Abstract
This paper presents an effective method for human identification using temporal and wavelet domain features extracted from electrocardiogram (ECG) signal. Instead of directly using the ECG data of a person as the feature, first, it is shown that a few number of reflection coefficients extracted from the autocorrelation function of the data can efficiently perform the recognition task. Next, the discrete wavelet transform (DWT) coefficients are utilized as features, which offer even a better recognition performance. A combination of these two features is found to demonstrate a high within class compactness and between class separation. A pre-processing scheme is incorporated prior to feature extraction to reduce the effect of different noises and artefacts. In the recognition phase, a linear discriminant based classifier is employed, where the two features are jointly utilized. The proposed human identification method has been tested on a standard ECG database and high recognition accuracy is achieved with a low feature dimension.
Original language | English |
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Title of host publication | TENCON 2012 IEEE Region 10 Conference |
Publisher | IEEE |
Number of pages | 4 |
ISBN (Electronic) | 978-1-4673-4824-9, 978-1-4673-4822-5 |
ISBN (Print) | 978-1-4673-4823-2 |
DOIs | |
Publication status | Published - 17 Jan 2013 |
Event | 2012 IEEE Region 10 Conference: Sustainable Development Through Humanitarian Technology - Cebu, Philippines Duration: 19 Nov 2012 → 22 Nov 2012 |
Conference
Conference | 2012 IEEE Region 10 Conference: Sustainable Development Through Humanitarian Technology |
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Abbreviated title | TENCON 2012 |
Country/Territory | Philippines |
City | Cebu |
Period | 19/11/12 → 22/11/12 |
Keywords
- ECG
- human identification
- discrete wavelet transform
- reflection coefficient
- discriminant analysis