ArticleJournal of neural engineering2020
Seizure tracking of epileptic EEGs using a model-driven approach.
Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Prognosis prediction of patients with disorders of consciousness based on digital twin brain models.Journal of neuroengineering and rehabilitation · 2026Article
- Computational modeling allows unsupervised classification of epileptic brain states across species.Scientific reports · 2023Article
- Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings.Medical & biological engineering & computing · 2021Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveAs a chronic neurological disorder, epilepsy is characterized by recurrent and unprovoked epileptic seizures that can disrupt the normal neuro-biologic, cognitive, psychological conditions of patients. Therefore, it is worthwhile to give a detailed account of how the epileptic EEG evolves during a period of seizure so that an effective control can be guided for epileptic patients in clinics. APPROACH: Considering the successful application of the neural mass model (NMM) in exploring the insights into brain activities for epilepsy, in this paper, we aim to construct a model-driven approach to track the development of seizures using epileptic EEGs. We first propose a new time-delay Wendling model with sub-populations (TD-W-SP model) with respect to three aspects of improvements. Then we introduce a model-driven seizure tracking approach, where a model training method is designed based on extracted features from epileptic EEGs and a tracking index is defined as a function of the trained model parameters. MAIN
resultsNumerical results on eight patients on CHB-MIT database demonstrate that our proposed method performs well in simulating epileptic-like EEGs as well as tracking the evolution of three stages (that is, from pre-ictal to ictal and from ictal to post-ictal) during a period of epileptic seizure. SIGNIFICANCE: A useful attempt to track epileptic seizures by combining the NMM with the data analysis.
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Registered trials
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