ArticleScientific reports2023
Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 22 citations in OpenAlex.
- The use of quantitative electroencephalography in evaluating epilepsy treatment.Acta epileptologica · 2026Review
- Incorporation bias in clinical prediction models for epilepsy.Epilepsia open · 2026Article
- Development and validation of a deep survival model to predict time to seizure from routine electroencephalography.Epilepsia · 2026Article
- Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure.Epilepsia open · 2026Article
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Predictive value of traditional laboratory parameters and inflammatory indices for same-day seizure recurrence in patients with epileptic seizures.Epilepsia open · 2025Article
- Epileptic seizures diagnosis and prognosis from EEG signals using heterogeneous graph neural network.PeerJ. Computer science · 2025Article
- Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.Brain communications · 2025Article
- EEG focal delta slowing in focal epilepsy - A didactic review.Clinical neurophysiology practice · 2025Review
- The Eastern Association of Electroencephalographers: A Canadian/USA success story.Clinical neurophysiology practice · 2025Review
- EEG correlates of seizure without awareness and depression in patients with epilepsy: A secondary analysis of a 2022 study on EEG correlates of quality of life.PCN reports : psychiatry and clinical neurosciences · 2024Article
- Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events.Nature communications · 2024Article
- The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.Scientific reports · 2024Article
- Prediction of the Need for Anticonvulsants in the Management of Orofacial Neuropathic Pain Using Machine Learning.Cureus · 2024Article
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
- Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events.medRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.