ArticleFrontiers in molecular biosciences2023
Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN).
Article in Frontiers in molecular biosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis.PloS one · 2026Article
- Artificial intelligence for automated detection of interictal epileptiform discharges: methodological progress, clinical evaluation, and neurodevelopmental context.Frontiers in cell and developmental biology · 2026Review
- A Novel Co-Designed Multi-Domain Entropy and Its Dynamic Synapse Classification Approach for EEG Seizure Detection.Entropy (Basel, Switzerland) · 2025Article
- Development and validation of a multimodal automatic interictal epileptiform discharge detection model: a prospective multi-center study.BMC medicine · 2025Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network.Scientific reports · 2025Article
- A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features.Frontiers in neuroscience · 2025Article
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11 authors.
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Abstract
Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection.
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