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Journal of Applied Mathematics
Volume 2012, Article ID 904905, 12 pages
Research Article

Personal Identification Based on Vectorcardiogram Derived from Limb Leads Electrocardiogram

1Deptartment of Biomedical Engineering, Hanyang University, Seoul 133-791, Republic of Korea
2School of Electrical Engineering, College of Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea

Received 28 October 2011; Accepted 23 November 2011

Academic Editor: Chang-Hwan Im

Copyright © 2012 Jongshill Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a new method for personal identification using the derived vectorcardiogram (dVCG), which is derived from the limb leads electrocardiogram (ECG). The dVCG was calculated from the standard limb leads ECG using the precalculated inverse transform matrix. Twenty-one features were extracted from the dVCG, and some or all of these 21 features were used in support vector machine (SVM) learning and in tests. The classification accuracy was 99.53%, which is similar to the previous dVCG analysis using the standard 12-lead ECG. Our experimental results show that it is possible to identify a person by features extracted from a dVCG derived from limb leads only. Hence, only three electrodes have to be attached to the person to be identified, which can reduce the effort required to connect electrodes and calculate the dVCG.