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Electrocardiogram signals-based user authentication systems using soft computing techniques

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Abstract

With the advent of various security attacks, biometric authentication methods are gaining momentum in the security literature. Electrocardiogram or ECG signals are one of the essential biometric features generated by the human heart’s electrical activities. Many authentication schemes apply these signals due to their uniqueness, resistance to fabrication attacks, and support for continuous authentication. This survey article focuses on the ECG-based authentication approaches and provides the required background knowledge about the ECG signals and authentication methods. Then, it presents a taxonomy of the ECG-based authentication approaches first based on the authentication factors and then according to the applied algorithms for conducting authentication. It then describes their key contributions, applied algorithms, and possible drawbacks. Furthermore, their employed evaluation factors, ECG datasets, and simulators are illuminated and compared. Finally, the concluding remarks and future studies directions in this context are provided.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mehdi Hosseinzadeh

  2. Health Management and Economics Research Centre, Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh

  3. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Bay Vo

  4. Computer Engineering Department, Afagh Higher Education Institute, Urmia, Iran

    Sajjad Naghipour

  5. Department of Computer Science, College of Science, University of Halabja, Halabja, Iraq

    Marwan Yassin Ghafour

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  1. Mehdi Hosseinzadeh
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  2. Bay Vo
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  3. Marwan Yassin Ghafour
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  4. Sajjad Naghipour
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Correspondence to Bay Vo or Sajjad Naghipour.

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Hosseinzadeh, M., Vo, B., Ghafour, M.Y. et al. Electrocardiogram signals-based user authentication systems using soft computing techniques. Artif Intell Rev 54, 667–709 (2021). https://doi.org/10.1007/s10462-020-09863-0

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