한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제20권3호
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  • Pages.465-473
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  • 2017
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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곡률기반 기준점 검출을 이용한 계층적 심전도 신호 개인인증 알고리즘

Hierarchical Authentication Algorithm Using Curvature Based Fiducial Point Extraction of ECG Signals

  • Kim, Jungjoon (School of Electronics Engineering, Kyungpook National University) ;
  • Lee, SeungMin (School of Electronics Engineering, Kyungpook National University) ;
  • Ryu, Gang-Soo (Dept. of Information & Communications Eng., Gumi University) ;
  • Lee, Jong-Hak (Dept. of Information Technology Eng., Catholic University of Daegu) ;
  • Park, Kil-Houm (School of Electronics Engineering, Kyungpook National University)
  • 투고 : 2016.12.30
  • 심사 : 2017.02.06
  • 발행 : 2017.03.30
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초록

Electrocardiogram(ECG) signal is one of the unique bio-signals of individuals and is used for personal authentication. The existing studies on personal authentication method using ECG signals show a high detection rate for a small group of candidates, but a low detection rate and increased execution time for a large group of candidates. In this paper, we propose a hierarchical algorithm that extracts fiducial points based on curvature of ECG signals as feature values for grouping candidates ​and identifies candidates using waveform-based comparisons. As a result of experiments on 74 ECG signal records of QT-DB provided by Physionet, the detection rate was about 97% at 3-heartbeat input and about 99% at 5-heartbeat input. The average execution time was 22.4 milliseconds. In conclusion, the proposed method improves the detection rate by the hierarchical personal authentication process, and also shows reduced amount of computation which is plausible in real-time personal authentication usage in the future.

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피인용 문헌

  1. Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor vol.10, pp.10, 2018, https://doi.org/10.3390/sym10100487
  2. Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics vol.19, pp.4, 2019, https://doi.org/10.3390/s19040935