A CNN-BiLSTM-GRU and attention-integrated hybrid network for epileptic seizure detection.

Overview

1. School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang, China

Abstract

Introduction: Epilepsy is a common neurological disease, and accurate seizure detection is essential for clinical monitoring and scientific treatment. This study aims to construct an effective intelligent detection model to achieve precise automatic identification of epileptic EEG signals and assist clinical medical decisions.

Methods: To capture subtle local waveform variations and suppress redundant noise interference in EEG signals, this study adopts one-dimensional convolutional neural network (1D-CNN) layers for adaptive local feature extraction and a lightweight global temporal soft attention mechanism for critical feature enhancement. A hybrid classification model based on bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) is proposed for the binary classification of epileptic EEG signals. The synthetic minority oversampling technique (SMOTE) is applied only to the training data within each cross-validation fold to alleviate the class imbalance problem of EEG datasets.

Results: The proposed hybrid model achieves a binary classification accuracy of 99.23%, while delivering an especially balanced sensitivity (99.29%) and specificity (99.34%), with a difference (∆ Sens–Spec) of only 0.05%, verified on the public UCI epileptic seizure recognition data set.

Discussion: The CNN-Bi-LSTM-GRU and attention-integrated hybrid network can effectively distinguish seizure and non-seizure EEG signals. And a nearly equal sensitivity and specificity suggests robust and unbiased classification. Which is critical for clinical deployment. The proposed method achieves competitive performance compared with most recent mainstream algorithms, which can offer a potential automated detection reference to assist clinical analysis of epilepsy EEG signals.

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

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Data

Datasets cited

• kaggle.com/datasets/harunshimanto, at Kaggle; found in the text, “EEG dataset”

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

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, 4 authors, 7 keywords, 46 references.

Cite

This paper

Wang, X., Gong, T., Li, X., & Lin, T. (2026). A CNN-BiLSTM-GRU and attention-integrated hybrid network for epileptic seizure detection. Frontiers in neuroscience, 20, 1909680. https://doi.org/10.3389/fnins.2026.1909680

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