Seizure Prediction Analysis of Infantile Spasms
Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infanti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9954441/ |
| _version_ | 1827926997621276672 |
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| author | Runze Zheng Jiuwen Cao Yuanmeng Feng Xiaodan Zhao Tiejia Jiang Feng Gao |
| author_facet | Runze Zheng Jiuwen Cao Yuanmeng Feng Xiaodan Zhao Tiejia Jiang Feng Gao |
| author_sort | Runze Zheng |
| collection | DOAJ |
| description | Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model. The analysis is conducted on dividing the continuous scalp electroencephalograms (sEEG) into 5 phases: Interictal, Preictal, Seizure Prediction Horizon (SPH), Seizure, and Postictal. The brain network of Phase-Locking Value (PLV) of 5 typical brain rhythms is constructed, and the mechanism of epileptic changes is analyzed by statistical methods. It is found that 1) the connections between the prefrontal, occipital, and central regions show a large variability at each stage of seizure transition, and 2) 4 sub-bands of brain rhythms (<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>) are predominant. Group and individual variabilities are validated by using the Resnet18 deep model on data from 25 patients with infantile spasms, where the consistent results to statistical analyses can be observed. The optimized model achieves an average of <inline-formula> <tex-math notation="LaTeX">$79.78~\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$94.46\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$75.46\%$ </tex-math></inline-formula> accuracy, specificity, and recall rate, respectively. The method accomplishes the analysis of the synergy between infantile spasms mechanism, model, data and algorithm, providing a guideline to build an intelligent and systematic model for comprehensive IS seizure prediction. |
| first_indexed | 2024-03-13T05:46:29Z |
| format | Article |
| id | doaj.art-62107faa2fc94e7a99fdd9054010797c |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-62107faa2fc94e7a99fdd9054010797c2023-06-13T20:09:25ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-013136637610.1109/TNSRE.2022.32230569954441Seizure Prediction Analysis of Infantile SpasmsRunze Zheng0https://orcid.org/0000-0001-5604-4422Jiuwen Cao1https://orcid.org/0000-0002-6480-5794Yuanmeng Feng2https://orcid.org/0000-0001-8528-5428Xiaodan Zhao3Tiejia Jiang4Feng Gao5https://orcid.org/0000-0003-4907-7212Machine Learning and I-health International Cooperation Base of Zhejiang Province, Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, ChinaMachine Learning and I-health International Cooperation Base of Zhejiang Province, Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, ChinaMachine Learning and I-health International Cooperation Base of Zhejiang Province, Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, ChinaMachine Learning and I-health International Cooperation Base of Zhejiang Province, Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, ChinaDepartment of Neurology, National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaMachine Learning and I-health International Cooperation Base of Zhejiang Province, Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, ChinaInfantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model. The analysis is conducted on dividing the continuous scalp electroencephalograms (sEEG) into 5 phases: Interictal, Preictal, Seizure Prediction Horizon (SPH), Seizure, and Postictal. The brain network of Phase-Locking Value (PLV) of 5 typical brain rhythms is constructed, and the mechanism of epileptic changes is analyzed by statistical methods. It is found that 1) the connections between the prefrontal, occipital, and central regions show a large variability at each stage of seizure transition, and 2) 4 sub-bands of brain rhythms (<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>) are predominant. Group and individual variabilities are validated by using the Resnet18 deep model on data from 25 patients with infantile spasms, where the consistent results to statistical analyses can be observed. The optimized model achieves an average of <inline-formula> <tex-math notation="LaTeX">$79.78~\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$94.46\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$75.46\%$ </tex-math></inline-formula> accuracy, specificity, and recall rate, respectively. The method accomplishes the analysis of the synergy between infantile spasms mechanism, model, data and algorithm, providing a guideline to build an intelligent and systematic model for comprehensive IS seizure prediction.https://ieeexplore.ieee.org/document/9954441/Infantile spasmsscalp electroencephalogrambrain network analysisseizure prediction |
| spellingShingle | Runze Zheng Jiuwen Cao Yuanmeng Feng Xiaodan Zhao Tiejia Jiang Feng Gao Seizure Prediction Analysis of Infantile Spasms IEEE Transactions on Neural Systems and Rehabilitation Engineering Infantile spasms scalp electroencephalogram brain network analysis seizure prediction |
| title | Seizure Prediction Analysis of Infantile Spasms |
| title_full | Seizure Prediction Analysis of Infantile Spasms |
| title_fullStr | Seizure Prediction Analysis of Infantile Spasms |
| title_full_unstemmed | Seizure Prediction Analysis of Infantile Spasms |
| title_short | Seizure Prediction Analysis of Infantile Spasms |
| title_sort | seizure prediction analysis of infantile spasms |
| topic | Infantile spasms scalp electroencephalogram brain network analysis seizure prediction |
| url | https://ieeexplore.ieee.org/document/9954441/ |
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