Feature separation and adversarial training for the patient-independent detection of epileptic seizures
An epileptic seizure is the external manifestation of abnormal neuronal discharges, which seriously affecting physical health. The pathogenesis of epilepsy is complex, and the types of epileptic seizures are diverse, resulting in significant variation in epileptic seizure data between subjects. If w...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1195334/full |
| _version_ | 1797777397008826368 |
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| author | Yong Yang Yong Yang Yong Yang Feng Li Xiaolin Qin Han Wen Xiaoguang Lin Xiaoguang Lin Dong Huang |
| author_facet | Yong Yang Yong Yang Yong Yang Feng Li Xiaolin Qin Han Wen Xiaoguang Lin Xiaoguang Lin Dong Huang |
| author_sort | Yong Yang |
| collection | DOAJ |
| description | An epileptic seizure is the external manifestation of abnormal neuronal discharges, which seriously affecting physical health. The pathogenesis of epilepsy is complex, and the types of epileptic seizures are diverse, resulting in significant variation in epileptic seizure data between subjects. If we feed epilepsy data from multiple patients directly into the model for training, it will lead to underfitting of the model. To overcome this problem, we propose a robust epileptic seizure detection model that effectively learns from multiple patients while eliminating the negative impact of the data distribution shift between patients. The model adopts a multi-level temporal-spectral feature extraction network to achieve feature extraction, a feature separation network to separate features into category-related and patient-related components, and an invariant feature extraction network to extract essential feature information related to categories. The proposed model is evaluated on the TUH dataset using leave-one-out cross-validation and achieves an average accuracy of 85.7%. The experimental results show that the proposed model is superior to the related literature and provides a valuable reference for the clinical application of epilepsy detection. |
| first_indexed | 2024-03-12T23:03:26Z |
| format | Article |
| id | doaj.art-1dd13b0737434ee59251537ffdd74af5 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-03-12T23:03:26Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-1dd13b0737434ee59251537ffdd74af52023-07-19T06:13:56ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-07-011710.3389/fncom.2023.11953341195334Feature separation and adversarial training for the patient-independent detection of epileptic seizuresYong Yang0Yong Yang1Yong Yang2Feng Li3Xiaolin Qin4Han Wen5Xiaoguang Lin6Xiaoguang Lin7Dong Huang8Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, ChinaChongqing School, University of Chinese Academy of Sciences, Chongqing, ChinaDepartment of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaChengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, ChinaChengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, ChinaChongqing School, University of Chinese Academy of Sciences, Chongqing, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, ChinaAn epileptic seizure is the external manifestation of abnormal neuronal discharges, which seriously affecting physical health. The pathogenesis of epilepsy is complex, and the types of epileptic seizures are diverse, resulting in significant variation in epileptic seizure data between subjects. If we feed epilepsy data from multiple patients directly into the model for training, it will lead to underfitting of the model. To overcome this problem, we propose a robust epileptic seizure detection model that effectively learns from multiple patients while eliminating the negative impact of the data distribution shift between patients. The model adopts a multi-level temporal-spectral feature extraction network to achieve feature extraction, a feature separation network to separate features into category-related and patient-related components, and an invariant feature extraction network to extract essential feature information related to categories. The proposed model is evaluated on the TUH dataset using leave-one-out cross-validation and achieves an average accuracy of 85.7%. The experimental results show that the proposed model is superior to the related literature and provides a valuable reference for the clinical application of epilepsy detection.https://www.frontiersin.org/articles/10.3389/fncom.2023.1195334/fullepileptic seizure detectionEEGfeature separationadversarial trainingpatient-independent |
| spellingShingle | Yong Yang Yong Yang Yong Yang Feng Li Xiaolin Qin Han Wen Xiaoguang Lin Xiaoguang Lin Dong Huang Feature separation and adversarial training for the patient-independent detection of epileptic seizures Frontiers in Computational Neuroscience epileptic seizure detection EEG feature separation adversarial training patient-independent |
| title | Feature separation and adversarial training for the patient-independent detection of epileptic seizures |
| title_full | Feature separation and adversarial training for the patient-independent detection of epileptic seizures |
| title_fullStr | Feature separation and adversarial training for the patient-independent detection of epileptic seizures |
| title_full_unstemmed | Feature separation and adversarial training for the patient-independent detection of epileptic seizures |
| title_short | Feature separation and adversarial training for the patient-independent detection of epileptic seizures |
| title_sort | feature separation and adversarial training for the patient independent detection of epileptic seizures |
| topic | epileptic seizure detection EEG feature separation adversarial training patient-independent |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2023.1195334/full |
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