Imagen de Portada

CTFN: Multistage CNN‐Transformer Fusion Network for ECG Authentication

Guardado en:
Publicado en:IET Biometrics vol. 2025, no. 1 (2025)
Autor principal: Jia, Heng
Otros Autores: Zhao, Zhidong, Zhang, Yefei, Zhang, Xianfei, Deng, Yanjun, Wang, Yongguang, Wang, Hao, Jiao, Pengfei
Publicado:
John Wiley & Sons, Inc.
Materias:
CNN
Physiology
Feature extraction
Face recognition
Emotional factors
Accuracy
Electrocardiography
Datasets
Data acquisition
Wavelet transforms
Spoofing
Fingerprinting
Biometrics
Pattern recognition
Artificial neural networks
Signal processing
EKG
Performance evaluation
Heart rate
Emotions
Data processing
Experiments
Neural networks
Databases
Biomarkers
Algorithms
Robustness (mathematics)
Authentication
Cybersecurity
Correlation analysis
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:In the face of the mounting challenges posed by cybersecurity threats, there is an imperative for the development of robust identity authentication systems to safeguard sensitive user data. Conventional biometric authentication methods, such as fingerprinting and facial recognition, are vulnerable to spoofing attacks. In contrast, electrocardiogram (ECG) signals offer distinct advantages as dynamic, “liveness”‐assured biomarkers, exhibiting individual specificity. This study proposes a novel fusion network model, the convolutional neural network (CNN)‐transformer fusion network (CTFN), to achieve high‐precision ECG‐based identity authentication by synergizing local feature extraction and global signal correlation analysis. The proposed framework integrates a multistage enhanced CNN to capture fine‐grained local patterns in ECG morphology and a transformer encoder to model long‐range dependencies in heartbeat sequences. An adaptive weighting mechanism dynamically optimizes the contributions of both modules during feature fusion. The efficacy of CTFN was evaluated in three critical real‐world scenarios: single/multi‐heartbeat authentication, cross‐temporal consistency, and emotional variability resistance. The evaluation was conducted on 283 subjects from four public ECG databases: CYBHi, PTB, ECG‐ID, and MIT‐BIH. The CYBHi dataset revealed that CTFN exhibited a state‐of‐the‐art recognition accuracy of 98.46%, 80.95%, and 90.76%, respectively, signifying its remarkable performance. Notably, the model attained a 100% authentication accuracy rate using only six heartbeats. This represents a 25% decrease in input requirements when compared to prior works, while concurrently maintaining its robust performance against physiological variations induced by emotional states or temporal gaps. These results demonstrate that CTFN significantly advances the practicality of ECG biometrics by balancing high accuracy with minimal data acquisition demands, offering a scalable and spoof‐resistant solution for secure authentication systems.
ISSN:2047-4938
2047-4946
DOI:10.1049/bme2/8757767
Fuente:Advanced Technologies & Aerospace Database