Citation
Hejazi, Maryamsadat and Syed Mohamed, Syed Abdul Rahman Al-Haddad and Hashim, Shaiful Jahari and Abdul Aziz, Ahmad Fazli and Singh, Yashwant Prasad
(2016)
Feature level fusion for biometric verification with two-lead ECG signals.
In: 2016 IEEE 12th IEEE International Colloquium on Signal Processing and its Applications (CSPA2016), 4-6 Mar. 2016, Melaka, Malaysia. (pp. 54-59).
Abstract
Electrocardiogram (ECG) is a new generation of biometric modality which has unique identity properties for human recognition. There are few studies on feature level fusion over short-term ECG signals for extracting non-fiducial features from autocorrelation of ECG windows with an identical length. In this paper, we provide an experimental study on fusion at feature extraction level by using autocorrelation method in conjunction with different dimensionality reduction techniques over vector sets with different window lengths from short and long-term two-lead ECG recordings. The results indicate that the window and recording lengths have significant effects on recognition rates of the fused ECG data sets.
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Additional Metadata
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Faculty of Engineering Faculty of Medicine and Health Science |
| DOI Number: | https://doi.org/10.1109/CSPA.2016.7515803 |
| Publisher: | IEEE |
| Keywords: | Dimension reduction; ECG; Feature level fusion; Gaussian OAA SVM; Non-fiducial approach |
| Depositing User: | Nabilah Mustapa |
| Date Deposited: | 06 Jun 2017 08:28 |
| Last Modified: | 06 Jun 2017 08:28 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/CSPA.2016.7515803 |
| URI: | http://psasir.upm.edu.my/id/eprint/52402 |
| Statistic Details: | View Download Statistic |
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