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<p>Electrocardiogram (ECG) signals exhibit unique electrical heart activity patterns that vary across individuals, offering significant potential as a biometric modality for human identification. This study introduces a novel non-fiducial framework for ECG-based biometric identification, integrating unsupervised features learning and deep learning methodologies. The proposed approach employs a 1D-Local Difference Pattern (1D-LDP) to extract discriminative features from raw ECG signals, capturing unique characteristics of individual heartbeat dynamics. Subsequently, a hybrid Stacked Autoencoder?Deep Belief Network (SAE-DBN) architecture is implemented to refine and optimize feature representation while enhancing the performance of the classification task, and effectively addressing challenges related to ECG signal variability and noise. Comparative analysis with existing Local Binary Pattern (LBP) variants and machine learning classifiers (e.g., SVM, KNN) confirms the robustness of the proposed approach in handling signal non-stationarity and artifacts. Experimental validation on two public databases, MIT-BIH Normal Sinus Rhythm and ECG-ID, showcases the framework’s advantages over traditional methods, achieving identification accuracies of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{96.00\%}\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{94.80\%}\)</EquationSource> </InlineEquation>, respectively, along with a low equal error rate (EER) of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{3.05\%}\)</EquationSource> </InlineEquation>. The results underscore its suitability for real-time applications, computational efficiency, and robust performance for real world physiological conditions.</p>

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Enhanced local patterns using deep learning techniques for ECG based identity recognition system

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Lotfi Mostefai,
  • Mohamed Benouis,
  • Mouloud Denai,
  • Merzoug Bouhamdi

摘要

Electrocardiogram (ECG) signals exhibit unique electrical heart activity patterns that vary across individuals, offering significant potential as a biometric modality for human identification. This study introduces a novel non-fiducial framework for ECG-based biometric identification, integrating unsupervised features learning and deep learning methodologies. The proposed approach employs a 1D-Local Difference Pattern (1D-LDP) to extract discriminative features from raw ECG signals, capturing unique characteristics of individual heartbeat dynamics. Subsequently, a hybrid Stacked Autoencoder?Deep Belief Network (SAE-DBN) architecture is implemented to refine and optimize feature representation while enhancing the performance of the classification task, and effectively addressing challenges related to ECG signal variability and noise. Comparative analysis with existing Local Binary Pattern (LBP) variants and machine learning classifiers (e.g., SVM, KNN) confirms the robustness of the proposed approach in handling signal non-stationarity and artifacts. Experimental validation on two public databases, MIT-BIH Normal Sinus Rhythm and ECG-ID, showcases the framework’s advantages over traditional methods, achieving identification accuracies of \(\varvec{96.00\%}\) and \(\varvec{94.80\%}\) , respectively, along with a low equal error rate (EER) of \(\varvec{3.05\%}\) . The results underscore its suitability for real-time applications, computational efficiency, and robust performance for real world physiological conditions.