Sparse Matrix for ECG Identification with Two-Lead Features
Joint Authors
Tseng, Kuo-Kun
Luo, Jiao
Hegarty, Robert
Wang, Wenmin
Haiting, Dong
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-16
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Tseng, Kuo-Kun& Luo, Jiao& Hegarty, Robert& Wang, Wenmin& Haiting, Dong. 2015. Sparse Matrix for ECG Identification with Two-Lead Features. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1078966
Modern Language Association (MLA)
Tseng, Kuo-Kun…[et al.]. Sparse Matrix for ECG Identification with Two-Lead Features. The Scientific World Journal No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1078966
American Medical Association (AMA)
Tseng, Kuo-Kun& Luo, Jiao& Hegarty, Robert& Wang, Wenmin& Haiting, Dong. Sparse Matrix for ECG Identification with Two-Lead Features. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1078966
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078966