ArticleFrontiers in neuroscience2022
Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.Scientific data · 2026Article
- The research progress of wearable digital health technologies in epilepsy management.Acta epileptologica · 2026Review
- Synchronization-based graph spatio-temporal attention network for seizure prediction.Scientific reports · 2025Article
- Epileptic seizures diagnosis and prognosis from EEG signals using heterogeneous graph neural network.PeerJ. Computer science · 2025Article
- Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion.Journal of translational medicine · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG.Frontiers in neuroscience · 2023Article
- Memristive Neural Networks for Predicting Seizure Activity.Sovremennye tekhnologii v meditsine · 2023Article
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4 authors.
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Abstract
As one of the most common neurological disorders, epilepsy causes great physical and psychological damage to the patients. The long-term recurrent and unprovoked seizures make the prediction necessary. In this paper, a novel approach for epileptic seizure prediction based on successive variational mode decomposition (SVMD) and transformers is proposed. SVMD is extended to multidimensional form for time-frequency analysis of multi-channel signals. It could adaptively extract common band-limited intrinsic modes among all channels on different time scales by solving a variational optimization problem. In the proposed seizure prediction method, data are first decomposed into multiple modes on different time scales by multivariate SVMD, and then, irrelevant modes are removed for preprocessing. Finally, power spectrum of denoised data is input to a pre-trained bidirectional encoder representations from transformers (BERTs) for prediction. The BERT could identify the mode information related to epileptic seizures in time-frequency domain. It shows fair prediction performance on an intracranial EEG dataset with the average sensitivity of 0.86 and FPR of 0.18/h.
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