ArticleScientific reports2024
A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Knowledge-Guided Deep Learning with Clinical EEG Biomarkers for Automated Dementia Detection and Staging.Diagnostics (Basel, Switzerland) · 2026Article
- Quantum inspired feature engineering for explainable EEG signal classification.Scientific reports · 2026Article
- Pathway to Adaptive Neuromodulation: Modeling Epileptogenic Brain States Using SEEG.Epilepsy currents · 2026Article
- Predicting Epileptic Seizures From EEG Signals Using Machine Learning.Neurosciences (Riyadh, Saudi Arabia) · 2026Article
- Efficient and Accurate Epilepsy Seizure Prediction and Detection Based on Multi-Teacher Knowledge Distillation RGF-Model.Brain sciences · 2026Article
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.Visual computing for industry, biomedicine, and art · 2025Review
- [Predicting epileptic seizures based on a multi-convolution fusion network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal.Scientific reports · 2025Article
- EEG detection and recognition model for epilepsy based on dual attention mechanism.Scientific reports · 2025Article
- A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.BMC medical informatics and decision making · 2025Article
- Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.Frontiers in neuroscience · 2025Review
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
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Authors and funding
6 authors.
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Abstract
Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time-frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.
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