CTFN: Multistage CNN‐Transformer Fusion Network for ECG Authentication
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| Udgivet i: | IET Biometrics vol. 2025, no. 1 (2025) |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , , , |
| Udgivet: |
John Wiley & Sons, Inc.
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| Fag: | |
| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 3288469791 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2047-4938 | ||
| 022 | |a 2047-4946 | ||
| 024 | 7 | |a 10.1049/bme2/8757767 |2 doi | |
| 035 | |a 3288469791 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 186536 |2 nlm | ||
| 100 | 1 | |a Jia, Heng |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 245 | 1 | |a CTFN: Multistage CNN‐Transformer Fusion Network for ECG Authentication | |
| 260 | |b John Wiley & Sons, Inc. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a CNN | |
| 653 | |a Physiology | ||
| 653 | |a Feature extraction | ||
| 653 | |a Face recognition | ||
| 653 | |a Emotional factors | ||
| 653 | |a Accuracy | ||
| 653 | |a Electrocardiography | ||
| 653 | |a Datasets | ||
| 653 | |a Data acquisition | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Spoofing | ||
| 653 | |a Fingerprinting | ||
| 653 | |a Biometrics | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a EKG | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Heart rate | ||
| 653 | |a Emotions | ||
| 653 | |a Data processing | ||
| 653 | |a Experiments | ||
| 653 | |a Neural networks | ||
| 653 | |a Databases | ||
| 653 | |a Biomarkers | ||
| 653 | |a Algorithms | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Authentication | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Correlation analysis | ||
| 700 | 1 | |a Zhao, Zhidong |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 700 | 1 | |a Zhang, Yefei |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 700 | 1 | |a Zhang, Xianfei |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 700 | 1 | |a Deng, Yanjun |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 700 | 1 | |a Wang, Yongguang |u Department of Cardiology, , Rui’an People’s Hospital, , Wenzhou, , , China | |
| 700 | 1 | |a Wang, Hao |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 700 | 1 | |a Jiao, Pengfei |u School of Cyberspace, , Hangzhou Dianzi University, , Hangzhou, , , China, <url href="http://hdu.edu.cn">hdu.edu.cn</url> | |
| 773 | 0 | |t IET Biometrics |g vol. 2025, no. 1 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3288469791/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3288469791/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3288469791/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |