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DOI: 10.14569/IJACSA.2019.0100237
PDF

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Author 1: Sugondo Hadiyoso
Author 2: Suci Aulia
Author 3: Achmad Rizal

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 2, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: In this research, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics. Ten adult people were participated as the subjects in this research taken from their signal ECG using the one-lead ECG machine. A total of 65 raw ECG waves from the 10 subjects were analyzed. This raw signal is then processed using the Hjorth Descriptor and Sample Entropy (SampEn) to get the signal features. Support Vector Machine (SVM) algorithm was used as the classifier for the subject authentication based upon the record of ECG signal. The results of the research showed that the highest accuracy value of 93.8% was found in Hjorth Descriptor. Compared to SampEn, this method is quite promising to be implemented for having a good performance and fewer features.

Keywords: ECG; biometric; Hjorth; sample entropy; SVM

Sugondo Hadiyoso, Suci Aulia and Achmad Rizal, “One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine” International Journal of Advanced Computer Science and Applications(IJACSA), 10(2), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100237

@article{Hadiyoso2019,
title = {One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100237},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100237},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {2},
author = {Sugondo Hadiyoso and Suci Aulia and Achmad Rizal}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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