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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.13820 |
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| _version_ | 1866910377066889216 |
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| author | Zhang, Pengju Sun, Chenxi Zhang, Jianwei Guo, Hong |
| author_facet | Zhang, Pengju Sun, Chenxi Zhang, Jianwei Guo, Hong |
| contents | We have developed an individual identification system based on magnetocardiography (MCG) signals captured using optically pumped magnetometers (OPMs). Our system utilizes pattern recognition to analyze the signals obtained at different positions on the body, by scanning the matrices composed of MCG signals with a 2*2 window. In order to make use of the spatial information of MCG signals, we transform the signals from adjacent small areas into four channels of a dataset. We further transform the data into time-frequency matrices using wavelet transforms and employ a convolutional neural network (CNN) for classification. As a result, our system achieves an accuracy rate of 97.04% in identifying individuals. This finding indicates that the MCG signal holds potential for use in individual identification systems, offering a valuable tool for personalized healthcare management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13820 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identity information based on human magnetocardiography signals Zhang, Pengju Sun, Chenxi Zhang, Jianwei Guo, Hong Machine Learning Cryptography and Security Signal Processing We have developed an individual identification system based on magnetocardiography (MCG) signals captured using optically pumped magnetometers (OPMs). Our system utilizes pattern recognition to analyze the signals obtained at different positions on the body, by scanning the matrices composed of MCG signals with a 2*2 window. In order to make use of the spatial information of MCG signals, we transform the signals from adjacent small areas into four channels of a dataset. We further transform the data into time-frequency matrices using wavelet transforms and employ a convolutional neural network (CNN) for classification. As a result, our system achieves an accuracy rate of 97.04% in identifying individuals. This finding indicates that the MCG signal holds potential for use in individual identification systems, offering a valuable tool for personalized healthcare management. |
| title | Identity information based on human magnetocardiography signals |
| topic | Machine Learning Cryptography and Security Signal Processing |
| url | https://arxiv.org/abs/2403.13820 |