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<p>Using the Electrocardiogram (ECG) signals for biometric identification as a biometric trait has become popular today because of their unique characteristics. The primary objective of this research is to design, implement, and test an efficient and accurate ECG-based biometric identification system that leverages the benefits of Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) neural networks to improve classification performance and robustness. The proposed method comprises three main stages: preprocessing, feature extraction, and classification. We first start by denoising the signal using a 4th-order Butterworth bandpass filter and then normalizing the amplitude of the filtered signal between 0 and 1. Next, we decompose the normalized signal into multiple intrinsic mode functions (IMFs) using EMD. The first two IMFs are retained as features, and each one of them is segmented into 5-second windows with a 4-second overlap to create a new feature vector. A GRU-based neural network is then trained to identify individuals from their ECG signals. We evaluated the proposed method on three publicly available databases: PTB Diagnostic ECG, MIT-BIH Arrhythmia, and ECG-ID. These are among the most widely used datasets in the field of ECG-based biometric identification because of their diverse characteristics and good representation of various types of heart conditions. We achieved accuracy rates of 99.88%, 99.89%, and 96.34% respectively. which outperformed most existing ECG biometric identification methods. Furthermore, we compared our method with other approaches proposed in the literature. The results show that our proposed system is more accurate and robust than these methods. However, it’s important to note that our method relies on single-lead ECG data, which simplifies signal acquisition but may limit its performance in certain scenarios. Our method can be applied to access control systems, e-healthcare, and biometric identification. Future work could explore incorporating additional modalities to enhance accuracy and robustness.</p>

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EMD based biometric identification system from electrocardiogram signals using GRU neural networks

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Hatem Zehir,
  • Toufik Hafs,
  • Sara Daas,
  • Amine Nait-ali

摘要

Using the Electrocardiogram (ECG) signals for biometric identification as a biometric trait has become popular today because of their unique characteristics. The primary objective of this research is to design, implement, and test an efficient and accurate ECG-based biometric identification system that leverages the benefits of Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) neural networks to improve classification performance and robustness. The proposed method comprises three main stages: preprocessing, feature extraction, and classification. We first start by denoising the signal using a 4th-order Butterworth bandpass filter and then normalizing the amplitude of the filtered signal between 0 and 1. Next, we decompose the normalized signal into multiple intrinsic mode functions (IMFs) using EMD. The first two IMFs are retained as features, and each one of them is segmented into 5-second windows with a 4-second overlap to create a new feature vector. A GRU-based neural network is then trained to identify individuals from their ECG signals. We evaluated the proposed method on three publicly available databases: PTB Diagnostic ECG, MIT-BIH Arrhythmia, and ECG-ID. These are among the most widely used datasets in the field of ECG-based biometric identification because of their diverse characteristics and good representation of various types of heart conditions. We achieved accuracy rates of 99.88%, 99.89%, and 96.34% respectively. which outperformed most existing ECG biometric identification methods. Furthermore, we compared our method with other approaches proposed in the literature. The results show that our proposed system is more accurate and robust than these methods. However, it’s important to note that our method relies on single-lead ECG data, which simplifies signal acquisition but may limit its performance in certain scenarios. Our method can be applied to access control systems, e-healthcare, and biometric identification. Future work could explore incorporating additional modalities to enhance accuracy and robustness.