Designing a Robust ECG Biometric Authentication System with Deep Learning

Mohamed Azab; Viktoriia Korzhuk

UDC 004 UDC 62

2 more Collapse

DESIGNING A ROBUST ECG BIOMETRIC AUTHENTICATION SYSTEM WITH DEEP LEARNING Designing a Robust ECG Biometric Authentication System with Deep Learning

АА Аа

Published in Journal of Technical Research · Volume 11, Issue 1 · Pages 20–32 · Rubric: Information technology and telecommunication

Received: 22.01.2025 Accepted: 24.01.2025 Published: 03.03.2025 Language of publication: RUS

Authors

Mohamed Azab

ORCID

Viktoriia Korzhuk

Mohamed Azab

Academic degree: graduate student of technical sciences

Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics Lecturer · Faculty of Information Technology Security (FBIT) · Saint-Petersburg

ORCID

Viktoriia Korzhuk

Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics Associate Professor · Faculty of Information Technology Security · Saint-Petersburg

ABSTRACT

Today, we are increasingly looking to biometric authentication systems to protect our crucial information and resources. Our contribution in this paper is a robust and novel biometric authentication system for ECG signals that are augmented using the deep learning techniques. A comprehensive methodology including signal processing, feature extraction, wavelet decomposition, QRS wave detection, internal modeling, distance and deviation calculations, and averaging threshold along with an artificial neural network (ANN) classifier are used in the proposed system. The quality of ECG signals is improved using preprocessing, and ECG signals are acquired. The ECG waveform features unique characteristics which are then extracted, and the signal is decomposed in both time and frequency domains using wavelet decomposition. Using QRS wave detection, critical components for biometric authentication are identified. The system constructs an internal representation of ECG waves, calculates parameters such as distance and deviation, and refines the feature set to improve robustness. An averaging threshold is applied to enhance resilience to noise and variability. Finally, an ANN classifier, trained on the extracted features, performs the authentication. The system outputs the authentication result and the verified identity of the individual. Extensive testing was conducted using a well-known ECG dataset, achieving an accuracy of 98%, demonstrating the system's effectiveness. The True Positive Rate (sensitivity) was 95%, indicating strong performance in identifying authentic individuals. With a processing time of 10 seconds the system would be appropriate for use in real-time applications. ROC curve analysis also demonstrated excellent performance in discriminating authentic from non-authentic individuals with an Area Under the Curve (AUC) of 0.98. Securing, dependable and adaptable ECG based biometric authentication system is provided with the integration of complicated signal processing and deep learning to deal with real ECG pattern variations, that improves on past work. Although the system is highly sensitive and accurate future work will be directed towards improving selectivity and decreasing false positives to improve performance as a whole.

KEYWORDS

Deep learning, ECG Signal, Artificial Neural Network (ANN), Security, Biometric Authentication, Signal Processing, QRS Complex, Wavelet Decomposition

Text

Text (PDF)

Read Download

1. Ingale M. et al. ECG Biometric Authentication: A Comparative Analysis // IEEE Access. ieee institute of electrical electronics engineers, 2020. Vol. 8. P. 117853–117866.

2. Ahmed M.J. et al. CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems // SLAS Technology. sage, 2024. Vol. 29, № 5. P. 100193.

3. Liu J. et al. A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks // Computers in biology and medicine. n tab tab pergamon n tab tab n tab tab elmsford ny usa n tab, 2022. Vol. 150. P. 106110.

4. Schwerdtfeger A.R. et al. Heart rate variability (HRV): From brain death to resonance breathing at 6 breaths per minute // Clinical Neurophysiology. elsevier ireland, 2019. Vol. 131, № 3. P. 676–693.

5. Sohn J. et al. Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network. // Sensors. molecular diversity preservation, 2020. Vol. 20, № 11. P. 3278.

6. Qu H., Pang L., Gao X. Classification of mental workload based on multiple features of ECG signals // Informatics in Medicine Unlocked. elsevier, 2021. Vol. 24. P. 100575.

7. Xiong P. et al. Short-term paroxysmal atrial fibrillation detection with intra- and inter-patient paradigm based on R-R intervals // Biomedical Signal Processing and Control. american geophysical union, 2023. Vol. 89. P. 105750.

8. Da Silva H.P. et al. Finger ECG signal for user authentication: Usability and performance. institute of electrical electronics engineers, 2013. Vol. 2. P. 1–8.

9. El Boujnouni I. et al. A wavelet-based capsule neural network for ECG biometric identification // Biomedical Signal Processing and Control. american geophysical union, 2022. Vol. 76. P. 103692.

10. Meltzer D., Luengo D. Efficient Clustering-Based electrocardiographic biometric identification // Expert Systems with Applications. elsevier, 2023. Vol. 219. P. 119609. DOI: https://doi.org/10.1016/j.eswa.2023.119609; EDN: https://elibrary.ru/WMBGTB

11. Wagner P. et al. Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery // Computers in Biology and Medicine. n tab tab pergamon n tab tab n tab tab elmsford ny usa n tab, 2024. Vol. 176. P. 108525.

12. M S., Gupta N. A Survey on Biometric Authentication Using Electrocardiogram // SSRN Electronic Journal. elsevier, 2020.

13. Yang W., Wang S. A Privacy-Preserving ECG-Based Authentication System for Securing Wireless Body Sensor Networks // IEEE Internet of Things Journal. institute of electrical electronics engineers, 2022. Vol. 9, № 8. P. 6148–6158.

14. Khan M.U. et al. Biometric Authentication System Based on Electrocardiogram (ECG). institute of electrical electronics engineers, 2019. P. 1–6.

15. Issa M.F. et al. Heartbeat classification based on single lead-II ECG using deep learning // Heliyon. elsevier bv, 2023. Vol. 9, № 7. P. e17974.

16. Van Dam P.M. et al. The relation of 12 lead ECG to the cardiac anatomy: The normal CineECG // Journal of Electrocardiology. elsevier, 2021. Vol. 69. P. 67–74.

17. Uwaechia A.N., Ramli D.A. A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges // IEEE Access. ieee institute of electrical electronics engineers, 2021. Vol. 9. P. 97760–97802.

18. Wang D. et al. A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding // Electronics. mdpi ag, 2019. Vol. 8, № 6. P. 667.