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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/2408.00437 |
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| _version_ | 1866913455295954944 |
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| author | de Rooij, Seline J. S. Wesel, Frederiek Hunyadi, Borbála |
| author_facet | de Rooij, Seline J. S. Wesel, Frederiek Hunyadi, Borbála |
| contents | Recent developments in wearable devices have made accurate and efficient seizure detection more important than ever. A challenge in seizure detection is that patient-specific models typically outperform patient-independent models. However, in a wearable device one typically starts with a patient-independent model, until such patient-specific data is available. To avoid having to construct a new classifier with this data, as required in conventional kernel machines, we propose a transfer learning approach with a tensor kernel machine. This method learns the primal weights in a compressed form using the canonical polyadic decomposition, making it possible to efficiently update the weights of the patient-independent model with patient-specific data. The results show that this patient fine-tuned model reaches as high a performance as a patient-specific SVM model with a model size that is twice as small as the patient-specific model and ten times as small as the patient-independent model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00437 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine de Rooij, Seline J. S. Wesel, Frederiek Hunyadi, Borbála Signal Processing Machine Learning Recent developments in wearable devices have made accurate and efficient seizure detection more important than ever. A challenge in seizure detection is that patient-specific models typically outperform patient-independent models. However, in a wearable device one typically starts with a patient-independent model, until such patient-specific data is available. To avoid having to construct a new classifier with this data, as required in conventional kernel machines, we propose a transfer learning approach with a tensor kernel machine. This method learns the primal weights in a compressed form using the canonical polyadic decomposition, making it possible to efficiently update the weights of the patient-independent model with patient-specific data. The results show that this patient fine-tuned model reaches as high a performance as a patient-specific SVM model with a model size that is twice as small as the patient-specific model and ten times as small as the patient-independent model. |
| title | Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2408.00437 |