Enhancing biometric identification using 12-lead ECG signals and graph convolutional networks

Enhancing biometric identification using 12-lead ECG signals and graph convolutional networks

IntroductionThe electrocardiogram (ECG) is a highly secure biometric modality due to its intrinsic physiological characteristics, making it resilient to forgery and external attacks. This study presents a novel real-time biometric authentication system integrating Graph Convolutional Networks (GCN)...

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Bibliographic Details
Main Authors: Maram Al Alfi, Pedro Peris-Lopez, Carmen Camara
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Digital Health
Subjects:
graph convolutional networks (GCN)
electrocardiogram (ECG)
mutual information (MI)
Identification
12 ECG leads
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2025.1547208/full
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