TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification
Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper p...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/9878131/ |
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| author | Ruimin Peng Changming Zhao Jun Jiang Guangtao Kuang Yuqi Cui Yifan Xu Hao Du Jianbo Shao Dongrui Wu |
| author_facet | Ruimin Peng Changming Zhao Jun Jiang Guangtao Kuang Yuqi Cui Yifan Xu Hao Du Jianbo Shao Dongrui Wu |
| author_sort | Ruimin Peng |
| collection | DOAJ |
| description | Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized. |
| format | Article |
| id | doaj-art-2c92b4dca96b4f42b0d87acc9df9f77c |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-2c92b4dca96b4f42b0d87acc9df9f77c2025-08-20T03:05:39ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102022-01-01302567257610.1109/TNSRE.2022.32045409878131TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype ClassificationRuimin Peng0https://orcid.org/0000-0002-4869-1328Changming Zhao1Jun Jiang2Guangtao Kuang3Yuqi Cui4Yifan Xu5https://orcid.org/0000-0003-1591-0384Hao Du6Jianbo Shao7Dongrui Wu8https://orcid.org/0000-0002-7153-9703Key Laboratory of theMinistry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaKey Laboratory of theMinistry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaChildren’s Hospital, Wuhan, ChinaChildren’s Hospital, Wuhan, ChinaKey Laboratory of theMinistry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaKey Laboratory of theMinistry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaChildren’s Hospital, Wuhan, ChinaChildren’s Hospital, Wuhan, ChinaKey Laboratory of theMinistry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaElectroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.https://ieeexplore.ieee.org/document/9878131/EEGseizure subtype classificationEEGNetdeep learningpositional encoding |
| spellingShingle | Ruimin Peng Changming Zhao Jun Jiang Guangtao Kuang Yuqi Cui Yifan Xu Hao Du Jianbo Shao Dongrui Wu TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification IEEE Transactions on Neural Systems and Rehabilitation Engineering EEG seizure subtype classification EEGNet deep learning positional encoding |
| title | TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification |
| title_full | TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification |
| title_fullStr | TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification |
| title_full_unstemmed | TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification |
| title_short | TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification |
| title_sort | tie eegnet temporal information enhanced eegnet for seizure subtype classification |
| topic | EEG seizure subtype classification EEGNet deep learning positional encoding |
| url | https://ieeexplore.ieee.org/document/9878131/ |
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