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.

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Title Human identification from ECG signals via sparse representation of local segments
Author(s) Wang, Jin
She, MaryORCID iD for She, Mary
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Kouzani, AbbasORCID iD for Kouzani, Abbas
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
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
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