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SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG Recordings
- SeizureTransformer is a deep learning-based model for simultaneous time-step level seizure detection from long EEG recordings.
- The model consists of a deep encoder with 1D convolutions, a residual CNN stack, and a transformer encoder.
- SeizureTransformer effectively handles long-range patterns in EEG data and outperforms existing approaches in seizure detection.
- The model ranked first in the 2025 "seizure detection challenge" and shows potential for real-time, precise seizure detection.
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