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ECG-Based Biometric Recognition: A Survey of Methods and Databases.

David Meltzer1, David Luengo2

  • 1Department of Telematics & Electronics, Universidad Politécnica de Madrid, Calle Nikola Tesla s/n, 28031 Madrid, Spain.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

This survey offers a comprehensive overview of Electrocardiogram (ECG) biometric recognition systems, detailing key features, techniques, and available databases for researchers.

Keywords:
ECG biometricsECG databasesidentificationsurveyverification

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Area of Science:

  • Biometrics
  • Signal Processing
  • Computer Science

Background:

  • Electrocardiogram (ECG) signals offer unique physiological characteristics for biometric identification.
  • Existing literature on ECG-based biometrics lacks a consolidated overview of studies and data sources.
  • Advancements in signal processing and machine learning enable sophisticated biometric recognition using ECG data.

Purpose of the Study:

  • To provide a chronologically ordered survey of studies on ECG-based biometric recognition systems.
  • To systematically review and compile the main ECG features and recognition techniques used in the literature.
  • To identify, characterize, and compile available ECG databases for biometric research.

Main Methods:

  • Conducted a comprehensive literature review of ECG biometric recognition studies.
  • Categorized and analyzed ECG features and recognition methodologies.
  • Surveyed and documented characteristics of publicly available ECG databases.

Main Results:

  • Identified and detailed a wide range of ECG features and recognition techniques.
  • Compiled a comprehensive list of relevant research papers and their methodologies.
  • Provided a detailed overview of ECG databases, including their key characteristics and usage in studies.

Conclusions:

  • This work presents the most complete overview to date of studies and data sources for ECG-based biometrics.
  • Researchers can use this survey to understand the state-of-the-art and identify relevant literature and databases.
  • Facilitates the testing and validation of novel biometric algorithms using established ECG datasets.