Abstract
Using electrocardiogram (ECG) as biometrics has been explored over the years because it fits well in health monitoring applications. Most of the ECG biometrics models are specialized and can only work with very specific conditions otherwise fine-tuning, retraining or redesigning are required. Generalized ECG biometrics models are more suitable for real world applications but require large and diverse datasets to train. In this study, we introduced three databases into training the generalized ECG biometrics transformer to enhance its generalization capability. The model scored 2.93, 0.86, 2.47 and 0.29% in equal error rate for authentication task and 97.15, 99.87, 97.46 and 100.00% in identification accuracies on AFDB, NSRDB, STDB and CEBSDB respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Biel L, Pettersson O, Philipson L, Wide P (2001) ECG analysis: a new approach in human identification. IEEE Trans Instrum Meas 50:808–812. https://doi.org/10.1109/19.930458
Turner D (2017) Digital authentication: the basics. https://www.cryptomathic.com/news-events/blog/digital-authentication-the-basics
Pal A, Singh YN (2018) ECG biometric recognition. In: International conference on mathematics and computing. Springer, Varanasi, pp 61–73. https://doi.org/10.1007/978-981-13-0023-3_7
Agrafioti F, Hatzinakos D (2008) Fusion of ECG sources for human identification. In: Proceedings of the 2008 3rd international symposium on communications, control, and signal processing, ISCCSP 2008. IEEE, Saint Julian’s, Malta, pp 1542–1547. https://doi.org/10.1109/ISCCSP.2008.4537472
Arteaga-Falconi JS, Al Osman H, El Saddik A (2016) ECG authentication for mobile devices. IEEE Trans Instrum Meas 65:591–600. https://doi.org/10.1109/TIM.2015.2503863
Jaya Prakash A, Patro KK, Hammad M, Tadeusiewicz R, Pławiak P (2022) BAED: a aecured biometric authentication system using ECG signal based on deep learning techniques. Biocybern Biomed Eng 42:1081–1093. https://doi.org/10.1016/J.BBE.2022.08.004
Ullah S, Khan P, Ullah N, Saleem S, Higgins H, Sup Kwak K (2009) A review of wireless body area networks for medical applications. Int J Commun Netw Syst Sci 02:797–803. https://doi.org/10.4236/ijcns.2009.28093
Ekiz D, Can YS, Dardagan YC, Ersoy C (2020) Can a smartband be used for continuous implicit authentication in real life. IEEE Access 8:59402–59411. https://doi.org/10.1109/ACCESS.2020.2982852
Abd El-Rahiem B, Hammad M (2022) A multi-fusion IoT authentication system based on internal deep fusion of ECG signals. In: Studies in big data. Springer, Cham, pp 53–79. https://doi.org/10.1007/978-3-030-85428-7_4
Sun H, Guo Y, Chen B, Chen Y (2019) A practical cross-domain ECG biometric identification method. In: Proceedings of the 2019 IEEE global communications conference (GLOBECOM). IEEE, Waikoloa, HI, pp 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9014278
Zhang Q, Zhou D, Zeng X (2017) HeartID: a multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications. IEEE Access 5:11805–11816. https://doi.org/10.1109/ACCESS.2017.2707460
Ingale M, Cordeiro R, Thentu S, Park Y, Karimian N (2020) ECG biometric authentication: a comparative analysis. IEEE Access 8:117853–117866. https://doi.org/10.1109/ACCESS.2020.3004464
Li Y, Pang Y, Wang K, Li X (2020) Toward improving ECG biometric identification using cascaded convolutional neural networks. Neurocomputing 391:83–95. https://doi.org/10.1016/j.neucom.2020.01.019
Chee KJ, Ramli DA (2022) Electrocardiogram biometrics using transformer’s self-attention mechanism for sequence pair feature extractor and flexible enrollment scope identification. Sensors 22:3346. https://doi.org/10.3390/s22093446
Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems. Long Beach, CA
Acknowledgements
This paper is supported under the Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2020/ICT03/USM/02/1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chee, K.J., Ramli, D.A. (2024). Enhancing Generalized Electrocardiogram Biometrics Transformer. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_54
Download citation
DOI: https://doi.org/10.1007/978-981-99-9005-4_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9004-7
Online ISBN: 978-981-99-9005-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)