Multi-perspective characterization of seizure prediction based on microstate analysis
Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality and quality of life. This study aims to integrate cognitive dynamic attributes of the brain into seizure prediction, evaluating the effectiveness of various char...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1474782/full |
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| author | Wei Shi Wei Shi Yina Cao Fangni Chen Fangni Chen Wei Tong Wei Tong Lei Zhang Lei Zhang Jian Wan Jian Wan |
| author_facet | Wei Shi Wei Shi Yina Cao Fangni Chen Fangni Chen Wei Tong Wei Tong Lei Zhang Lei Zhang Jian Wan Jian Wan |
| author_sort | Wei Shi |
| collection | DOAJ |
| description | Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality and quality of life. This study aims to integrate cognitive dynamic attributes of the brain into seizure prediction, evaluating the effectiveness of various characterization perspectives for seizure prediction, while delving into the impact of varying fragment lengths on the performance of each characterization. We adopted microstate analysis to extract the dynamic properties of cognitive states, calculated the EEG-based and microstate-based features to characterize nonlinear attributes, and assessed the power values across different frequency bands to represent the spectral information of the EEG. Based on the aforementioned characteristics, the predictor achieved a sensitivity of 93.82% on the private FH-ZJU seizure dataset and 93.22% on the Siena Scalp EEG dataset. The study outperforms state-of-the-art works in terms of sensitivity metrics in seizure prediction, indicating that it is crucial to incorporate cognitive dynamic attributes of the brain in seizure prediction. |
| format | Article |
| id | doaj-art-71dce32ff3dd4d4cba1c81dabd2f37d7 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-71dce32ff3dd4d4cba1c81dabd2f37d72024-11-19T06:15:24ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-11-011810.3389/fnins.2024.14747821474782Multi-perspective characterization of seizure prediction based on microstate analysisWei Shi0Wei Shi1Yina Cao2Fangni Chen3Fangni Chen4Wei Tong5Wei Tong6Lei Zhang7Lei Zhang8Jian Wan9Jian Wan10College of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaZhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, ChinaDepartment of Neurology, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaCollege of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaZhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, ChinaCollege of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaZhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, ChinaCollege of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaZhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, ChinaCollege of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaZhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, ChinaEpilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality and quality of life. This study aims to integrate cognitive dynamic attributes of the brain into seizure prediction, evaluating the effectiveness of various characterization perspectives for seizure prediction, while delving into the impact of varying fragment lengths on the performance of each characterization. We adopted microstate analysis to extract the dynamic properties of cognitive states, calculated the EEG-based and microstate-based features to characterize nonlinear attributes, and assessed the power values across different frequency bands to represent the spectral information of the EEG. Based on the aforementioned characteristics, the predictor achieved a sensitivity of 93.82% on the private FH-ZJU seizure dataset and 93.22% on the Siena Scalp EEG dataset. The study outperforms state-of-the-art works in terms of sensitivity metrics in seizure prediction, indicating that it is crucial to incorporate cognitive dynamic attributes of the brain in seizure prediction.https://www.frontiersin.org/articles/10.3389/fnins.2024.1474782/fullelectroencephalogramseizure predictionfrequencymicrostatenonlinear |
| spellingShingle | Wei Shi Wei Shi Yina Cao Fangni Chen Fangni Chen Wei Tong Wei Tong Lei Zhang Lei Zhang Jian Wan Jian Wan Multi-perspective characterization of seizure prediction based on microstate analysis Frontiers in Neuroscience electroencephalogram seizure prediction frequency microstate nonlinear |
| title | Multi-perspective characterization of seizure prediction based on microstate analysis |
| title_full | Multi-perspective characterization of seizure prediction based on microstate analysis |
| title_fullStr | Multi-perspective characterization of seizure prediction based on microstate analysis |
| title_full_unstemmed | Multi-perspective characterization of seizure prediction based on microstate analysis |
| title_short | Multi-perspective characterization of seizure prediction based on microstate analysis |
| title_sort | multi perspective characterization of seizure prediction based on microstate analysis |
| topic | electroencephalogram seizure prediction frequency microstate nonlinear |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1474782/full |
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