ArticleFrontiers in computational neuroscience2023
Feature separation and adversarial training for the patient-independent detection of epileptic seizures.
Article in Frontiers in computational neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Neonatal Seizure Detection Based on Spatiotemporal Feature Decoupling and Domain-Adversarial Learning.Sensors (Basel, Switzerland) · 2026Article
- Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis.Neuroscience informatics · 2025Article
- SPA-IoT with MCSV-CNN: a novel IoT-enabled method for robust pre-ictal seizure prediction.BMC medical informatics and decision making · 2025Article
- The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network.Frontiers in neuroinformatics · 2025Article
- An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network.Frontiers in physiology · 2024Article
- Epileptic focus localization using transfer learning on multi-modal EEG.Frontiers in computational neuroscience · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
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Abstract
An epileptic seizure is the external manifestation of abnormal neuronal discharges, which seriously affecting physical health. The pathogenesis of epilepsy is complex, and the types of epileptic seizures are diverse, resulting in significant variation in epileptic seizure data between subjects. If we feed epilepsy data from multiple patients directly into the model for training, it will lead to underfitting of the model. To overcome this problem, we propose a robust epileptic seizure detection model that effectively learns from multiple patients while eliminating the negative impact of the data distribution shift between patients. The model adopts a multi-level temporal-spectral feature extraction network to achieve feature extraction, a feature separation network to separate features into category-related and patient-related components, and an invariant feature extraction network to extract essential feature information related to categories. The proposed model is evaluated on the TUH dataset using leave-one-out cross-validation and achieves an average accuracy of 85.7%. The experimental results show that the proposed model is superior to the related literature and provides a valuable reference for the clinical application of epilepsy detection.
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