ArticleFrontiers in computational neuroscience2024
Residual and bidirectional LSTM for epileptic seizure detection.
Article in Frontiers in computational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Seizure detection using hierarchical temporal trend integration and self-supervised learning.Scientific reports · 2026Article
- [A generalizable epilepsy detection network based on dual-attention mechanism].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.BioData mining · 2026Article
- Artificial intelligence-assisted detection of epileptic spasms using electroencephalographic-video analysis.Epilepsia · 2026Article
- A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.BMC medicine · 2026Article
- Operational Transformer: An investigation of epilepsy detection.Brain topography · 2026Article
- A time-frequency cross-attention network model for epileptic seizure detection.Scientific reports · 2026Article
- Hybrid Spike-Encoded Spiking Neural Networks for Real-Time EEG Seizure Detection: A Comparative Benchmark.Biomimetics (Basel, Switzerland) · 2026Article
- Leednet: a lightweight network for event detection in EEG signals.Journal on advances in signal processing · 2026Article
- CLSP-REQA: a real-time quality-aware closed-loop seizure prediction framework with Mamba-BiLSTM and confidence-gated intervention.Frontiers in neuroinformatics · 2026Article
- Deep Learning Method Based on Multivariate Variational Mode Decomposition for Classification of Epileptic Signals.Brain sciences · 2025Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.Current neurology and neuroscience reports · 2025Review
- Multi-scale convolutional transformer network for motor imagery brain-computer interface.Scientific reports · 2025Article
- MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).PloS one · 2025Article
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Review
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8 authors.
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Abstract
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.
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