Human identification from ECG signals via sparse representation of local segments

Journal article


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. 20(10), pp. 937 - 940. https://doi.org/10.1109/LSP.2013.2267593
AuthorsWang, Jin, She, Mary, Nahavandi, Saeid and Kouzani, Abbas
Abstract

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.

Keywordssparse coding; dictionary learning; local features
Year2013
JournalIEEE Signal Processing Letters
Journal citation20 (10), pp. 937 - 940
PublisherInstitute of Electrical and Electronics Engineers
ISSN1070-9908
Digital Object Identifier (DOI)https://doi.org/10.1109/LSP.2013.2267593
Scopus EID2-s2.0-84881158122
Page range937 - 940
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationUnited States of America
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