ReviewInternational journal of environmental research and public health2021
Epileptic Seizures Detection Using Deep Learning Techniques: A Review.
Review in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.Brain informatics · 2026Article
- CosFNet: A Lightweight Epileptic EEG Detection Model Based on Cosine Convolution and FNet.Bioengineering (Basel, Switzerland) · 2026Article
- Towards Interpretable Seizure Detection: An Excitation/Inhibition Dynamic Polynomial Network Framework for Electroencephalography.Sensors (Basel, Switzerland) · 2026Article
- Near-Real-Time Epileptic Seizure Detection with Reduced EEG Electrodes: A BiLSTM-Wavelet Approach on the EPILEPSIAE Dataset.Brain sciences · 2026Article
- Neurosense: Bridging Neural Dynamics and Mental Health Through Deep Learning for Brain Health Assessment via Reaction Time and p-Factor Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- Recent Advances in the Management of Seizures in Children.Paediatric drugs · 2026Review
- Physics-informed graph neural networks for robust cross-patient epileptic seizure prediction via chimera state detection.PloS one · 2026Article
- TFFBN-HDLF: a hybrid deep learning framework based on time-frequency functional brain networks for epileptic seizure detection.Frontiers in medicine · 2026Article
- Localizing the epileptogenic zone in drug-resistant epilepsy based on ictal SEEG high-resolution time-frequency representation.Frontiers in neuroscience · 2026Article
- A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings.Sensors (Basel, Switzerland) · 2025Article
- Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.Visual computing for industry, biomedicine, and art · 2025Review
- Epileptic Seizure Detection Using Hyperdimensional Computing and Binary Naive Bayes Classifier.Bioengineering (Basel, Switzerland) · 2025Article
- Seizure Type Classification Based on Hybrid Feature Engineering and Mutual Information Analysis Using Electroencephalogram.Entropy (Basel, Switzerland) · 2025Article
- Multiscale predictive modeling robustly improves the accuracy of pseudo-prospective seizure forecasting in drug-resistant epilepsy.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.Epilepsia open · 2025Review
- Detecting Diverse Seizure Types with Wrist-Worn Wearable Devices: A Comparison of Machine Learning Approaches.Sensors (Basel, Switzerland) · 2025Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.