ArticleCognitive neurodynamics2019
Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Analysis of Epilepsy Treatment Strategies Based on an Astrocyte-Neuron-Coupled Network Model.Brain sciences · 2025Article
- Frequency-band specific directed connectivity networks reveal functional disruptions and pathogenic patterns in temporal lobe epilepsy: a MEG study.Scientific reports · 2025Article
- Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study.Sensors (Basel, Switzerland) · 2025Article
- Source Causal Connectivity Noninvasively Predicting Surgical Outcomes of Drug-Refractory Epilepsy.CNS neuroscience & therapeutics · 2025Article
- Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction.Cognitive neurodynamics · 2024Article
- Closed-loop seizure modulation via extreme learning machine based extended state observer.Cognitive neurodynamics · 2023Article
- Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation.Cognitive neurodynamics · 2023Article
- Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals.Cognitive neurodynamics · 2023Article
- Fusion Graph Representation of EEG for Emotion Recognition.Sensors (Basel, Switzerland) · 2023Article
- [Dynamic analysis of epileptic causal brain networks based on directional transfer function].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis.NeuroImage · 2022Article
- Deep-layer motif method for estimating information flow between EEG signals.Cognitive neurodynamics · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information.Cognitive neurodynamics · 2021Article
- Sharp decrease in the Laplacian matrix rank of phase-space graphs: a potential biomarker in epilepsy.Cognitive neurodynamics · 2021Article
- Complex networks and deep learning for EEG signal analysis.Cognitive neurodynamics · 2021Review
- Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method.Cognitive neurodynamics · 2021Article
- A novel quality prediction model for component based software system using ACO-NM optimized extreme learning machine.Cognitive neurodynamics · 2020Article
- Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia.Cognitive neurodynamics · 2019Article
- Functional and effective connectivity based features of EEG signals for object recognition.Cognitive neurodynamics · 2019Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.
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