The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
B2-ViT Net: Broad Vision Transformer Network With Broad Attention for Seizure Prediction
Open accessAbstract
Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle these issues, in this study, an novel bi-level programming seizure prediction model, B2-ViT Net, is proposed for learning the new generalized spatio-temporal long-range correlation features, which can characterize the global interactions among channels in spatial, and long-range dependencies in temporal required for seizure prediction. In addition, the proposed model can comprehensively learn generalized seizure prediction features in a vast space due to its strong deep and broad feature search capabilities. Sufficient experiments are conducted on two public datasets, CHB-MIT and Kaggle datasets. Compared with other existing methods, our proposed model has shown promising results in automatic seizure prediction tasks, and provides a certain degree of interpretability.
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“Furthermore, multimodal systems have shown promise in delivering fairer and more equitable care, as they can incorporate broader patient contexts, reducing disparities that often arise when models are trained on limited or homogeneous data (10–12).”
From The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities (2026) · cites this paper as [12]
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Metadata
- Authors
- Shi, S., & Liu, W.
- Affiliations
- Kunming University of Science and Technology (CN)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2024
- Volume
- 32
- Pages
- 178-188
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W4390204163
- Cited by
- 124
- References
- 43
- Citations by year
- 2026: 77 · 2025: 39 · 2024: 8
- FWCI
- 18.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (12371460)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Clinical Neurology › Epilepsy research and treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- seizure prediction, epilepsy, vision transformer, long-range temporal dependencies, spatiotemporal feature learning, bi-level optimization, EEG, CHB-MIT, interpretability
- MeSH terms
- Algorithms, Electroencephalography, Humans, Electric Power Supplies, Seizures
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