ReviewCognitive neurodynamics2023
Epileptic seizure focus detection from interictal electroencephalogram: a survey.
Review in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- 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
- Machine learning detection of epileptic seizure onset zone from iEEG.Biomedical engineering letters · 2025Review
- Analysis of Epilepsy Treatment Strategies Based on an Astrocyte-Neuron-Coupled Network Model.Brain sciences · 2025Article
- Machine learning on interictal intracranial EEG predicts surgical outcome in drug resistant epilepsy.NPJ digital medicine · 2025Article
- EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network.Frontiers in computational neuroscience · 2025Article
- A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features.Frontiers in neuroscience · 2025Article
- Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction.Cognitive neurodynamics · 2024Article
- TATPat based explainable EEG model for neonatal seizure detection.Scientific reports · 2024Article
- A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.Journal of medical signals and sensors · 2024Review
- Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram.Cognitive neurodynamics · 2023Article
- Identification of TLE Focus from EEG Signals by Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Anticonvulsant role of nitric oxide synthase inhibitors in epilepsy (Review).Medicine internationalReview
- Review
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Authors and funding
5 authors.
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Abstract
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.
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