WAVELET2VEC: A Filter Bank Masked Autoencoder for EEG-Based Seizure Subtype Classification
Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches face two challenges in such applications: 1) convolutional or recurrent neural network based models have difficulty learning long-term dependenc...
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| Vydané v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5 |
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IEEE
04.06.2023
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| ISSN: | 2379-190X |
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| Abstract | Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches face two challenges in such applications: 1) convolutional or recurrent neural network based models have difficulty learning long-term dependencies; and, 2) there are not enough labeled seizure sub-type data for training such models. This paper proposes a Transformer-based self-supervised learning model for EEG-based seizure subtype classification, which copes well with these two challenges. Filter bank analysis is first employed to improve Vision Transformer as a Wavelet Transformer (WaT) encoder, which generates multi-grained feature representations of EEG signals. Then, self-supervised learning is used to pre-train WaT from unlabeled EEG data. Experiments on two public datasets demonstrated that Wavelet2Vec outperformed several other supervised and self-supervised models in cross-subject seizure subtype classification. |
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| AbstractList | Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches face two challenges in such applications: 1) convolutional or recurrent neural network based models have difficulty learning long-term dependencies; and, 2) there are not enough labeled seizure sub-type data for training such models. This paper proposes a Transformer-based self-supervised learning model for EEG-based seizure subtype classification, which copes well with these two challenges. Filter bank analysis is first employed to improve Vision Transformer as a Wavelet Transformer (WaT) encoder, which generates multi-grained feature representations of EEG signals. Then, self-supervised learning is used to pre-train WaT from unlabeled EEG data. Experiments on two public datasets demonstrated that Wavelet2Vec outperformed several other supervised and self-supervised models in cross-subject seizure subtype classification. |
| Author | Wu, Dongrui Zhao, Changming Xu, Yifan Kuang, Guangtao Shao, Jianbo Peng, Ruimin Jiang, Jun |
| Author_xml | – sequence: 1 givenname: Ruimin surname: Peng fullname: Peng, Ruimin organization: Huazhong University of Science and Technology,Wuhan,China – sequence: 2 givenname: Changming surname: Zhao fullname: Zhao, Changming organization: Huazhong University of Science and Technology,Wuhan,China – sequence: 3 givenname: Yifan surname: Xu fullname: Xu, Yifan organization: Huazhong University of Science and Technology,Wuhan,China – sequence: 4 givenname: Jun surname: Jiang fullname: Jiang, Jun organization: Children's Hospital,Wuhan,China – sequence: 5 givenname: Guangtao surname: Kuang fullname: Kuang, Guangtao organization: Children's Hospital,Wuhan,China – sequence: 6 givenname: Jianbo surname: Shao fullname: Shao, Jianbo organization: Children's Hospital,Wuhan,China – sequence: 7 givenname: Dongrui surname: Wu fullname: Wu, Dongrui organization: Huazhong University of Science and Technology,Wuhan,China |
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| PublicationTitle | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) |
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| Snippet | Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches... |
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| SubjectTerms | Brain modeling Deep learning EEG filter bank analysis Filter banks masked autoencoder seizure subtype classification Self-supervised learning Training data Transformers Wavelet transforms |
| Title | WAVELET2VEC: A Filter Bank Masked Autoencoder for EEG-Based Seizure Subtype Classification |
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