Publication Date

1-1-2020

Document Type

Article

Department

Computer Engineering

Publication Title

IEEE Access

Volume

8

DOI

10.1109/ACCESS.2020.3004464

First Page

117853

Last Page

117866

Abstract

Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data. Passwords have provided access control and authentication, but have shown their inherent vulnerabilities. The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention. To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit. However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases. In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community. We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics. Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching. This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation. In addition, an average of 2.11% EER among 1,694 subjects is obtained.

Funding Sponsor

San José State University

Keywords

authentication, ECG biometric, ECG datasets, feature extraction, Kalman filter, off-the-person, on-the-person, segment

Comments

This is the Version of Record and can also be read online here.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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