Synchronization-based graph spatio-temporal attention network for seizure prediction
Abstract Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88492-5 |
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| author | Jie Xiang Yanan Li Xubin Wu Yanqing Dong Xin Wen Yan Niu |
| author_facet | Jie Xiang Yanan Li Xubin Wu Yanqing Dong Xin Wen Yan Niu |
| author_sort | Jie Xiang |
| collection | DOAJ |
| description | Abstract Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is crucial for improving their lives. In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. Important time-varying information may be overlooked if only graph space features during seizures are considered. To address these issues, we propose a synchronization-based graph spatio-temporal attention network (SGSTAN). This model effectively leverages the intricate information embedded within EEG recordings through spatio-temporal correlations. Experimental results on public datasets demonstrate the efficacy of our approach. On the CHB-MIT dataset, our method achieves accuracy, specificity, and sensitivity scores of 98.2%, 98.07%, and 97.85%, respectively. In the case of challenging subjects that are difficult to classify, we achieved an outstanding average classification accuracy of 97.59%, surpassing the results of previous studies. |
| format | Article |
| id | doaj-art-c97a26535b824670a451fdbfbd4255cd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c97a26535b824670a451fdbfbd4255cd2025-02-09T12:30:40ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-025-88492-5Synchronization-based graph spatio-temporal attention network for seizure predictionJie Xiang0Yanan Li1Xubin Wu2Yanqing Dong3Xin Wen4Yan Niu5College of Computer Science and Technology (College of Big Data), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Big Data), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Big Data), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Big Data), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Big Data), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Big Data), Taiyuan University of TechnologyAbstract Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is crucial for improving their lives. In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. Important time-varying information may be overlooked if only graph space features during seizures are considered. To address these issues, we propose a synchronization-based graph spatio-temporal attention network (SGSTAN). This model effectively leverages the intricate information embedded within EEG recordings through spatio-temporal correlations. Experimental results on public datasets demonstrate the efficacy of our approach. On the CHB-MIT dataset, our method achieves accuracy, specificity, and sensitivity scores of 98.2%, 98.07%, and 97.85%, respectively. In the case of challenging subjects that are difficult to classify, we achieved an outstanding average classification accuracy of 97.59%, surpassing the results of previous studies.https://doi.org/10.1038/s41598-025-88492-5Seizure predictionGraph attention networkTransformerSpatio-temporal attention |
| spellingShingle | Jie Xiang Yanan Li Xubin Wu Yanqing Dong Xin Wen Yan Niu Synchronization-based graph spatio-temporal attention network for seizure prediction Scientific Reports Seizure prediction Graph attention network Transformer Spatio-temporal attention |
| title | Synchronization-based graph spatio-temporal attention network for seizure prediction |
| title_full | Synchronization-based graph spatio-temporal attention network for seizure prediction |
| title_fullStr | Synchronization-based graph spatio-temporal attention network for seizure prediction |
| title_full_unstemmed | Synchronization-based graph spatio-temporal attention network for seizure prediction |
| title_short | Synchronization-based graph spatio-temporal attention network for seizure prediction |
| title_sort | synchronization based graph spatio temporal attention network for seizure prediction |
| topic | Seizure prediction Graph attention network Transformer Spatio-temporal attention |
| url | https://doi.org/10.1038/s41598-025-88492-5 |
| work_keys_str_mv | AT jiexiang synchronizationbasedgraphspatiotemporalattentionnetworkforseizureprediction AT yananli synchronizationbasedgraphspatiotemporalattentionnetworkforseizureprediction AT xubinwu synchronizationbasedgraphspatiotemporalattentionnetworkforseizureprediction AT yanqingdong synchronizationbasedgraphspatiotemporalattentionnetworkforseizureprediction AT xinwen synchronizationbasedgraphspatiotemporalattentionnetworkforseizureprediction AT yanniu synchronizationbasedgraphspatiotemporalattentionnetworkforseizureprediction |