort Tseng, Kuo-Kun
collection PubMed
description Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.
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spelling pubmed-44156692015-05-10 Sparse Matrix for ECG Identification with Two-Lead Features Tseng, Kuo-Kun Luo, Jiao Hegarty, Robert Wang, Wenmin Haiting, Dong ScientificWorldJournal Research Article Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods. Hindawi Publishing Corporation 2015 2015-04-16 /pmc/articles/PMC4415669/ /pubmed/25961074 http://dx.doi.org/10.1155/2015/656807 Text en Copyright © 2015 Kuo-Kun Tseng et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Tseng, Kuo-Kun
Luo, Jiao
Hegarty, Robert
Wang, Wenmin
Haiting, Dong
Sparse Matrix for ECG Identification with Two-Lead Features
title Sparse Matrix for ECG Identification with Two-Lead Features
title_full Sparse Matrix for ECG Identification with Two-Lead Features
title_fullStr Sparse Matrix for ECG Identification with Two-Lead Features
title_full_unstemmed Sparse Matrix for ECG Identification with Two-Lead Features
title_short Sparse Matrix for ECG Identification with Two-Lead Features
title_sort sparse matrix for ecg identification with two-lead features
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415669/
https://www.ncbi.nlm.nih.gov/pubmed/25961074
http://dx.doi.org/10.1155/2015/656807
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