Maleki Lonbar Sajjad, Beigi Akram, Bagheri Nasour, Peris-Lopez Pedro, Camara Carmen
CPS2 Lab, Department of Communication, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran.
Department of AI, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran.
Front Digit Health. 2024 Dec 17;6:1463713. doi: 10.3389/fdgth.2024.1463713. eCollection 2024.
Identity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies-software, hardware, and biometric-have been developed. Among these, biometric modalities have gained significant attention due to their high accuracy and resistance to falsification. This paper focuses on utilizing electrocardiogram (ECG) signals for identity verification, capitalizing on their unique, individualized characteristics.
In this study, we propose a novel identity verification framework based on ECG signals. Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. The proposed framework involves two main steps: (1) signal cleansing to remove noise and (2) transforming the signals into the frequency domain for feature extraction. This is achieved by applying the Wigner-Ville distribution, which converts ECG signals into image data. Each image captures unique cardiac signal information of the individual, ensuring distinction in a noise-free environment. For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. The GoogleNet architecture is selected for its effectiveness in processing complex image data, and is used for both training and testing the system.
The identity verification model achieved impressive results across two benchmark datasets. For the NSRDB dataset, the model achieved an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8%. Similarly, for the MITDB dataset, the model demonstrated an accuracy of 99.004% and an EER of 0.8%. These results indicate that the proposed framework offers superior performance in comparison to alternative biometric authentication methods.
The outcomes of this study highlight the effectiveness of using ECG signals for identity verification, particularly in terms of accuracy and robustness against noise. The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. The results from the NSRDB and MITDB datasets reflect the high reliability of the model, with exceptionally low error rates. This approach could be extended to other biometric modalities or combined with additional layers of security to enhance its practical applications. Furthermore, future research could explore additional preprocessing techniques or alternative deep learning architectures to further improve the performance of ECG-based identity verification systems.
身份验证在现代社会中起着至关重要的作用，其应用范围涵盖从在线服务到安全系统。随着对强大的自动认证系统需求的增加，已开发出各种方法——软件、硬件和生物识别方法。其中，生物识别方式因其高精度和抗伪造能力而备受关注。本文重点利用心电图（ECG）信号进行身份验证，利用其独特的个性化特征。
在本研究中，我们提出了一种基于ECG信号的新型身份验证框架。使用了诸如NSRDB和MITDB等著名数据集来评估系统性能。然而，这些数据集包含固有噪声，因此需要进行预处理。所提出的框架包括两个主要步骤：（1）信号清理以去除噪声；（2）将信号转换到频域进行特征提取。这通过应用维格纳-威利分布来实现，该分布将ECG信号转换为图像数据。每张图像捕获个体独特的心脏信号信息，确保在无噪声环境中的区分度。为了进行识别，应用了深度学习技术，特别是卷积神经网络（CNN）。选择GoogleNet架构是因为其在处理复杂图像数据方面的有效性，并用于训练和测试系统。
身份验证模型在两个基准数据集上取得了令人印象深刻的结果。对于NSRDB数据集，模型的准确率达到99.3%，等错误率（EER）为0.8%。同样，对于MITDB数据集，模型的准确率为99.004%，EER为0.8%。这些结果表明，与其他生物识别认证方法相比，所提出的框架具有卓越的性能。
本研究的结果突出了使用ECG信号进行身份验证的有效性，特别是在准确性和抗噪声鲁棒性方面。所提出的框架利用维格纳-威利分布和GoogleNet架构，展示了深度学习技术在生物识别认证中的潜力。NSRDB和MITDB数据集的结果反映了模型的高可靠性，错误率极低。这种方法可以扩展到其他生物识别方式，或与额外的安全层相结合以增强其实际应用。此外，未来的研究可以探索额外的预处理技术或替代的深度学习架构，以进一步提高基于ECG的身份验证系统的性能。
