ArticleIEEE transactions on bio-medical engineering2020
A New Neural Mass Model Driven Method and Its Application in Early Epileptic Seizure Detection.
Article in IEEE transactions on bio-medical engineering, 2020. 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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6 citing papers in PubMed.
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Journal of computational neuroscience · 2026Article
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Research square · 2026Article
- Neural Mass Modeling in the Cortical Motor Area and the Mechanism of Alpha Rhythm Changes.Sensors (Basel, Switzerland) · 2024Article
- Exploration of interictal to ictal transition in epileptic seizures using a neural mass model.Cognitive neurodynamics · 2024Article
- Improved control effect of pathological oscillations by using delayed feedback stimulation in neural mass model with pedunculopontine nucleus.Brain and behavior · 2023Article
- Computational modeling allows unsupervised classification of epileptic brain states across species.Scientific reports · 2023Article
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5 authors.
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Abstract
objectiveDespite numerous neural computational models proposed to explain physiological and pathological mechanisms of brain activity, a large gap remains between theory and application of the models. Building on the successful application of data-driven methods in epileptic seizure detection, we aim to build a bridge between data and models in this paper.
methodsWe first propose a novel model-driven seizure detection method based on dynamic features in epileptic EEGs, where the rationale for dynamic features in epileptic EEGs can be clarified in theory by characterizing the variation of parameters of the model. Then we apply the proposed D&F-model-driven method to the problem of early epileptic seizure detection, where the evolution of model parameters selected and optimized by the proposed method is measured and used to detect the starting point of the seizure.
resultsNumerical results on two open EEG databases demonstrate that our proposed method does a good job of early epileptic seizure detection. The average detection sensitivity, false positive rate and early detection period attain 100%, 0.1/h, and 7.1 s respectively.
conclusionThis paper provides a strategy to characterize EEG signals using a NMM-related method and the model parameters optimized by real EEG may then serve as features in their own right for early seizure detection. SIGNIFICANCE: An useful attempt to early detect epileptic seizures by combining the neural mass model with data analysis.
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