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Personal Identification Based on Vectorcardiogram Derived from Limb Leads Electrocardiogram

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  • Jongshill Lee
  • Youngjoon Chee
  • Inyoung Kim

Abstract

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.

Suggested Citation

  • Jongshill Lee & Youngjoon Chee & Inyoung Kim, 2012. "Personal Identification Based on Vectorcardiogram Derived from Limb Leads Electrocardiogram," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:904905
    DOI: 10.1155/2012/904905
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    1. Vladimir N. Vapnik, 1995. "The Nature of Statistical Learning Theory," Springer Books, Springer, number 978-1-4757-2440-0, March.
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    Cited by:

    1. E. Castillo & D. P. Morales & A. García & F. Martínez-Martí & L. Parrilla & A. J. Palma, 2013. "Noise Suppression in ECG Signals through Efficient One‐Step Wavelet Processing Techniques," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    2. Pengcheng Han & Junping Du & Ming Fang, 2013. "Spatial Object Tracking Using an Enhanced Mean Shift Method Based on Perceptual Spatial‐Space Generation Model," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).

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