Bio-metric authentication with electrocardiogram (ECG) by considering variable signals

Hoon Ko; Kwangcheol Rim; Jong Youl Hong; Hoon Ko; Kwangcheol Rim; Jong Youl Hong

doi:10.3934/mbe.2023078

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 1716-1729. doi: 10.3934/mbe.2023078

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Bio-metric authentication with electrocardiogram (ECG) by considering variable signals

1.
Research & Development Center, MetaiONE Inc., Business Incubation Center (#504), Chosun University, Gwangju 61452, Korea
2.
Instituto Superior de Engenharia do Porto (ISEP/IPP), Porto 4249-015, Portugal
3.
The Department of Mathematics, Chosun University, 309 pilmundae-ro, Gwangju 61452, Korea
4.
College of Culture & Sports, Korea University, Korea University Sejong Campus, Sejong City 30019, Korea

† The authors contributed equally to this work as the first author.
Academic Editor: Weining Wu

Received: 05 September 2022 Revised: 17 October 2022 Accepted: 26 October 2022 Published: 04 November 2022

The use of conventional bio-signals such as an electrocardiogram (ECG) for biometric authentication is vulnerable to a lack of verification of continuity of signals; this is because the system does not consider the change in signals caused by a change in the situation of a person, that is, conventional biological signals. Prediction technology based on tracking and analyzing new signals can overcome this shortcoming. However, since the biological signal data sets are massive, their utilization is crucial for higher accuracy. In this study, we defined a 10 $ \times $ 10 matrix for 100 points based on the R-peak point and an array for the dimension of the signals. Furthermore, we defined the future predicted signals by analyzing the continuous points in each array of the matrices at the same point. As a result, the accuracy of user authentication was 91%.
- biometric authentication,
- electrocardiogram,
- variable signal,
- smart devices,
- wearable devices
Citation: Hoon Ko, Kwangcheol Rim, Jong Youl Hong. Bio-metric authentication with electrocardiogram (ECG) by considering variable signals[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 1716-1729. doi: 10.3934/mbe.2023078

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Abstract

The use of conventional bio-signals such as an electrocardiogram (ECG) for biometric authentication is vulnerable to a lack of verification of continuity of signals; this is because the system does not consider the change in signals caused by a change in the situation of a person, that is, conventional biological signals. Prediction technology based on tracking and analyzing new signals can overcome this shortcoming. However, since the biological signal data sets are massive, their utilization is crucial for higher accuracy. In this study, we defined a 10 $ \times $ 10 matrix for 100 points based on the R-peak point and an array for the dimension of the signals. Furthermore, we defined the future predicted signals by analyzing the continuous points in each array of the matrices at the same point. As a result, the accuracy of user authentication was 91%.

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