ArticleCognitive neurodynamics2019
Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis.
Article in Cognitive neurodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Directed graph neural networks with partial directed coherence for seizure prediction and epileptogenic network characterization.iScience · 2026Article
- Combining temporal and spatial attention for seizure prediction.Health information science and systems · 2023Article
- Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure prediction models: a retrospective cohort study.The Lancet. Digital health · 2023Article
- Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.Scientific reports · 2023Article
- Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia.Frontiers in psychiatry · 2023Article
- Dynamic networks of P300-related process.Cognitive neurodynamics · 2022Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- An Explainable Statistical Method for Seizure Prediction Using Brain Functional Connectivity from EEG.Computational intelligence and neuroscience · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- [Research progress of epileptic seizure predictions based on electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network.BMC medical informatics and decision making · 2021Article
- Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN.BMC medical informatics and decision making · 2021Article
- Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals.Brain informatics · 2021Article
- Research on Differential Brain Networks before and after WM Training under Different Frequency Band Oscillations.Neural plasticity · 2021Article
- Prediction of Seizure Recurrence. A Note of Caution.Frontiers in neurology · 2021Article
- Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent.Frontiers in bioengineering and biotechnology · 2020Article
- Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective.Frontiers in physiology · 2020Article
- Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures.Frontiers in physiology · 2020Article
- Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG.Neural plasticity · 2020Article
- Deep Convolutional Neural Network Based Interictal-Preictal Electroencephalography Prediction: Application to Focal Cortical Dysplasia Type-II.Frontiers in neurology · 2020Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.
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