GT-STAFG: Graph Transformer with Spatiotemporal Attention Fusion Gate for Epileptic Seizure Detection in Imbalanced EEG Data
Open accessAbstract
Background: Electroencephalography (EEG) assists clinicians in diagnosing epileptic seizures by recording brain electrical activity. Existing models process spatiotemporal features inefficiently either through cascaded spatiotemporal architectures or static functional connectivity, limiting their ability to capture deeper spatial–temporal correlations. Objectives: To address these limitations, we propose a Graph Transformer with Spatiotemporal Attention Fusion Gate (GT-STAFG). Methods: We analyzed 18-channel EEG data sampled at 200 Hz, transformed into the frequency domain, and segmented into 30- second windows. The graph transformer exploits dynamic graph data, while STAFG leverages self-attention and gating mechanisms to capture complex interactions by augmenting graph features with both spatial and temporal information. The clinical significance of extracted features was validated using the Integrated Gradients attribution method, emphasizing the clinical relevance of the proposed model. Results: GT-STAFG achieves the highest area under the precision–recall curve (AUPRC) scores of 0.605 on the TUSZ dataset and 0.498 on the CHB-MIT dataset, surpassing baseline models and demonstrating strong cross-patient generalization on imbalanced datasets. We applied transfer learning to leverage knowledge from the TUSZ dataset when analyzing the CHB-MIT dataset, yielding an average improvement of 8.3 percentage points in AUPRC. Conclusions: Our approach has the potential to enhance patient outcomes and optimize healthcare utilization.
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Hybrid spatiotemporal transformer models combine attention mechanisms with structural modeling capacity to capture both temporal evolution and relational dependencies.
“Hybrid spatiotemporal transformer models combine the strength of attention mechanisms with the structural modeling capacity to capture both temporal evolution and relational dependencies44,45.”
From K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction (2026) · cites this paper as [45]
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“Hybrid spatiotemporal transformer models combine the strength of attention mechanisms with the structural modeling capacity to capture both temporal evolution and relational dependencies 44,45 .”
- Authors
- Nafea, M. S., & Ismail, Z. H.
- Affiliations
- University of Technology Malaysia (MY)
- Corresponding author
- Ismail, Zool Hilmi
- Venue
- AI
- Publisher
- MDPI AG
- Published
- 2025-06-09
- Volume
- 6
- Issue
- 6
- Pages
- 120
- ISSN
- 2673-2688 (ISSN-L)
- OpenAlex
- W4411187431
- Cited by
- 8
- References
- 42
- Citations by year
- 2026: 6 · 2025: 2
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- AI (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- seizure detection, EEG, graph transformer, class imbalance, self-attention, gating mechanism, functional connectivity, transfer learning, CHB-MIT dataset, seizure prediction
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