Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.

Overview

1. College of Information Engineering, Henan University of Science and Technology, Luoyang, China
2. The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China

Abstract

Background: Accurately predicting seizures remains challenging. With advances in smart medical technology, EEG-based monitoring has become essential. This study aims to improve prediction accuracy using a hybrid framework that models multiscale EEG characteristics.

Methods: EEG signals are decomposed into multiple sub-bands using the Discrete Wavelet Transform, and representative time-frequency and nonlinear features are extracted. These features are fed into a channel-centric model integrating depthwise separable convolution, self-attention, and an enhanced bidirectional long short-term memory network (DC-SA-EBiLSTM). The architecture integrates depthwise separable convolution for local spatial feature extraction, multi-head self-attention for global inter-channel dependencies, and an enhanced BiLSTM for channel-wise sequence modeling. The proposed method was evaluated on the CHB-MIT dataset using a 10-fold cross-validation protocol. An event-level leave-one-seizure-event-out validation was also conducted to assess alarm-based prediction performance.

Results: The proposed approach achieved an average accuracy of 95.89%, sensitivity of 96.70%, specificity of 95.48%, and AUC of 99.02%. In the event-level validation, the model achieved an event sensitivity of 95.96%, an average false alarm rate of 0.316 FPR/h, and a mean early warning time of 30.52 min.

Conclusion: The DC-SA-EBiLSTM framework effectively captures local and global inter-channel dependencies and provides a feature-driven approach for patient-specific preictal state prediction, showing potential for EEG-based seizure prediction.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.13026/c2k01r, at the source; found in “Data availability statement”
• physionet.org/content/chbmit, at PhysioNet; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: The dataset analyzed in this study is the publicly available CHB-MIT Scalp EEG Database hosted on PhysioNet: https://physionet.org/content/chbmit/1.0.0/; DOI: 10.13026/C2K01R (https://doi.org/10.13026/C2K01R).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 54 references.

Cite

This paper

Wang, S., Zhang, J., Chen, W., Xiang, F., Jiang, H., & Du, G. (2026). Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism. Frontiers in neuroscience, 20, 1856135. https://doi.org/10.3389/fnins.2026.1856135

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