ResNet-Attention model for human authentication using ECG signals
Article
Hammad, Mohamed, Pławiak, Paweł, Wang, Kuanquan and Acharya, Udyavara Rajendra. 2021. "ResNet-Attention model for human authentication using ECG signals." Expert Systems: the journal of knowledge engineering. 38 (6). https://doi.org/10.1111/exsy.12547
| Article Title | ResNet-Attention model for human authentication using ECG signals |
|---|---|
| ERA Journal ID | 17851 |
| Article Category | Article |
| Authors | Hammad, Mohamed, Pławiak, Paweł, Wang, Kuanquan and Acharya, Udyavara Rajendra |
| Journal Title | Expert Systems: the journal of knowledge engineering |
| Journal Citation | 38 (6) |
| Article Number | e12547 |
| Number of Pages | 17 |
| Year | 2021 |
| Publisher | John Wiley & Sons |
| Place of Publication | United Kingdom |
| ISSN | 0266-4720 |
| 1468-0394 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1111/exsy.12547 |
| Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1111/exsy.12547 |
| Abstract | Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end-to-end deep neural network models for ECG-based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet-Attention is designed for human authentication. We have used 2-s duration ECG signals obtained from two ECG databases (Physikalisch-Technische Bundesanstalt [PTB] and Check Your Bio-signals Here initiative [CYBHi]) for authentication. Our proposed ResNet-Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. |
| Keywords | authentication; biometrics; convolutional neural network; DNN; ECG; end-to-end structure; ResNet |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Harbin Institute of Technology, China |
| Menoufia University, Egypt | |
| Cracow University of Technology, Poland | |
| Polish Academy of Sciences, Poland | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v99/resnet-attention-model-for-human-authentication-using-ecg-signals
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