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Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns.

Wonki Lee1, Seulgee Kim2, Daeeun Kim3

  • 1School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Korea. wonkilee@yonsei.ac.kr.

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Summary
This summary is machine-generated.

This study introduces a novel electrocardiogram (ECG) pattern matching method for biometric identification. The technique achieves high accuracy, with finger measurements reaching 100% identification success.

Keywords:
electrocardiographyindividual biometric identificationmulti-cycle ECG waveformpattern matching

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Area of Science:

  • Biometrics
  • Cardiology
  • Signal Processing

Background:

  • Electrocardiogram (ECG) waveforms reflect unique individual heart electrical properties.
  • ECG patterns offer potential for biometric recognition applications.

Purpose of the Study:

  • To present a new method for individual biometric identification using the entire ECG waveform pattern.
  • To evaluate the effectiveness of different pattern matching algorithms for ECG-based biometrics.

Main Methods:

  • Multi-cycle ECG signals were acquired using a 3-wire ECG circuit with electrodes on wrists or fingers.
  • Four pattern matching algorithms (cosine similarity, cross correlation, city block, Euclidean distance) were tested.
  • k-fold cross-validation was employed to assess generalization on independent datasets.

Main Results:

  • Wrist measurements achieved up to 93.3% accuracy with three heartbeats for 55 subjects.
  • Finger measurements demonstrated superior performance, reaching 99.3% with two heartbeats and 100% with three heartbeats for 20 subjects.

Conclusions:

  • The proposed ECG waveform pattern matching method shows high potential for reliable individual biometric identification.
  • ECG biometrics, particularly using finger measurements, offers a promising non-invasive identification approach.