ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2019
Multi-feature localization of epileptic foci from interictal, intracranial EEG.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Pooled it
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Inroads into epilepsy through high-frequency oscillations: Achievements and benchmark areas for improvement.Epilepsia · 2026Review
- Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.Scientific reports · 2026Article
- Predicting Surgical Outcome in Drug-Resistant Epilepsy by Combining Interictal Biomarkers within a Machine Learning Framework.Research square · 2026Article
- No association between histopathology and neurophysiology in surgical specimens from pediatric focal epilepsy patients.Epilepsia · 2025Article
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Human brain local field potential recordings during a battery of multilingual cognitive and eye-tracking tasks.Scientific data · 2025Article
- Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning.Scientific reports · 2025Article
- SEEG in 2025: progress and pending challenges in stereotaxy methods, biomarkers and radiofrequency thermocoagulation.Current opinion in neurology · 2025Review
- Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data.Frontiers in network physiology · 2025Article
- Leveraging interictal multimodal features and graph neural networks for automated planning of epilepsy surgery.Brain communications · 2025Article
- A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation.Sensors (Basel, Switzerland) · 2024Article
- The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.Brain : a journal of neurology · 2024Article
- Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy.Scientific reports · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Generalisability of epileptiform patterns across time and patients.Scientific reports · 2024Article
- High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker.Scientific reports · 2024Article
- Spatial and amplitude dynamics of neurostimulation: Insights from the acute intrahippocampal kainate seizure mouse model.Epilepsia open · 2024Article
- Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.Brain communications · 2024Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveWhen considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data.
methodsWe propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording.
resultsThe tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95.
conclusionSVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects. SIGNIFICANCE: In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.
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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.